WO2026036563A1 - Procédé, appareil et système de traitement de données - Google Patents

Procédé, appareil et système de traitement de données

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Publication number
WO2026036563A1
WO2026036563A1 PCT/CN2024/134379 CN2024134379W WO2026036563A1 WO 2026036563 A1 WO2026036563 A1 WO 2026036563A1 CN 2024134379 W CN2024134379 W CN 2024134379W WO 2026036563 A1 WO2026036563 A1 WO 2026036563A1
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WO
WIPO (PCT)
Prior art keywords
data
network function
plane
request
function
Prior art date
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Pending
Application number
PCT/CN2024/134379
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English (en)
Inventor
Chenchen YANG
Bidi YING
Hang Zhang
Xu Li
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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Filing date
Publication date
Application filed by Huawei Technologies Co Ltd filed Critical Huawei Technologies Co Ltd
Publication of WO2026036563A1 publication Critical patent/WO2026036563A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0806—Configuration setting for initial configuration or provisioning, e.g. plug-and-play
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0894—Policy-based network configuration management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Definitions

  • the present disclosure relates generally to the field of wireless communications technologies and, in particular, to method, apparatus, and system for data processing.
  • Future networks e.g., 5.5G network, 6G network
  • 5.5G network 5.5G network
  • 6G network 6G network
  • 5.5G network 5.5G network
  • sensing data e.g., sensing data
  • these data may be collected via different network planes. How to collect and manage these data should be considered for future networks.
  • This present disclosure provides method, apparatus, and system for data processing.
  • a method for data processing includes: obtaining a request from a second network function, where the request is for configuring a first network function to perform data processing on first data on a control or management (C/M) plane and second data on a data plane; and performing the data processing on the first data and the second data based on one or more parameters in the request.
  • C/M control or management
  • the first network function such as a processing service function (PSF) in a network.
  • the request for configuring the first network function to perform data processing on first data on the C/M plane and second data on the data plane may be obtained by the first network function from the second network device, such as a task control function (TCF) in the network.
  • TCF task control function
  • the first network function can know that it is requested to perform data processing, such as data fusion, on the first data on the C/M plane and the second data on the data plane, and can obtain one or more parameters from the request which may be required for performing the data processing.
  • the first network function can process different types of data.
  • different types of data including first data on the C/M plane and second data on the data plane can be collected and processed at the first network function, and thus, data sharing and data processing can be performed within the network on C/M plane and data plane for different types of data, e.g., network data from control plane and new types of data, such as sensing data and AI related data, from data plane.
  • the one or more parameters include at least one of following parameters: association information, a data plane tunnel identifier (ID) , a session ID, a fusion indication, data source information, or an address of the first network function.
  • the first network function can know the parameter (s) used for marking data packets. Different data packets marked with a same parameter or a same set of parameters are correlated, and may be, for example, aggregated together for data fusion. After receiving data packets, the first network function can clearly know, based on the one or more parameters of these data packets, whether they are correlated with each other such as whether one data packet should be aggregated with another data packet for data fusion. Meanwhile, by including the fusion indication, the first network function can know that the data fusion is required for different types of data such as first data on the C/M plane and second data on the data plane. Furthermore, by including the data source information, the first network function can know which source the different types of data is originated from. In this way, by including one or more parameters in the request, various application scenarios can be accommodated.
  • the association information is an association ID (AID) , a set of parameters, or an input of a calculation function.
  • the association information can be any one of the AID, the set of parameters, or the input of the calculation function
  • the data packets can be marked according to actual needs, and thus flexibility and reasonability of marking manners may be improved.
  • the AID can be used for the first network function to directly identify which data packets should be processed together under the scenarios with high time delay.
  • the set of parameters can be used for the first network function to indirectly identify which data packets should be processed together under the scenarios with time delay and privacy requirements.
  • the input of the calculation function can be used for the first network function to indirectly identify which data packets should be processed together under the scenarios with high privacy requirements.
  • the data plane tunnel ID is a general packet radio service (GPRS) tunnelling protocol for user plane (GTP-U) tunnel end ID (TEID) identifying a GTP-U tunnel, a quick user datagram protocol (UDP) internet connections (QUIC) connection ID identifying a QUIC connection, or a media over QUIC (MoQ) session ID identifying a MoQ session.
  • GPRS general packet radio service
  • GTP-U general packet radio service
  • TEID tunnel end ID
  • UDP quick user datagram protocol
  • QUIC internet connections
  • MoQ session ID is a uniform resource identifier (URI) .
  • the data plane tunnel ID can be different types of ID, the data plane tunnel can be identified according to actual needs, thereby various application scenarios can be accommodated.
  • the QUIC ID can adapt to the application scenarios requiring higher safety and/or higher speed.
  • the TEID can adapt to the application scenarios requiring higher flexibility.
  • the MoQ Session ID can adapt to the application scenarios requiring high scale and low latency data distribution.
  • IETF Internet Engineering Task Force
  • RRC Request for Comments
  • IETF Internet-Draft draft-ietf-moq-transport-05 for more details on MoQ transport.
  • the session ID is for identifying a connection, and the connection is based on one or more protocols of: a hypertext transfer protocol (HTTP) , a transmission control protocol (TCP) , a UDP, a QUIC, a MoQ, or a packet forwarding control protocol (PFCP) .
  • HTTP hypertext transfer protocol
  • TCP transmission control protocol
  • UDP User Datagram Protocol
  • QUIC transmission control protocol
  • MoQ MoQ
  • PFCP packet forwarding control protocol
  • the session ID can be used for identifying connections based on various protocols, flexibility and diversity of data delivery may be improved.
  • the first data is obtained from a third network function via a second network function through a first interface between the second network function and the third network function and a second interface between the first network function and the second network function.
  • a connection identified by the session ID is established on the second interface.
  • the second network function can participate in collecting the second data on the C/M plane from the data source, thereby facilitating the data management and distribution in a centralized way.
  • the first data is obtained from a third network function through a third interface between the first network function and the third network function.
  • a connection identified by the session ID is established on the third interface.
  • the first data can be obtained from the third network function through the third interface between the first network function and the third network function, it is not required for the second network function to participate in collecting the first data on the C/M plane from the data source, thereby reducing the processing overhead of the second network function.
  • the third network function is deployed on the C/M plane.
  • the first network function and the second network function are for providing a first service
  • the third network function is for providing a second service
  • the first service includes an integrated sensing and communication (ISAC) service.
  • IIC integrated sensing and communication
  • the first service is for one or more of types of data: sensing data, sensor data, network data, UE subscription data, data from third party, or artificial intelligence (AI) related data
  • the second service is for one or more of types of data: sensing data, sensor data, network data, UE subscription data, data from third party, or AI related data.
  • the third network function includes one or more of: a network data analytics function (NWDAF) , a data collection coordination function (DCCF) , a messaging framework adaptor function (MFAF) , a location management function (LMF) , an analytics data repository function (ADRF) , and a management data analytics function (MDAF) .
  • NWDAAF network data analytics function
  • DCCF data collection coordination function
  • MFAF messaging framework adaptor function
  • LMF location management function
  • ADRF analytics data repository function
  • MDAF management data analytics function
  • the first network function, the second network function, and the third network function can provide services for a variety of data consumers.
  • the third network function such as NWDAF, DCCF, and MFAF of control plane can be enhanced to support the service related to the first network function and the second network function such as the DAM service in an evolutionary solution.
  • performing the data processing on the first data and the second data includes: performing data fusion on the first data and the second data.
  • the request includes the association information, and when the first data is marked with same association information as the second data, the first data is correlated with the second data.
  • performing the data processing on the first data and the second data includes: performing data fusion on the first data marked with the association information and the second data marked with the same association information.
  • the request includes the association information
  • different data packets can be marked with the same association to indicate the correlation between them. For example, whether different data packets can be aggregated together for data fusion can be determined based on the association information of the different data packets.
  • the association information to mark the data packets, the first network function can easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network.
  • the request includes the association information and the data plane tunnel ID, and the first data marked with the association information is correlated with the second data delivered on a data plane tunnel identified by the data plane tunnel ID.
  • performing the data processing on the first data and the second data includes: performing data fusion on the first data marked with the association information and the second data delivered on the data plane tunnel identified by the data plane tunnel ID.
  • the association information and the data plane tunnel ID can be used together for identifying correlated data packets which can be processed together, for example, identifying different data packets that can be aggregated together for data fusion.
  • the first network function can easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets transferred via a data plane tunnel with association information, or encapsulate the association information in a data packet transferred via the data plane tunnel.
  • the request includes the association information and the session ID, and the first data delivered on a connection corresponding to the session ID is correlated with the second data marked with the association information.
  • performing the data processing on the first data and the second data includes: performing data fusion on the first data delivered on the connection corresponding to the session ID and the second data marked with the association information.
  • the association information and the session ID can be used together for identifying correlated data packets that can be processed together, for example, for identifying data packets that can be aggregated together for data fusion.
  • the first network function can easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets transferred via a C/M plane connection with association information, or encapsulate the association information in a data packet transferred via the C/M plane connection.
  • the request includes the session ID and the data plane tunnel ID, and the first data delivered on a connection corresponding to the session ID is correlated with the second data delivered on a data plane tunnel identified by the data plane tunnel ID.
  • performing the data processing on the first data and the second data includes: performing data fusion on the first data delivered on the connection corresponding to the session ID and the second data delivered on the data plane tunnel identified by the data plane tunnel ID.
  • the session ID and the data plane tunnel ID can be used together for identifying correlated data packets that can be processed together, for example, for identifying data packets that can be aggregated together for data fusion.
  • the first network function can easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets with the association information.
  • the data plane tunnel identified by the data plane tunnel ID is established on a fourth interface between the first network function and a fourth network function.
  • the data plane tunnel identified by the data plane tunnel ID can be established on the fourth interface between the first network function such as a PSF and the fourth network function such as a user plane function (UPF) .
  • the first network function can easily identify data packets on the data plane with the data plane tunnel ID of the data plane tunnel between the first network function and the fourth network function, thereby facilitating implementation of the data fusion on data plane.
  • the request includes a fusion indication
  • the fusion indication is for indicating that data fusion is to be performed on the first data and the second data.
  • the first network function can clearly know that the obtained first data on the C/M plane and second data on the data plane is required to be aggregated together, thereby facilitating implementation of the data fusion on data plane.
  • the request includes data source information
  • the data source information is for indicating one or more of: an area in which the second data is to be collected, or a time when the second data is to be collected.
  • the first network function can clearly know where the second data is to be collected or when the second data is to be collected, thereby the first network function can obtain the second data on the data plane correctly.
  • the second data is obtained from a fourth network function via a fourth interface.
  • the fourth network function is deployed on the data plane, and the fourth network function is a data trustworthy gateway (data-TW-GW) or a UPF.
  • data-TW-GW data trustworthy gateway
  • UPF UPF
  • the second data such as sensing data can be obtained from the fourth network function such as UPF on the data plane, thereby the first network function can obtain the second data on the data plane via the fourth network function directly.
  • the method further includes: obtaining the second data from a first data source; and obtaining the first data from a second data source.
  • the first network function is deployed on the data plane, and the second network function is deployed on the C/M plane.
  • the first network function is a data analysis and management (DAM) -PSF
  • the second network function is a DAM-TCF
  • the first network function deployed on the data plane can obtain the second data from the first data source and obtain the first data from the second data source so as to perform data fusion on the first data and the second data.
  • the second data on the data plane and the first data on the C/M plane can be shared and processed within network.
  • a method for data processing includes: sending a request to a first network function, where the request is for configuring the first network function to perform data processing on first data on a C/M plane and second data on a data plane; where the data processing is performed on the first data and the second data based on one or more parameters in the request.
  • This solution can be performed by a second network function such as a TCF in a network.
  • the second network function can send, to the first network function such as a PSF, the request for configuring the first network function to perform data processing on first data on the C/M plane and second data on the data plane.
  • the second network function can let the first network function know that it is requested to perform data processing, such as data fusion, on the first data on the C/M plane and the second data on the data plane, and obtain one or more parameters from the request which may be required for performing the data processing.
  • the first network function can process different types of data.
  • different types of data including first data on the C/M plane and second data on the data plane can be collected and processed at the first network function, and thus, data sharing and data processing can be performed within the network on C/M plane and data plane for different types of data, e.g., network data from control plane and new types of data, such as sensing data and AI related data, from data plane.
  • the one or more parameters include at least one of following parameters: association information, a data plane tunnel ID, a session ID, a fusion indication, data source information, or an address of the first network function.
  • the second network function can let the first network function know the parameter (s) used for marking data packets. Different data packets marked with a same parameter or a same set of parameters are correlated, and may be, for example, aggregated together for data fusion. After receiving data packets, the first network function can clearly know, based on the one or more parameters of these data packets, whether they are correlated with each other such as whether one data packet should be aggregated with another data packet for data fusion. Meanwhile, by including the fusion indication, the second network function can let the first network function know that the data fusion is required for different types of data such as first data on the C/M plane and second data on the data plane. Furthermore, by including the data source information, the second network function can let the first network function know which source the different types of data is originated from. In this way, by including one or more parameters in the request, various application scenarios can be accommodated.
  • the association information is an AID, a set of parameters, or an input of a calculation function.
  • the association information can be any one of the AID, the set of parameters, or the input of the calculation function
  • the data packets can be marked according to actual needs, and thus flexibility and reasonability of marking manners may be improved.
  • the AID can be used for the first network function to directly identify which data packets should be processed together under the scenarios with high time delay.
  • the set of parameters can be used for the first network function to indirectly identify which data packets should be processed together under the scenarios with time delay and privacy requirements.
  • the input of the calculation function can be used for the first network function to indirectly identify which data packets should be processed together under the scenarios with high privacy requirements.
  • the data plane tunnel ID is a GTP-U TEID identifying a GTP-U tunnel, a QUIC connection ID identifying a QUIC connection, or a MoQ session ID identifying a MoQ session.
  • the MoQ session ID is a URI.
  • the data plane tunnel ID can be different types of ID, the data plane tunnel can be identified according to actual needs, thereby various application scenarios can be accommodated.
  • the QUIC ID can adapt to the application scenarios requiring higher safety and/or higher speed.
  • the TEID can adapt to the application scenarios requiring higher flexibility.
  • the MoQ Session ID can adapt to the application scenarios requiring high scale and low latency data distribution.
  • the session ID is for identifying a connection
  • the connection is based on one or more protocols of: a HTTP, a TCP, a UDP, a QUIC, a MoQ, or a PFCP.
  • the session ID can be used for identifying connections based on various protocols, flexibility and diversity of data delivery may be improved.
  • the first data is obtained from a third network function via a second network function through a first interface between a second network function and the third network function and a second interface between the first network function and the second network function.
  • the second network function is a DAM-TCF.
  • a connection identified by the session ID is established on the second interface.
  • the second network function can participate in collecting the second data on the C/M plane from the data source, thereby facilitating the data management and distribution in a centralized way.
  • the first data is obtained from a third network function via a third interface between the first network function and the third network function.
  • a connection identified by the session ID is established on the third interface.
  • the first data can be obtained from the third network function through the third interface between the first network function and the third network function, it is not required for the second network function to participate in collecting the first data on the C/M plane from the data source, thereby reducing the processing overhead of the second network function.
  • the third network function is deployed on the C/M plane.
  • the first network function and the second network function are for providing a first service
  • the third network function is for providing a second service
  • the first service includes an ISAC service.
  • the first service is for one or more of types of data: sensing data, sensor data, network data, UE subscription data, data from third party, or AI related data
  • the second service is for one or more of types of data: sensing data, sensor data, network data, UE subscription data, data from third party, or AI related data.
  • the third network function includes one or more of: a NWDAF, a DCCF, a MFAF, a LMF, an ADRF, and a MDAF.
  • the first network function, the second network function, and the third network function can provide services for a variety of data consumers.
  • the third network function such as NWDAF, DCCF, and MFAF of control plane can be enhanced to support the service related to the first network function and the second network function such as the DAM service in an evolutionary solution.
  • the request includes the association information, and when the first data is marked with same association information as the second data, the first data is correlated with the second data.
  • the request includes the association information
  • different data packets can be marked with the same association to indicate the correlation between them. For example, whether different data packets can be aggregated together for data fusion can be determined based on the association information of the different data packets.
  • the second network function can let the first network function easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network.
  • the request includes the association information and the data plane tunnel ID, and the first data marked with the association information is correlated with the second data delivered on a data plane tunnel identified by the data plane tunnel ID.
  • the association information and the data plane tunnel ID can be used together for identifying correlated data packets which can be processed together, for example, identifying different data packets that can be aggregated together for data fusion.
  • the second network function can let the first network function easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets transferred via a data plane tunnel with association information, or encapsulate the association information in a data packet transferred via the data plane tunnel.
  • the request includes the association information and the session ID, and the first data delivered on a connection corresponding to the session ID is correlated with the second data marked with the association information.
  • the association information and the session ID can be used together for identifying correlated data packets that can be processed together, for example, for identifying data packets that can be aggregated together for data fusion.
  • the second network function can let the first network function easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets transferred via a C/M plane connection with association information, or encapsulate the association information in a data packet transferred via the C/M plane connection.
  • the request includes the session ID and the data plane tunnel ID, and the first data delivered on a connection corresponding to the session ID is correlated with the second data delivered on a data plane tunnel identified by the data plane tunnel ID.
  • the session ID and the data plane tunnel ID can be used together for identifying correlated data packets that can be processed together, for example, for identifying data packets that can be aggregated together for data fusion.
  • the second network function can let the first network function easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets with the association information.
  • the data plane tunnel identified by the data plane tunnel ID is established on a fourth interface between the first network function and a fourth network function.
  • the data plane tunnel identified by the data plane tunnel ID can be established on the fourth interface between the first network function such as a PSF and the fourth network function such as a UPF. In this way, data packets on the data plane with the data plane tunnel ID of the data plane tunnel between the first network function and the fourth network function can be easily identified by the first network function, thereby facilitating implementation of the data fusion on data plane.
  • the request includes a fusion indication
  • the fusion indication is for indicating that data fusion is to be performed on the first data and the second data.
  • the second network function can let the first network function clearly know that the obtained first data on the C/M plane and second data on the data plane is required to be aggregated together, thereby facilitating implementation of the data fusion on data plane.
  • the request includes data source information
  • the data source information is for indicating one or more of: an area in which the second data is to be collected, or a time when the second data is to be collected.
  • the second network function can let the first network function clearly know where the second data is to be collected or when the second data is to be collected, thereby the first network function can obtain the second data on the data plane correctly.
  • the second data is obtained from a fourth network function via a fourth interface.
  • the fourth network function is deployed on the data plane, and the fourth network function is a data-TW-GW or a UPF.
  • the second data such as sensing data can be obtained from the fourth network function such as the UPF on the data plane, thereby the first network function can obtain the second data on the data plane via the fourth network function directly.
  • the method further includes: obtaining the first data from a second data source; and sending the first data to the first network function.
  • the first network function is deployed on the data plane
  • the second network function is deployed on the C/M plane.
  • the first network function is a DAM-PSF.
  • the second network function deployed on the C/M plane can obtain the first data from the second data source and then send it to the first network function deployed on the data plane such that the first network function can perform data fusion on the first data and the second data.
  • the second data on the data plane and the first data on the C/M plane can be shared and processed within network.
  • a method for data processing includes: sending a request to a second network function, where the request is for configuring the second network function to process first data on a C/M plane; obtaining a response from the second network function, where the response is for acknowledging a receipt of the request; where the first data is processed based on one or more parameters in the request.
  • the first network function can send, to the second network function such as a NWDAF, the request for configuring the second network function to process first data on the C/M plane.
  • the first network function can let the second network function know that it is requested to process the first data, such as data collection, data source discovery, data analysis, and obtain one or more parameters from the request which may be required for processing the first data.
  • the second network function can share the data on the C/M plane with the first network function, so that different types of data at least including the first data on the C/M plane can be collected and processed at the first network function.
  • the one or more parameters include at least one of following parameters: association information, an indication on whether to collect data, an indication on whether to analyze data, data source information, an address of a first network function, or a session ID.
  • the first network function can let the second network function know the parameter (s) used for marking data packets. Different data packets marked with same association information can be sent to the first network function together. Meanwhile, by including the indication on whether to collect data and/or the indication on whether to analyze data, the first network function can let the second network function know what it should do for the first data. Furthermore, by including the data source information, the first network function can let the second network function know which source the first data is originated from. And by including the address of the first network function, the first network function can let the second network function know where the first data should be sent. Then by including the session ID, the first network function can let the second network function know which connection between the first network function and the second network function the data should be delivered to. In this way, by including one or more parameters in the request, various application scenarios can be accommodated.
  • the first network function is a DAM.
  • the method further includes: obtaining second data on a data plane from a first data source; obtaining the first data from a second data source; and performing data fusion on the first data and the second data.
  • the first network function such as the DAM can obtain the second data from the first data source and obtain the first data from the second data source so as to perform data fusion on the first data and the second data.
  • the second data on the data plane and the first data on the C/M plane can be shared and processed within network.
  • performing data fusion on the first data and the second data includes one or more of: performing data fusion on the first data marked with the association information and the second data marked with the same association information, performing data fusion on the first data marked with the association information and the second data delivered on a data plane tunnel identified by a data plane tunnel ID, performing data fusion on the first data delivered on a connection corresponding to the session ID and the second data marked with the association information, or performing data fusion on the first data delivered on a connection corresponding to the session ID and the second data delivered on a data plane tunnel identified by a data plane tunnel ID.
  • the first network function can easily and clearly know which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network.
  • configuring the second network function to process first data includes one or more of: configuring the second network function to collect the first data, configuring the second network function to discover a second data source, or configuring the second network function to analyze the first data.
  • the second network function includes one or more of: a NWDAF, a DCCF, a MFAF, a LMF, an ADRF, and a MDAF.
  • the first network function can send, to the second network function such as the NWDAF, the request for configuring the second network function to process first data on the C/M plane.
  • the first network function can let the second network function know that it is requested to process the first data, such as data collection, data source discovery, data analysis.
  • the second network function can share the data on the C/M plane with the first network function, so that different types of data at least including the first data on the C/M plane can be collected and processed at the first network function.
  • a method for data processing includes: obtaining a request from a first network function, where the request is for configuring a second network function to process first data on a C/M plane; sending a response to the first network function, where the response is for acknowledging a receipt of the request; and processing the first data based on one or more parameters in the request.
  • the second network function can obtain the request for configuring the second network function to process first data on the C/M plane from the first network function such as a DAM.
  • the second network function can know that it is requested to process the first data, such as data collection, data source discovery, data analysis, and can obtain one or more parameters from the request which may be required for processing the first data.
  • the second network function can share the data on the C/M plane with the first network function, so that different types of data at least including the first data on the C/M plane can be collected and processed at the first network function.
  • the one or more parameters include at least one of following parameters: association information, an indication on whether to collect data, an indication on whether to analyze data, data source information, an address of the first network function, or a session ID.
  • the second network function can know the parameter (s) used for marking data packets. Different data packets marked with same association information can be sent to the first network function together. Meanwhile, by including the indication on whether to collect data and/or the indication on whether to analyze data, the second network function can know what it should do for the first data. Furthermore, by including the data source information, the second network function can know which source the first data is originated from. And by including the address of the first network function, the second network function can know where the first data should be sent. Then by including the session ID, the second network function can know which connection between the first network function and the second network function the data should be delivered to. In this way, by including one or more parameters in the request, various application scenarios can be accommodated.
  • processing the first data includes one or more of: collecting the first data, discovering a second data source, or analyzing the first data.
  • the second network function can obtain the request for configuring the second network function to process first data on the C/M plane from the first network function. Through the request, the second network function can know that it is requested to process the first data, such as data collection, data source discovery, data analysis. Then, by processing the first data in such way, the second network function can share the data on the C/M plane with the first network function, so that different types of data at least including the first data on the C/M plane can be collected and processed at the first network function.
  • processing the first data includes collecting the first data, and the method further includes: obtaining the first data from a second data source; and sending the first data to the first network function.
  • the second network function can obtain the first data from the second data source and then send it to the first network function so that the first network function can perform data fusion on the first data and other data such as second data on the data plane.
  • the second data on the data plane and the first data on the C/M plane can be shared and processed within network.
  • sending the first data to the first network function includes one or more of: sending the first data, where the first data is marked with the association information, sending the first data via a connection corresponding to the session ID, sending an analytics result of the first data, where the analytics result is marked with the association information, or sending an analytics result of the first data via a connection corresponding to the session ID.
  • the first network function is a DAM
  • the second network function includes one or more of: a NWDAF, a DCCF, a MFAF, a LMF, an ADRF, and a MDAF.
  • the data packets can be marked by association information and the connection between the first network function and the second network function can be identified by the session ID. In this way, based on the association and the session ID, the second network function can send the first data and/or the analytics result of the first data in a clear way.
  • a method for data processing includes: sending, by a second network function, a request to a first network function, where the request is for configuring the first network function to perform data processing on first data on a C/M plane and second data on a data plane; obtaining, by the first network function, the request from the second network function; and performing, by the first network function, the data processing on the first data and the second data based on one or more parameters in the request.
  • This solution can be performed by a system including the first network function such as a PSF in a network and the second network function such as a TCF in the network.
  • the request for configuring the first network function to perform data processing on first data on the C/M plane and second data on the data plane may be obtained by the first network function from the second network function.
  • the second network function can let the first network function know that it is requested to perform data processing, such as data fusion, on the first data on the C/M plane and the second data on the data plane, and obtain one or more parameters from the request which may be required for performing the data processing.
  • the first network function can process different types of data.
  • different types of data including first data on the C/M plane and second data on the data plane can be collected and processed at the first network function, and thus, data sharing and data processing can be performed within the network on C/M plane and data plane for different types of data, e.g., network data from control plane and new types of data, such as sensing data and AI related data, from data plane.
  • a method for data processing includes: sending, by a first network function, a request to a second network function, where the request is for configuring the second network function to process first data on a C/M plane; obtaining, by the second network function, the request from the first network function; sending, by the second network function, a response to the first network function, where the response is for acknowledging a receipt of the request; obtaining, by the first network function, the response from the second network function; and processing, by the second network function, the first data based on one or more parameters in the request.
  • This solution can be performed by a system including the first network function such as a DAM in a network and the second network function such as a NWDAF in the network.
  • the first network function can send, to the second network function, the request for configuring the second network function to process first data on the C/M plane.
  • the first network function can let the second network function know that it is requested to process the first data, such as data collection, data source discovery, data analysis, and obtain one or more parameters from the request which may be required for processing the first data.
  • the second network function can share the data on the C/M plane with the first network function, so that different types of data at least including the first data on the C/M plane can be collected and processed at the first network function.
  • a communication apparatus has a function of implementing the first aspect or any possible implementation of the first aspect.
  • the communication apparatus includes a corresponding module, unit, or means for performing operations in the first aspect or any possible implementation of the first aspect.
  • the module, unit, or means may be specifically implemented by using software, may be implemented by using hardware, or may be implemented by using software in combination with hardware.
  • the communication apparatus has a function of implementing the second aspect or any possible implementation of the second aspect.
  • the communication apparatus includes a corresponding module, unit, or means for performing operations in the second aspect or any possible implementation of the second aspect.
  • the module, unit, or means may be specifically implemented by using software, may be implemented by using hardware, or may be implemented by using software in combination with hardware.
  • the communication apparatus has a function of implementing the third aspect or any possible implementation of the third aspect.
  • the communication apparatus includes a corresponding module, unit, or means for performing operations in the third aspect or any possible implementation of the third aspect.
  • the module, unit, or means may be specifically implemented by using software, may be implemented by using hardware, or may be implemented by using software in combination with hardware.
  • the communication apparatus has a function of implementing the fourth aspect or any possible implementation of the fourth aspect.
  • the communication apparatus includes a corresponding module, unit, or means for performing operations in the fourth aspect or any possible implementation of the fourth aspect.
  • the module, unit, or means may be specifically implemented by using software, may be implemented by using hardware, or may be implemented by using software in combination with hardware.
  • the communication apparatus includes a memory and one or more processors.
  • the memory is configured to store a part or all of a necessary computer program or instructions for implementing a function in the first aspect or any possible implementation of the first aspect.
  • the one or more processors may execute the computer program or the instructions, and when the computer program or the instructions is/are executed, the communication apparatus is enabled to implement the method in the first aspect or any possible implementation of the first aspect.
  • the communication apparatus includes a memory and one or more processors.
  • the memory is configured to store a part or all of a necessary computer program or instructions for implementing a function in the second aspect or any possible implementation of the second aspect.
  • the one or more processors may execute the computer program or the instructions, and when the computer program or the instructions is/are executed, the communication apparatus is enabled to implement the method in the second aspect or any possible implementation of the second aspect.
  • the communication apparatus includes a memory and one or more processors.
  • the memory is configured to store a part or all of a necessary computer program or instructions for implementing a function in the third aspect or any possible implementation of the third aspect.
  • the one or more processors may execute the computer program or the instructions, and when the computer program or the instructions is/are executed, the communication apparatus is enabled to implement the method in the third aspect or any possible implementation of the third aspect.
  • the communication apparatus includes a memory and one or more processors.
  • the memory is configured to store a part or all of a necessary computer program or instructions for implementing a function in the fourth aspect or any possible implementation of the fourth aspect.
  • the one or more processors may execute the computer program or the instructions, and when the computer program or the instructions is/are executed, the communication apparatus is enabled to implement the method in the fourth aspect or any possible implementation of the fourth aspect.
  • the communication apparatus may further include an interface circuit, and the processor is configured to communicate with another apparatus or component through the interface circuit.
  • the communication apparatus may further include the memory.
  • the communication system includes multiple communication apparatuses configured to perform the method according to any one of the possible designs of the above aspects or any possible implementation of the above aspects.
  • a computer-readable storage medium stores computer-readable instructions, and when a computer reads and executes the computer-readable instructions, the computer is enabled to perform the method according to any one of the possible designs of the above aspects or any possible implementation of the above aspects.
  • this disclosure provides a computer program product.
  • the computer When a computer reads and executes the computer program product, the computer is enabled to perform the method according to any one of the possible designs of the above aspects or any possible implementation of the above aspects.
  • This disclosure encompasses various implementations, including not only method implementations, but also other implementations such as apparatus implementations and implementations related to non-transitory computer readable storage media. Implementations may incorporate, individually or in combinations, the features disclosed herein.
  • the present disclosure provides method, apparatus, and system for data processing, where the request for configuring the first network function to perform data processing on first data on the C/M plane and second data on the data plane may be obtained by the first network function from the second network function.
  • the first network function can know that it is requested to perform data processing, such as data fusion, on the first data on the C/M plane and the second data on the data plane, and can obtain one or more parameters from the request which may be required for performing the data processing. Then, by performing the data processing on the first data and the second data based on one or more parameters in the request, the first network function can process different types of data.
  • different types of data including first data on the C/M plane and second data on the data plane can be collected and processed at the first network function, and thus, data sharing and data processing can be performed within the network on C/M plane and data plane for different types of data, e.g., network data from control plane and new types of data, such as sensing data and AI related data, from data plane.
  • FIG. 1 shows a simplified schematic illustration of 5G NFs for data collection and management on control plane according to one or more example implementations of the present disclosure.
  • FIG. 2 shows a simplified schematic illustration of 6G system conceptual structure according to one or more example implementations of the present disclosure.
  • FIG. 3 shows a simplified schematic illustration of deployment of 6G system according to one or more example implementations of the present disclosure.
  • FIG. 4 shows a simplified schematic illustration of an apparatus in a communication system according to one or more example implementations of the present disclosure.
  • FIG. 5 shows a simplified schematic illustration of interfaces between service A function and service B functions according to one or more example implementations of the present disclosure.
  • FIG. 6 shows a simplified schematic illustration of possible ways for data sharing between service A function and service B functions according to one or more example implementations of the present disclosure.
  • FIG. 7 shows a schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • FIG. 8 shows a procedure for data sharing and processing between service A function and service B functions according to one or more example implementations of the present disclosure.
  • FIG. 9 shows call flows for data sharing between service A function and service B functions via indirect interface on C/M plane according to one or more example implementations of the present disclosure.
  • FIG. 10 shows another procedure for data sharing and processing between service A function and service B functions according to one or more example implementations of the present disclosure.
  • FIG. 11 shows another call flow for data sharing between service A function and service B functions via direct interface according to one or more example implementations of the present disclosure.
  • FIG. 12 shows another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • FIG. 13 shows yet another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • FIG. 14 shows yet another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • FIG. 15 shows yet another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • FIG. 16 shows yet another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • FIG. 17 shows a schematic flowchart of a procedure of data collection and pre-processing according to one or more example implementations of the present disclosure.
  • FIG. 18 shows a schematic flowchart of a procedure of data analytics according to one or more example implementations of the present disclosure.
  • FIG. 19 shows a schematic flowchart of a procedure of data de-privatization according to one or more example implementations of the present disclosure.
  • FIG. 20 shows a schematic flowchart of a procedure of data delivery from service requester to data destination according to one or more example implementations of the present disclosure.
  • FIG. 21 shows a simplified schematic illustration of network architecture to implement DAM service in evolutionary way according to one or more example implementations of the present disclosure.
  • FIG. 22 shows a simplified schematic illustration of network architecture to implement DAM service in revolutionary way according to one or more example implementations of the present disclosure.
  • FIG. 23 shows a schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • FIG. 24 shows another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • FIG. 25 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • FIG. 26 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • FIG. 27 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • FIG. 28 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • FIG. 29 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • FIG. 30 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • FIG. 31 shows a schematic structural diagram of an apparatus according to one or more example implementations of the present disclosure.
  • the different types of data includes 3GPP data and non-3GPP data.
  • the 3GPP data includes: 3GPP-based sensing data, network data, AI related data, UE subscription data, etc.
  • the non-3GPP data includes non-3GPP-based sensing data (e.g., sensing data from Radar, LiDAR, or WiFi) , sensor data (e.g., data from camera sensor, video sensor) , and other data (e.g., data from digital user/world/twin, 3 rd party data) .
  • 3GPP-based sensing data includes raw sensing signal, sensing measurement data, pre-processed sensing data (e.g., compressed sensing data) , and sensing result (e.g., result obtained with analyzing 3GPP-based sensing data) .
  • sensing result can be obtained with analyzing both 3GPP-based sensing data and non-3GPP-based sensing data) .
  • network data includes the data collected from network functions (e.g., control plane or user plane logical network functions) , e.g., channel measurement data, positioning data, location tracking data, UE connectivity management data, session management data, slice usage data, traffic monitor and detection data (e.g., traffic statistics data, ) , and other data for network operations, administration and maintenance.
  • network functions e.g., control plane or user plane logical network functions
  • channel measurement data e.g., channel measurement data, positioning data, location tracking data, UE connectivity management data, session management data, slice usage data, traffic monitor and detection data (e.g., traffic statistics data, )
  • traffic monitor and detection data e.g., traffic statistics data,
  • AI related data includes AI model (e.g., AI model structure, parameter) , AI dataset for training (inference, test or verification) , intermediate data generated during training (inference, test or verification) (e.g., gradient value) .
  • AI model e.g., AI model structure, parameter
  • AI dataset for training inference, test or verification
  • intermediate data generated during training inference, test or verification
  • gradient value e.g., gradient value
  • the data source can be terminal device (e.g., UE, vehicle, radar, sensor, and actuator) , wireless or wired physical infrastructure, virtual network function, and even 3rd parties.
  • terminal device e.g., UE, vehicle, radar, sensor, and actuator
  • the network data e.g., network operation data
  • NFs network functions
  • 3GPP-based sensing data from UE and RAN
  • Non-3GPP-based sensing data from Radar, LiDAR, or WiFi nodes.
  • FIG. 1 shows a simplified schematic illustration of 5G NFs for data collection and management on control plane according to one or more example implementations of the present disclosure.
  • data could be collected, analyzed and managed via network functions such as NWDAF, DCCF, MFAF, LMF, ADRF, MDAF, etc.
  • NWDAF Network Access Management Function
  • DCCF DCCF
  • MFAF Magnetic Fidelity
  • LMF Layer Management Function
  • ADRF MDAF
  • MDAF etc.
  • MDT Minimization of Drive Tests
  • SON self-organizing network
  • Previous networks e.g., 5G network
  • logical functions e.g., NWDAF, DCCF, MFAF, and LMF
  • NWDAF Network-to-Memastructure
  • DCCF DCCF
  • MFAF MFAF
  • LMF Layer-to-Memastructure
  • Protection of privacy which can be included in or inferred from data is less considered in previous network.
  • Future networks should support more diversified types of data including 3GPP data (e.g., 3GPP-based sensing data, network data, AI related data, UE subscription data) and non-3GPP data (e.g., 3 rd party data, sensor data, non-3GPP-based sensing data) .
  • 3GPP data e.g., 3GPP-based sensing data, network data, AI related data, UE subscription data
  • non-3GPP data e.g., 3 rd party data, sensor data, non-3GPP-based sensing data
  • the data consumers could be devices, network functions and 3 rd party applications, etc.
  • There will be extremely large amount of data (e.g., sensing data) in future network the burden will be huge if the data is still collected, analyzed and delivered on C/M plane.
  • This disclosure solves the following problem with the assumption that data service A which is supported only by C/M plane function, and data service B which is supported by both C/M function and data plane function, coexist in future network (s) : - How data service A and data service B share data (e.g., for data fusion/aggregation) with each other.
  • the data service A can be but is not limited to the service supported by logical functions (e.g., NWDAF, DCCF, MFAF, and LMF) on C/M plane.
  • service A focus on the data managed by the logical functions (e.g., NWDAF, DCCF, MFAF, and LMF) , e.g., network data.
  • the data service B can be but is not limited to the service supported by both logical functions on C/M plane and logical functions on data plane. In some cases, service B focus on new types of data, e.g., 3GPP-based sensing data, non-3GPP-based sensing data, AI related data, and sensor data.
  • the data service A can be but is not limited to the service provided by previous network (e.g., 5G network) .
  • the data service B can be but is not limited to the service provided by future network (e.g., 5.5G network, 6G network) .
  • Both data service A and data service B can be provided at the same time by a network (e.g., in a previous network, or a future network) .
  • Data Plane is defined as a plane which includes a collection of data process functions of XaaS services and is used for traffic processing and/or transmission.
  • Data plane can be a new plane deployed in a revolutionary method in future network, or an enhanced plane (e.g., enhanced user plane) in an evolutionary method in future network.
  • the Data Plane include Data-TW-GW, RAN node and XaaS PSF.
  • the Data Plane include UPF+, RAN node and XaaS PSF. If the XaaS service is only for packet data unit (PDU) connectivity, the Data Plane include UPF+ and RAN node, without XaaS PSF.
  • PDU packet data unit
  • one or more of data Service A and data Service B include ISAC services, e.g., data Service B includes ISAC service.
  • This disclosure protects the hybrid mode of data collection and management, where both service A function (e.g., NWDAF on C/M plane plane) and service B functions (e.g., DAM-TCF on C/M plane, DAM-PSF on data plane) are deployed.
  • the Service A function is for a type of data (e.g., network data)
  • Service B functions are for another type of data (e.g., sensing data, AI related data) .
  • the interfaces between service A function and service B functions are defined, and 4 possible ways of data sharing and processing (e.g., data fusion of network data and sensing data) between service A function and service B functions are protected.
  • New procedures on C/M plane and new actions of data plane functions e.g., GW/UPF+, DAM-PSF
  • the new information in C/M plane message and new information encapsulated in data plane packet are also protected.
  • data sharing and fusion can be performed within network on C/M plane and data plane, for different types of data, e.g., network data (or analytics results and AI model) from control plane, 6G new types of data (e.g., sensing data, AI data) from data plane.
  • types of data e.g., network data (or analytics results and AI model) from control plane, 6G new types of data (e.g., sensing data, AI data) from data plane.
  • the proposed 6G network architecture has been designed with a few important principles and requirements: openness, trustworthiness, simplicity in standardization, scalability, rapid deployment of 6G networks and future-proofing.
  • the proposed 6G network architecture design applies modularization strategy, utilizes service-based (XaaS) concepts and network virtualization techniques.
  • a procedure of the 6G System may include some procedures that can be reused by other procedures.
  • Such a reusable procedure is defined as a basic procedure.
  • a complex procedure can, thus, include multiple sequential or parallel basic procedures. It is expected that such methodology can simplify designs of procedures.
  • FIG. 2 shows a simplified schematic illustration of 6G system conceptual structure according to one or more example implementations of the present disclosure.
  • Infrastructure Layer includes infrastructures supporting 6G services. Among them are wireless networks (RAN, Core Network (CN) infrastructures, Cloud/data center infrastructures, satellite networks, storage/database infrastructures, and sensing networks, and etc. These infrastructures can be provided by a single provider or by multiple providers.
  • RAN wireless networks
  • CN Core Network
  • Cloud/data center infrastructures Cloud/data center infrastructures
  • satellite networks satellite networks
  • storage/database infrastructures and sensing networks, and etc.
  • each XaaS service is provided by identified 5G logical functions.
  • a XaaS service can be provided with 5G enhancement by more than one approaches. The figure above is only an example.
  • NET4AI Network for AI
  • 6G CN/RAN 6G CN/RAN which enables network with the capability to conduct/execute AI training/inferencing task (s) . i.e., AI task (s) , by network-based computing and communication resources.
  • NWDAF network data analytics function
  • NET4Data service provides a decentralized architecture for data stakeholders to collaboratively manage data lifecycle events. These data lifecycle events include data storage and data sharing. The data could be public, private, sensitive, confidential.
  • the NET4Data service could be integrated into the 5GS, or could be enhanced by the 5GS.
  • DAM focus on different types of data: network data (e.g., data collected from network functions, XaaS service) , ISAC data (3GPP-based sensing data (e.g., from UE and RAN) , Non-3GPP-based sensing data (e.g., from Radar, LiDAR, WiFi Sensing) ) , sensor data (e.g., data from camera sensor, video sensor) , and other data (e.g., Digital user data, 3 rd party data, synthetization data, and AI data) .
  • DAM provides services for a variety of data consumers, e.g., XaaS service, 3 rd party, NF, UE, etc.
  • 5G system logical functions for example: NWDAF, DCCF, and MFAF of control plane can be enhanced to support DAM service in an evolutionary solution.
  • - Network for Digital World (NET4DW) as a service provides the capability of intelligent integration/synthesis of information from the physical world and digital world (DW) .
  • Customers of NET4DW can be individuals, industries, governments. The customers can have the capability of creation, control, and management of a variety of applications running in the DW such as virtual reality applications.
  • DW services can be supported by enhancing 5G functions and adding new functions (e.g., an evolutionary solution) where necessary.
  • - Network for connectivity (NET4CON) as a service provides a capability to support exchange of messages and data among new 6G services.
  • the basic capabilities of NET4CON include to manage logical topology among XaaS services and between 6G XaaS services and all types of 6G system customers, to introduce intelligent GWs for controlling dynamic forwarding based on configured procedure principle and to support anonymous interactions among these XaaS services and customers by the introduced intelligent GWs.
  • the NET4CON service is provided by enhancement of 5G system.
  • - Mission Management (MM) as a Service provides a capability to program provisioning of XaaS services at Service Layer to provide mission services.
  • a mission is to achieve a designated goal, known as mission goal, which includes providing PDU connectivity and optionally providing data processing.
  • the MM services include the following: mission information management service, mission session management service, mission execution and access management service.
  • CM Connectivity Management
  • FIG. 3 shows a simplified schematic illustration of deployment of 6G system according to one or more example implementations of the present disclosure, where FIG. 3 is used to show one example of deployment of the 6G system in evolutionary solution.
  • the “+” represents “enhanced” , for example, the 5G AMF-Mobility function is enhanced, denoted as AMF-Mobility+, the 5G RRC function is enhanced, denoted as RRC+, the 5G Network Repository Function (NRF) is enhanced, denoted as NRF+, the 5G SMF is enhanced, denoted as SMF+, the 5G Network Exposure Function (NEF) is enhanced, denoted as NEF+, the 5G Authentication Server Function (AUSF) is enhanced, denoted as AUSF+, other enhanced functions are not described in detail herein.
  • C/M Radio Bearer (C/M RB) of a 6G device over-the-air connection for carrying control signaling for over-the-air interface management and C/M plane messages.
  • a 6G device can have multiple C/M RBs.
  • Data Radio Bearer (Data RB) of a 6G device over-the-air connection for carrying Data plane traffic.
  • a 6G device can have multiple Data RBs.
  • RB endpoint endpoint of an RB at network side.
  • An endpoint of an RB protocol stack e.g., PDCP
  • PDCP can be in, e.g., a RAN BAS domain, but not limited to.
  • an RB endpoint can be flexibly deployed/selected for a device.
  • RB handler over-the-air interface protocol stack handler.
  • An RB handler is defined as a logical function which perform RB protocol stack operations after getting configurations.
  • a protocol handler is PDCP-only handler or whole protocol stack handler.
  • An RB handler accepts RB configuration from CM service.
  • An RB handler also accepts security configuration, e.g., keying material, from Service Provisioning Management (SPM) service.
  • SPM Service Provisioning Management
  • the NET4CON service which is main service impacting on 6G system architecture is implemented by enhanced 5G Service Communication Proxy (SCP+) as C/M plane GW and enhanced 5G User Plane Function (UPF+) as data plane GW.
  • SCP+ Service Communication Proxy
  • UPF+ enhanced 5G User Plane Function
  • Proposed per device/D-User C/M session and data session are defined as logical connection between a device/D-User and its serving SCP+ (C/M-TW-GW) and serving UPF+ (Data-TW-GW) . All XaaS services are deployed across multiple BAS/clouds.
  • the 6G customer can be of various types, including a device (e.g., electronic device ED, terminal device) , apparatus, a chip, an equipment (e.g., user equipment) etc.
  • a device e.g., electronic device ED, terminal device
  • apparatus e.g., a chip
  • equipment e.g., user equipment
  • the customer may be an individual customer, a business customer, etc.
  • the 6G customer is used to connect persons, objects, machines, etc.
  • the 6G customer may be widely used in various scenarios including, for example, cellular communications, device-to-device (D2D) , vehicle to everything (V2X) , peer-to-peer (P2P) , machine-to-machine (M2M) , MTC, internet of things (IoT) , virtual reality (VR) , augmented reality (AR) , mixed reality (MR) , metaverse, digital twin, industrial control, self-driving, remote medical, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc.
  • D2D device-to-device
  • V2X vehicle to everything
  • P2P peer-to-peer
  • M2M machine-to-machine
  • MTC internet of things
  • IoT internet of things
  • VR virtual reality
  • AR augmented reality
  • MR mixed reality
  • metaverse digital twin
  • Each 6G customer represents any suitable end user device for wireless operation and may include such devices (or may be referred to but not limited to) as a user equipment (UE) or a user device or a terminal device, a wireless transmit/receive unit (WTRU) , a mobile station, a fixed or mobile subscriber unit, a cellular telephone, a station (STA) , a MTC device, a personal digital assistant (PDA) , a smartphone, a laptop, a computer, a tablet, a wireless sensor, a consumer electronics device, a smart book, a vehicle, a car, a truck, a bus, a train, or an IoT device, wearable devices (such as a watch, a pair of glasses, head mounted equipment, etc.
  • UE user equipment
  • WTRU wireless transmit/receive unit
  • PDA personal digital assistant
  • Future generation 6G customer may be referred to using other terms.
  • an 6G customer performs (or is configured to perform) a method described herein, it may be interpreted as the ED, one or more module (or units) in the ED, a circuit or chip, or a combination thereof, may perform the method.
  • the circuit or chip may include a modem chip, also referred to as a baseband chip, a system on chip (SoC) including a modem core, or system in package (SIP) , and the like, and may be responsible for one or more communication functions in the ED.
  • SoC system on chip
  • SIP system in package
  • FIG. 4 shows a simplified schematic illustration of an apparatus in a communication system according to one or more example implementations of the present disclosure, e.g., the 6G system in FIG. 3.
  • the apparatus 320 may be an electronic device (e.g. ED or other 6G customer) , a network node such as RAN, any components in RAN, CN or any Network Function of CN.
  • apparatus 320 may include at least one processor 260. Only one processor 260 is illustrated to avoid congestion in the drawing.
  • the processor 260 may perform (or control the apparatus 320 to perform) operations (or methods) described herein as being performed by the apparatus 320.
  • the apparatus 320 may further include a transmitter 252 and a receiver 254 coupled to one or more antennas.
  • the transmitter 201 and the receiver 203 may be integrated, e.g. as a transceiver.
  • the transceiver is configured to modulate data or other content for transmission by at least one antenna or network interface controller (NIC) .
  • the transceiver is also configured to demodulate data or other content received by the at least one antenna.
  • Each transceiver includes any suitable structure for generating signals for wireless or wired transmission and/or processing signals received wirelessly or by wire.
  • Each antenna includes any suitable structure for transmitting and/or receiving wireless or wired signals.
  • the transceiver (or transmitter 252 and/or receiver 254) may be viewed as an interface circuit.
  • the apparatus 320 may include at least one memory 258.
  • the memory 258 stores instructions used to perform operations described herein.
  • the memory 258 may also stores data used, generated, or collected by the apparatus 320.
  • the memory 258 could store software instructions or modules configured to implement some or all of the functionality and/or embodiments described herein and that are executed by one or more processor 260.
  • embodiments of this disclosure may be provided as a method, an apparatus (or system) , computer-readable storage medium, or a computer program product. Therefore, this disclosure may use a form of a hardware-only embodiment, a software-only embodiment, or an embodiment with a combination of software and hardware. Moreover, this disclosure may use a form of a computer program product that is implemented on one or more computer-usable storage media (including but not limited to a disk memory, an optical memory, and the like) that include computer-usable program code.
  • FIG. 5 shows a simplified schematic illustration of interfaces between service A function and service B functions according to one or more example implementations of the present disclosure.
  • service A function e.g., NWDAF on C/M plane
  • service B functions e.g., DAM-TCF on C/M plane, DAM-PSF on data plane
  • service A function include one or more of: NWDAF, DCCF, MAFA, LMF, ADRF, and MDAF.
  • Service A function is deployed on C/M plane.
  • NWDAF can perform data collection (DC) , data analytics (DA) , and data delivery (DD) .
  • Service B functions include functions on C/M plane and functions on data plane. This disclosure takes DAM service as a type of Service B as an example.
  • DAM-TCF is deployed on C/M plane
  • DAM-PSF is deployed on data plane.
  • Service A and Service B include Integrated Sensing and Communication (ISAC) services, e.g., Service B includes ISAC service.
  • ISAC Integrated Sensing and Communication
  • FIG. 5 it illustrates the network architecture to implement Service B (e.g., DAM service) by remaining previous network logical functions (e.g., NWDAF, DCCF, and MFAF) unchanged or evolved and introducing additional functions (e.g., DAM-TCF, DAM-PSF) of Service B.
  • NWDAF Network logical Function
  • DCCF DCCF
  • MFAF additional functions
  • DAM-TCF DAM-PSF
  • C/M plane both previous logical functions (e.g., NWDAF, DCCF, and MFAF) and DAM-TCFs are deployed, and on data plane, new function DAM-PSFs are deployed.
  • other network functions are considered to be evolved from previous networks, e.g., SCP+ on control plane and UPF+ on data plane.
  • the SCP+ is deployed as C/M-TW-GW
  • the UPF+ is deployed as Data-TW-GW.
  • Different DAM-PSFs in the same or different network domains can be connected directly (e.g., in mesh topology) or indirectly (e.g., via UPF+) .
  • DAM-TCFs control and configure the DAM-PSFs.
  • the functions (data collection, data analytics and data delivery) of previous network logical functions keep unchanged or evolved to manage NF data (e.g., data from CN NFs)
  • NF data e.g., data from CN NFs
  • DAM-TCF and DAM-PSF manage other data (e.g., ISAC data) for data collection, data analytics, data delivery and data de-privatization (with newly introduced data de-privatization function) .
  • Previous network logical functions e.g., NWDAF, DCCF, and MFAF
  • DAM-PSF can exchange data via direct interface between them, or via UPF+ indirectly (e.g., there is SBI-based interface between NWDAF and UPF in latest 5G release) , or via DAM-TCF indirectly.
  • some types of raw data e.g., NF data from NFs
  • the other types of raw data e.g., ISAC data
  • the Service A function is for a type of data (e.g., network data) and Service B functions are for another type of data (e.g., sensing data, sensor data, AI related data) . It does not rule out other possibilities, for example, both service A function and service B functions are for the same type of data (e.g., network data, AI related data, or sensing data) .
  • service A function There are interfaces between service A function and service B functions.
  • the interface on C/M plane between service A function e.g., NWDAF, DCCF, MFAF, or LMF
  • service B C/M plane function e.g., DAM-TCF
  • the interface on C/M plane can be SBI-based or not.
  • the interface between the service A function and service B data plane function e.g., DAM-PSF
  • the interface can be SBI-based or not.
  • service B C/M plane function e.g., DAM-TCF
  • service B data plane function e.g., DAM-PSF
  • the interface can be SBI-based or not.
  • the service B C/M plane function (e.g., DAM-TCF) has interfaces with other C/M plane functions (e.g., AF, SCP+, AMF+) .
  • the service B C/M plane function (e.g., DAM-TCF) may have interfaces with other data plane functions (e.g., Data-TW-GW/UPF+) which is not illustrated in FIG. 5.
  • the service B data plane function e.g., DAM-PSF
  • has interface with other data plane functions (e.g., Data-TW-GW/UPF+, RAN node, AS/DN) .
  • the service B data plane function (e.g., DAM-PSF) can connect to RAN node directly via direct interface or indirectly via intermediate functions (e.g., Data-TW-GW/UPF+) , can connect to AS/DN directly or indirectly via intermediate functions (e.g., Data-TW-GW/UPF+) .
  • the RAN node can connect to core network on C/M plane, e.g., via AMF+, SCP+ or other functions.
  • Hierarchical deployment of service B functions are enabled.
  • the service B functions e.g., DAM functions
  • the service B functions can be deployed in the cloud center, the transport network, core network and even extended into RAN or end-device.
  • Global and domain service B functions e.g., DAM functions
  • the global service B functions e.g., DAM function
  • the global service B functions is in charge of a whole service, and coordinates the domain service B functions (e.g., DAM functions) .
  • domain service B functions e.g., DAM function
  • RAN and global service B functions (e.g., DAM function) is deployed in core network.
  • Global and domain service B functions e.g., DAM functions
  • An entity with both data analysis function and data collection function may know private information about an object from the collected raw data if the entity wants. It may incur huge privacy and security issues. It recommends decoupling the data de-privatization functions with the data collection function and data analysis functions. The decoupling could avoid that all functions are owned and managed by one single entity. For example, the decoupled functions could be in the responsibilities of different players.
  • Data de-privatization is executed before the data is shared within a network or across different networks. It enables the flexible and trustworthy intra-and inter-network data sharing, and releases the concern about the leakage of data privacy. It applies to the current network ecosystem, also applies to future multi-party ecosystem where network participations are not only the operator, but also other players such as virtual network service provider, 3rd parties, etc.
  • DAM service includes data collection as a service (DCaaS) , data de-privatization as a service (DPaaS) , data analytics as a service (DAaaS) , and data delivery as a service (DDaaS) .
  • DAM service collects data from data source, and manages the lifecycle of the collected data, e.g., to perform data statistic feature analytics, data privacy protection, data pre-processing (e.g., data cleaning, data normalization, data stitching, data filtering, data aggregation) , data sharing, and data wiping.
  • the collected data can be network data (e.g., traffic statistics data, network operation data) , sensing data (3GPP-based data, non-3GPP-based data) , sensor data (e.g., IoT data) , AI related data (e.g., generative data, synthetic data) , etc.
  • the data source can be terminal device (e.g., UE, vehicle, radar, sensor, and actuator) , wireless or wired physical infrastructure, virtual network function, and even 3rd parties.
  • DAM Data Analytics and Management
  • DAM provides unified data collection, privacy protection, analytics, delivery and management service for a variety of data consumers, e.g., NET4AI, AI4NET, NET4Data, NET4DW, NET4CON, 3rd parties, etc.
  • DAM collects data from data source and then delivers data to DAM customer. Before delivering data to DAM customer, DAM may perform data de-privatization and data analytics.
  • DAM service includes Data collection as a service (DCaaS) .
  • DCaaS collects data from data source.
  • the data can be network data, sensing data, IoT data, data for AI, synthetic data, etc.
  • the data source can be terminals (e.g., UE, vehicle, radar, sensor, and actuator) , physical infrastructure, virtual network function, and even 3rd parties.
  • DCaaS performs data pre-processing if the data is non-directly useable, useless or redundant, to obtain the final target data.
  • One or multiple DAM nodes of DCaaS may participate into data pre-processing to obtain the final target data.
  • DCaaS may collect synthetic model or synthetic data of original data from data source for efficient data collection.
  • DAM service includes Data de-privatization as a service (DPaaS) .
  • DPaaS protects the privacy of the data and prevents the leakage of the privacy to any unauthorized entity.
  • the privacy may be contained in the data, or can be inferred from the data.
  • the privacy can be identification, location, trajectory, state, activity, biometric feature, data statistical characteristic, data feature, network topology, network configuration and any other private information which can be included in or inferred from the data.
  • DPaaS outputs the de-privatized data after performing data sanitization with privacy-preserving methods.
  • DPaaS may generate synthetic model or synthetic data of the original private data, and output the synthetic model or synthetic data to data consumer instead of the original private data for privacy protection.
  • DAM service includes Data analytics as a service (DAaaS) .
  • DAaaS analyzes and mines insight and knowledge from data. For example, DAaaS extracts data information from the raw data, and calculates correlation level between data items or datasets. The extracted information can be metadata, data representation, data feature, data statistical characteristics, etc. The data correlation level indicates the coherency between different data items or datasets.
  • the mined insight and knowledge can be used for data retrieval and data discovery.
  • DAM service includes Data delivery as a service (DDaaS) .
  • DDaaS delivers data to appropriate one or multiple destinations.
  • the data can be original data or post-processed data.
  • the destination can be a final DAM customer or an intermediate DAM nodes executing DAM tasks.
  • Security should be guaranteed during the data delivery. For example, ciphering and integrity protection should be provided to the data over the air and wired link.
  • Data forwarding may be terminated at an entity (e.g., a DAM node) within the network or an entity (e.g., an application server (AS) out of the network.
  • DDaaS decides data forwarding path for traffic steering, traffic aggregation, loop transmission, etc.
  • Data forwarding at each entity may be based on the path selection information configured to each entity or the path selection information encapsulated in packet header.
  • Data processing may be executed along data forwarding.
  • the data processing includes data pre-processing in DCaaS, data de-privatization in DPaaS and data analytics in DAaaS.
  • Data processing may executed in sequence or parallel among multiple entities.
  • DDaaS should work together with DCaaS, DAaaS and DPaaS.
  • DDaaS may support unified data query and data provision.
  • DAM service functions include C/M plane (e.g., DAM-TCF, DAM-SCF) and Data plane function (e.g., DAM-PSF) .
  • C/M plane e.g., DAM-TCF, DAM-SCF
  • Data plane function e.g., DAM-PSF
  • DAM C/M plane e.g., DAM TCFs
  • DAM PSFs DAM data plane
  • DAM PSFs setups the connections between DAM PSFs
  • DAM PSFs DAM data plane
  • external entities e.g., Data-TW-GW/UPF+
  • DAM service functions could be deployed as slice or distributed within the network infrastructures (e.g., within RAN, CN, clouds of 3rd parties, terminal, etc. )
  • DAM-TCF includes TCF for DCaaS (DC-TCF) , DC-TCF transmits and receives messages to interact with data source, DAM customer, network functions of DAM consortium, and other network functions.
  • DC-TCF selects data collection &pre-processing (DCP) rule as per customer’s request.
  • the DCP rule may indicate what data should be collect from which data source.
  • the DCP rule may indicate how the final target data can be obtained from raw data, e.g., the rule to modify and clean non-directly useable data, and to filter useless or redundant data.
  • the DCP rule indicates a function or an algorithm with which to process the data, metrics on data processing, data processing treatment parameter, and metrics on data collection.
  • DC-TCF selects DC-PSFs.
  • DC-TCF may select DC-PSF based on the capability of DCP-PSF, access overhead, network topology, mobility, communication link state, DCP-PSF’s connectivity with external entity (e.g., data-TW-GW) , etc.
  • DC-TCF configures the selected DCP rule to the selected DC-PSFs.
  • DC-TCF maintains data information on original data and maintains data correlation information.
  • the data information may be original data’s metadata, data representation, data name, data title, key word, semantics, data attribute, data feature, application category, etc.
  • Data correlation information indicates the coherency of different data items or datasets.
  • the data information may be reported by data source e.g. via registration, or may be extracted from original data e.g.
  • DC-TCF discovers and selects data source, e.g., with the joint consideration of the data consumer’s requirement, data correlation information, data access overhead, etc.
  • DAM-TCF includes TCF for DPaaS (DP-TCF) .
  • DP-TCF transmits and receives messages to interact with DAM customer, network functions of DAM consortium, and other network functions.
  • DP-TCF selects data de-privatization rule as per DAM customer’s request.
  • the data de-privatization rule indicates how to de-privatize data.
  • the rule may include but is not limited to de-privatization method, algorithm (e.g., privacy protection algorithm, ciphering key and other parameters for privacy protection) , security mode, metrics (e.g., privacy level, accuracy level) , etc.
  • the security mode indicates that none, one or more than one of: security protection and privacy protection, are to be executed on one or both of: a user plane (UP) and a control plane (CP) .
  • UP user plane
  • CP control plane
  • the security mode indicates none, one or more than one of security protection and privacy protection are to be executed over the air.
  • DP-TCF selects DP-PSFs, and configures the selected data de-privatization rule to the selected DP-PSFs.
  • DAM-TCF includes TCF for DAaaS (DA-TCF) .
  • DA-TCF transmits and receives messages to interact with DAM customer, network functions of DAM consortium, and other network functions.
  • DA-TCF selects data analytics rule as per DAM customer’s request.
  • Data analytics rule is to indicate how to mine insight and knowledge from the original data.
  • the data analytics rule may be data analytics model, algorithm, metrics (e.g., analytics accuracy) , etc.
  • DCaaS may be a customer of DAaaS.
  • data analytics rule may indicate how to mine data information (e.g., metadata, data representation, data feature, etc. ) on original data or how to calculate data correlation information.
  • Data correlation information indicates the coherency of different data items or datasets.
  • the correlation may be equality or inequality, similarity or dissimilarity, inclusion or exclusion dependency, transitive correlation, etc.
  • the mined data information or data correlation information may be feedback to DCaaS for further maintenance. They can be used for data discovery and data retrieval.
  • DA-TCF selects DP-PSFs, and configures the selected data analytics rule to the selected DP-PSFs.
  • DAM-TCF includes TCF for DDaaS (DD-TCF) .
  • DD-TCF transmits and receives messages to interact with DAM customer, network functions of DAM consortium, and other network functions.
  • DD-TCF selects data delivery rule as per DAM customer’s request.
  • the data delivery rule indicates how the data can be delivered to one or multiple destinations.
  • the data delivery rule may include data forwarding path selection information, data forwarding treatment parameters, etc.
  • the data forwarding treatment parameters include data loss rate, data transfer latency, etc.
  • DD-TCF selects DD-PSF, and configures the selected data delivery rule to the selected DD-PSF.
  • Data forwarding path may be scheduled and programmed by DD-TCF for the involved DD-PSFs.
  • the path may include radio link and wired tunnels.
  • the path may include multiple segments in sequence and parallel.
  • Data forwarding path selection information may be configured by DD-TCF to all the involved DD-PSF. For example, one-shot tunnels between the DD-PSFs along the path may be established under the control of DD-TCF, and the mapping between one or multiple previous-hop tunnels and one or multiple next-hop tunnels are configured to each of the involved DD-PSFs along the path.
  • the data forwarding path selection information may be only configured by DD-TCF to parts of the involved DD-PSFs along the path. For example, it is only configured to the firstly involved DD-PSF.
  • Parts of the involved DD-PSFs may encapsulate the path selection information into packet header, and the other involved DD-PSFs forward data to one or multiple next-hop DD-PSFs based on the instruction of the path selection information encapsulated in the packet header.
  • DAM-PSF includes PSF for DCaaS (DC-PSF) .
  • DC-PSF executes data collection and pre-processing task under the control of DC-TCF.
  • One or multiple DC-PSFs may participate into data pre-processing to obtain the final target data.
  • Several DC-PSFs may work together to collect and preprocess data to serve a specific data consumer, and one of the DC-PSF may serve as the DC-PSF anchor.
  • Each of the DC-PSFs may execute different data collection and preprocessing tasks, the collected and processed data can be exchanged between the DC-PSFs and the final target data is aggregated at the DC-PSF anchor.
  • DAM-PSF includes PSF for DPaaS (DP-PSF) .
  • DP-PSF executes data de-privatization task under the control of DP-TCF.
  • DP-PSF should support privacy-preserving computation if the original data needs to be transparent to the DP-PSF. For example, the generative model training which does not touch the real original data is executed at DP-PSF while the discriminative model training which touch the real original data is executed at data source.
  • DAM-PSF includes PSF for DAaaS (DA-PSF) .
  • DA-PSF executes data analytics task under the control of DA-TCF.
  • DA-PSF should also support privacy-preserving computation if the original data need be transparent to the DA-PSF.
  • DAM-PSF includes PSF for DDaaS (DD-PSF) .
  • DD-PSF execute data delivery task under the control of DD-PSF to deliver the original data or the processed version of the original data to one or multiple destinations.
  • One or multiple DD-PSFs deliver data via a specific data forwarding path.
  • the path selection information can be indicated to each DD-PSF via configuration message from DD-TCF or via being encapsulated into packet header.
  • the DD-PSF deliver data via the path determined by the path selection information.
  • DD-PSF may perform packet header encapsulation and decapsulation on the path selection information. For example, DD-PSF may update the information and delete expired information in the packet header.
  • the data processing configuration information may be encapsulated into packet header together with the path selection information.
  • DAM-PSF DAM service control function
  • DAM-SCF includes SCF for DCaaS (DC-SCF) .
  • DC-SCF customizes, orchestrates and provisions DCaaS.
  • DC-SCF builds, updates and manages Data Collection and Pre-processing (DCP) rule library.
  • the rules can be configured to or invoked by DC-TCF and DC-PSF.
  • the data to be collected can be raw data, data model, data representation, and synthetic data.
  • DCP rule is for but not limited to data cleaning, data normalization, data filtering, data stitch, data compression, data synthetization, feature engineering, dataset classification, data structuring, and data representation management. Each rule can be used to provide services of specific requirements.
  • DAM-SCF includes SCF for DPaaS (DP-SCF) .
  • DP-SCF customizes, orchestrates and provisions DPaaS.
  • DP-SCF builds, updates and manages data de-privatization method library. The methods can be configured to or invoked by DP-TCF and DP-PSF.
  • Data de-privatization method includes but is not limited to obfuscation-based method (e.g., K-anonymity, differential privacy, pseudonym) , cryptography-based method (e.g., homomorphic encryption, functional encryption, multi-party computation, zero knowledge) , hardware-based method (e.g., secure enclave, TEE) , and AI-based method (e.g., generative adversarial network (GAN) , auto-encoder) .
  • obfuscation-based method e.g., K-anonymity, differential privacy, pseudonym
  • cryptography-based method e.g., homomorphic encryption, functional encryption, multi-party computation, zero knowledge
  • hardware-based method e.g., secure enclave, TEE
  • AI-based method e.g., generative adversarial network (GAN) , auto-encoder
  • GAN generative adversarial network
  • DAM-SCF includes SCF for DAaaS (DA-SCF) .
  • DA-SCF customizes, orchestrates and provisions DAaaS.
  • DA-SCF builds, updates and manages data analytics model library.
  • the models can be configured to or invoked by DA-TCF and DA-PSF.
  • Data analytics model is for but not limited to data mining, data representation learning, feature extraction, data embedding, dataset classification, data labeling, and data correlation evaluation. Each model can be used to provide services of specific requirements.
  • DAM-SCF includes SCF for DDaaS (DD-SCF) .
  • DD-SCF customizes, orchestrates and provisions DDaaS.
  • DD-SCF builds, updates and manages data delivery rule library.
  • the rule can be configured to or invoked by DD-TCF and DD-PSF.
  • the delivered data can be the original data, or post-processed (e.g., posted analytics) data.
  • the data delivery rule is for but not limited to traffic steering, data forwarding, data aggregation, and routing. Each rule can be used to provide services of specific requirements.
  • Hierarchical Architecture are enabled for the deployment of DAM service.
  • the DAM functions can be deployed in the cloud center, the transport network, core network and even extended into RAN or end-device.
  • Hierarchical deployment of DAM functions are enabled.
  • Global and domain DAM functions cooperate to provide service.
  • the global DAM function is in charge of a whole DAM service, and coordinates the domain DAM functions.
  • domain DAM function is deployed in RAN
  • global DAM function is deployed in core network.
  • Global and domain DAM functions interact on C/M plane and exchange data on data plane for cooperation.
  • DAM TCFs there may be global TCFs and domain TCFs.
  • the global TCF controls a whole DAM service with the involvement of one or multiple of DCaaS, DPaaS, DAaaS and DDaaS.
  • the domain TCF controls one service or a subset services involved in the whole DAM service.
  • a domain TCF controls one of DCaaS, DPaaS, DAaaS and DDaaS.
  • the global DAM TCF is the anchor TCF contacting with external entity.
  • the external entity can be DAM customer, data source, Mission Control Function (MCF) , C/M-TW-GW, etc.
  • MCF Mission Control Function
  • the global TCF receives service request from external entity, maps and assigns the involved DAM tasks to specific domain TCFs.
  • DAM SCFs there may be global SCFs and domain SCFs.
  • the global DAM SCF is the anchor SCF contacting with external entity.
  • some of the DAM PSFs may be active as boarder DAM PSFs, and the boarder DAM PSFs can be connected to external entity.
  • the external entity on data plane can be DAM customer, data source, Mission Control Function (MCF) , Data-TW-GW, etc.
  • MCF Mission Control Function
  • Data-TW-GW Data-TW-GW
  • one of the DD-PSFs may be selected to be the anchor point to deliver data to DAM customer.
  • the external entity (e.g., MCF) on control plane and DAM TCFs may cooperate to select boarder DAM PSFs to execute DAM tasks, e.g., via bottom-up or top-down approaches.
  • Each service DAM module may provide one or multiple of DCaaS, DPaaS, DAaaS and DDaaS, and consist of the corresponding DAM functions on C/M plane and data plane. Different DAM functions may be deployed separately or integratedly.
  • DAM functions of C/M plane and data plane could be deployed as new type of network entities in core network, RAN and UE.
  • DAM PSF unit and DAM TCF unit can be deployed as new core network functions or new RAN node.
  • DAM functions of C/M plane and data plane could be deployed in new type of protocol layers in CN, RAN and UE.
  • the new protocol layer carries data and signalling of DAM service. Different from the traditional connectivity-oriented protocol layer, the payload of the data on the new protocol layer can be parsed and processed during being delivered. The original data or the processed version of the data can be forwarded by current entity to peer entity of the new protocol layer.
  • the new protocol layer can be the overlay, and its underlay protocols are not limited.
  • DAM service module can be an internal or external plug-play component of RAN node.
  • DAM service module may be deployed between the central unit (CU) and distributed unit (DU) and connects to CN via CU.
  • DAM service module may connect to CN directly.
  • wireless DAM node between RAN node and terminal may be deployed, e.g., for efficient data collection over the air.
  • DAM session or termed as data processing session is to provide DAM service with the involvement of one or multiple of DAM PSFs. It is the association between the DAM customer (e.g., a UE, an AS) and the last involved one or multiple DAM PSFs or data networks (DNs) . For example, it can be established between a UE and a DAM PSF deployed in core network. DAM session is to execute one or multiple tasks of data collection, data preprocessing, data de-privatization, data analytics, and data delivery. In most cases, DAM session is aimed at data processing instead of solely data forwarding.
  • DAM customer e.g., a UE, an AS
  • DNs data networks
  • Establishing a DAM session needs to establish all of the resources (e.g., connectivity resource, data processing resource, and storage resource) used to complete the DAM tasks, e.g., it needs DAM C/M plane function to select and configure DAM PSFs and setup the connections between DAM PSFs.
  • resources e.g., connectivity resource, data processing resource, and storage resource
  • the data may be processed in different nodes flexibly, e.g., in CN PSF, RAN PSF and UE PSF.
  • CN PSF CN PSF
  • RAN PSF RAN PSF
  • UE PSF UE PSF
  • CN PSF CN PSF
  • a DAM session includes one or multiple DAM quality of service (QoS) flows.
  • DAM QoS flow is the finest granularity of QoS differentiation in DAM service. Traffic in the same QoS flow receives the same data forwarding treatment and data processing treatment.
  • new radio bearers e.g., XRB
  • DAM digital advanced mobile broadband
  • the new bearer provides specific quality of service (QoS) on both data forwarding and data processing.
  • QoS quality of service
  • the new bearer could allow data to be terminated at or sourced from RAN. Unicasting, multicasting and broadcasting transmission is supported by the new bearer.
  • traffic of DAM session, DAM QoS flow and DAM bearer may be mapped with each other, e.g., as per QoS requirement.
  • C/M plane of DAM may configure and control DAM PSF via interface P1, e.g., in data plane establishment procedure.
  • the selected DAM PSF may be activated or deactivated via control message from C/M plane of DAM (e.g., DAM TCF) via interface P1.
  • DAM service modules may be deployed.
  • the DAM TCF and DAM PSF of different DAM service modules cannot be connected via interface P1 directly.
  • DAM PSF connects to DAM TCF of the same DAM service module.
  • DAM PSF and DAM TCF exchange and align their capability information on DAM service via interface P1, e.g., in interface management procedure.
  • the capability information may indicate that a DAM service is supported by the PSF or TCF in specific network via specific cell for specific terminals.
  • the capability information may also indicate the PSF’s or TCF’s supported DCP rules, data de-privatization methods, data analytics models and data delivery rules. These capabilities may be configured by management plane to TCF and PSF, or may depend on the SCF the TCF and PSF being connected to.
  • the P1 interface may be successfully setup if there is overlapped capability between DAM TCF and DAM PSF, otherwise failed.
  • a DAM TCF may interact with other DAM TCFs, for example, a DAM TCF on UE side may firstly receive signaling from a TCF on RAN side over new radio bearer (e.g., XRB) dedicated for DAM service, and then the DAM TCF on UE side configures the DAM PSF on UE side.
  • new radio bearer e.g., XRB
  • the DCP rule library, data de-privatization method library, data analytics model library and data delivery rule library maintained by C/M plane of DAM can be notified to, configured to or invoked by DAM PSF via interface P1.
  • DAM TCFs Different functions in C/M plane of DAM (e.g., DAM TCFs) connect with each other via interface P2, and signaling can be exchanged via interface P2.
  • DAM TCFs Different functions in C/M plane of DAM
  • the interconnected DAM TCFs may belong to the same network domain or not. For example, all of them belong to CN or RAN; or, one or multiple of them belong to CN and the others belong to RAN; or, one or multiple of them belong to UE and the others belong to CN and RAN.
  • the interconnected DAM TCFs may belong to the same type of DAM service or not.
  • a global DC-TCF coordinates multiple domain DC-TCFs via interface P2, or a DC-TCF sends message to a DA-TCF for data analytics request via interface P2, or a global DAM TCF contacting with external entities assigns DAM tasks to specific one or multiple of the involved DC-TCF, DP-TCF, DA-TCF and DD-TCF via interface P2.
  • DAM service modules may be deployed.
  • the DAM TCFs of different DAM service modules may be also connected via interface P2.
  • a DAM TCF may notify its capability information to a peer DAM TCF via interface P2.
  • the notified capability of a DAM TCF may be the overlapped capability supported by both the DAM TCF and at least one of its connected DAM PSFs.
  • the former DAM TCF can be at RAN, and the peer DAM TCF can be at CN.
  • the former DAM TCF can be at RAN, and the peer DAM-TCF can be at UE.
  • the interface P2 is over the air, and the notification message can be a broadcast or unicast message, e.g., a radio resource control (RRC) message, a system information (SI) , or a newly defined over-the-air signaling delivered on new signaling radio bearer (e.g., XRB) dedicated for DAM service.
  • RRC radio resource control
  • SI system information
  • XRB new signaling radio bearer
  • Different DAM PSFs connect with each other via interface P3, the original data or the processed version of data can be exchanged via interface P3.
  • the interconnected DAM PSFs may belong to the same network domain or not. For example, all of them belong to CN or RAN; or, one or multiple of them belong to CN and the others belong to RAN; or, one or multiple of them belong to UE and the others belong to CN and RAN.
  • the interconnected DAM PSFs may belong to the same type of DAM service or not.
  • an anchor DC-PSF aggregates data received from other DC-PSFs via P3, or an egress DC-PSF outputs its collected and preprocessed data to an ingress DP-PSF via P3 for further data privacy protection.
  • DAM service modules may be deployed.
  • the DAM PSFs of different DAM service modules may be also connected via interface P3.
  • a DAM PSF at RAN may exchange data with another DAM PSF at CN via interface P3.
  • a DAM PSF at RAN may exchange data with another DAM PSF at UE via interface P3.
  • the interface P3 is over the air, and the data can be delivered via a new radio bearer (e.g., XRB) dedicated for DAM service.
  • XRB new radio bearer
  • boarder DAM TCF On C/M plane, there is interface between boarder DAM TCF and external entity (e.g., C/M-TW-GW, MCF, data source, data customer) .
  • external entity e.g., C/M-TW-GW, MCF, data source, data customer
  • boarder DAM PSF On data plane, there is interface between boarder DAM PSF and external entity (e.g., Data-TW-GW, data source, data customer) .
  • external entity e.g., Data-TW-GW, data source, data customer
  • DAM function there may be interface between DAM function and other network entities.
  • DAM PSF and TCF have interfaces connected to CU and DU.
  • DAM provides DCaaS, DPaaS, DAaaS and DDaaS service. These services can be provided by different service providers. These service providers could form a trustworthy DAM consortium. DAM consortium could be supported by a DAM-chain (e.g., blockchain) to be trustworthy. DAM actions on data operation (e.g., data collection, data preprocessing, data de-privatization and data analytics) could be recorded on DAM-chain to be auditable and traceable. Data information (e.g., metadata, data representation, data feature, and Hash value of data) and data correlation information can be recorded on DAM-chain for data discovery and data retrieval.
  • DAM-chain e.g., blockchain
  • DCaaS, DPaaS, DAaaS and DDaaS can provide services cooperatively or independently to customer.
  • FIG. 6 shows a simplified schematic illustration of possible ways for data sharing between service A function and service B functions according to one or more example implementations of the present disclosure.
  • service A function e.g., NWDAF on C/M plane
  • service B functions e.g., DAM-TCF on C/M plane, DAM-PSF on data plane
  • service A function includes one or more of: NWDAF, DCCF, MAFA, LMF, ADRF, and MDAF.
  • Service A function is deployed on C/M plane.
  • Service B functions include functions on C/M plane and functions on data plane.
  • the service A function can support service A and the service B functions can support service B.
  • service A focus on the data managed by the logical functions (e.g., NWDAF, DCCF, MFAF, and LMF) , e.g., network data.
  • service B focus on new types of data, e.g., 3GPP-based sensing data, non-3GPP-based sensing data, AI related data, and sensor data.
  • the network data for example may occur.
  • the sensing data for example may occur.
  • NWDAF As an example of Service A function
  • DAM-PSF and DAM-TCF as an example of Service B functions
  • NWDAF and DAM-PSF need to share data to each other, e.g., for data fusion.
  • NWDAF need send its collected data such as data collected on the C/M plane (e.g., network data) to DAM-PSF.
  • DAM-PSF perform data fusion of its collected data such as data collected on the data plane (e.g., sensing data) and the data sent by NWDAF.
  • the following methods also applied to the cases where DAM-PSF need send its collected data to NWDAF and NWDAF performs data fusion.
  • NWDAF sends its collected data (e.g., network data) to DAM-PSF via DAM-TCF (as the first way illustrated in FIG. 6: NWDAF ⁇ DAM-TCF ⁇ DAM-PSF) .
  • NWDAF sends it collected data to DAM-TCF on C/M plane, then DAM-TCF sends the data to DAM-PSF via the interface between DAM-TCF and DAM-PSF.
  • NWDAF sends its collected data to DAM-PSF directly via a direct interface between them (as the second way illustrated in FIG. 6: NWDAF ⁇ DAM-PSF) .
  • NWDAF sends its collected data to DAM-PSF via data plane function (e.g., UPF+) on data plane, e.g., via the interface (e.g., SBI-based interface) between NWDAF and data plane function (e.g., UPF+) .
  • NWDAF first sends its collected data to the data plane function, then the data plane function sends the data to DAM-PSF (as the third way illustrated in FIG. 6: NWDAF ⁇ UPF+ ⁇ DAM-PSF) .
  • the data (e.g., network data) to be collected is discovered by NWDAF and sent to DAM-PSF directly without being collected by NWDAF, and the DAM-PSF itself also collects data (e.g., sensing data) targeted by DAM service (as the fourth way illustrated in FIG. 6) .
  • NWDAF performs data discovery of the data to be collected, and notifies the discovery result (e.g., data source location) to DAM-PSF.
  • DAM-PSF collects the data directly from the data source without collecting by NWDAF.
  • NWDAF can notify the discovery result to DAM-PSF via direct interface between NWDAF and DAM-PSF or via other functions (e.g., DAM-TCF) .
  • NWDAF triggers to setup the connection between DAM-PSF and data source (e.g., NFs, RAN nodes, UE) to collect data from the data source. If necessary, NWDAF may interact with other functions (e.g., DAM-TCF, SCP+) to setup the connection.
  • data source e.g., NFs, RAN nodes, UE
  • NWDAF may interact with other functions (e.g., DAM-TCF, SCP+) to setup the connection.
  • FIG. 7 shows a schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • the method can be implemented by a first network function such as a PSF in a network.
  • the first network function could be other device that has similar function (for example, the first network function can be a computer machine in a data center, a communication module, a chip, or any network element in the network) , which is not limited herein.
  • the first network function could also be a whole device.
  • the method may include the following steps.
  • S710 obtaining a request from a second network function, where the request is for configuring the first network function to perform data processing on first data on a C/M plane and second data on a data plane.
  • the first network function can be a DAM-PSF
  • the second network function can be a DAM-TCF
  • the first network function can be deployed on the data plane
  • the second network function can be deployed on the C/M plane.
  • the C/M plane represents control or management plane herein.
  • the C/M plane is defined as a plane which includes a collection of control or management functions and is used for implementing protocol processing, route calculation, forwarding control, system management, control and configuration on data plane functions, interaction or configuration between C/M plane functions, and system security.
  • the C/M plane includes NWDAF and DAM-TCF.
  • the first network function being a PSF on the data plane and the second network function being a TCF on the C/M plane as an example.
  • the first network function such as the PSF can obtain the request from the second network function such as the TCF, where the request is for configuring the PSF to perform data processing on first data on the C/M plane and second data on the data plane.
  • the PSF has not collected the first data and the second data.
  • the configuration procedure is performed before the data sharing and data fusion procedure.
  • the TCF can also configure or interact with the NWDAF for enabling the NWDAF to process the data such as collect data, analyze data, and so on.Once the PSF is initiated to collect data and perform data processing, it can obtain different types of data and perform the data processing on the different types of data based on the configuration from the TCF.
  • FIG. 8 shows a first procedure for data sharing and processing between service A function and service B functions according to one or more example implementations of the present disclosure.
  • FIG. 10 shows a second procedure for data sharing and processing between service A function and service B functions according to one or more example implementations of the present disclosure.
  • a TCF triggers to setup the connection between the PSF and data source (e.g., via UPF, RAN nodes, UE) to collect data from the data source.
  • the PSF on the data plane itself can collect the second data on the data plane from the data source.
  • the second data on the data plane can be sensing data from data plane.
  • the data source from which the PSF collects data on data plane is illustrated as UE.
  • UE the data source from which the PSF collects data on data plane
  • RAN node e.g., providing sensing data
  • CN NF e.g., providing AI related data
  • terminal e.g., camera, sensor, and actuator
  • the NWDAF on the C/M plane can collect the first data on the C/M plane from the data source and then send it to the PSF on the data plane via interface (s) so that the PSF on the data plane can collect the first data on the C/M plane.
  • the first data on the C/M plane can be network data from C/M plane.
  • the data source from which the service B function (e.g., PSF) collects data via data plane can be the same or different from the data source from which the service A function (e.g., NWDAF) collects data via C/M plane.
  • the PSF can know that it is requested to perform data processing, such as data fusion, on the first data on the C/M plane and the second data on the data plane, and can obtain one or more parameters from the request which may be required for performing the data processing. Then, by performing the data processing on the first data and the second data based on one or more parameters in the request, the PSF can process different types of data.
  • data processing such as data fusion
  • different types of data including first data on the C/M plane and second data on the data plane can be collected and processed at the PSF side, and thus, data sharing and data processing can be performed within the network on C/M plane and data plane for different types of data, e.g., network data from control plane and new types of data, such as sensing data and AI related data, from data plane.
  • data processing can be but not limited to AI training, AI inference, data pre-processing, data de-privatization (data privacy protection) , data cleaning, data collection, data analytics, processing on sensing/ISAC data (e.g., sensing data compression, sensing data analytics, sensing data pre-processing, sensing data fusion) , data sanitization, data management, data normalization, useless data filtering, data feature engineering, data compression, data embedding, data representation learning, and data feature extraction.
  • AI training AI inference
  • data pre-processing data de-privatization
  • data cleaning data collection
  • data analytics processing on sensing/ISAC data
  • sensing/ISAC data e.g., sensing data compression, sensing data analytics, sensing data pre-processing, sensing data fusion
  • data sanitization e.g., data management, data normalization, useless data filtering, data feature engineering, data compression, data embedding, data representation learning, and data feature extraction.
  • the first network function can perform data fusion on the first data and the second data.
  • the data packets from different service functions can be aggregated together for data fusion, for example, the first data on the C/M plane collected from the NWDAF and the second data on the data plane collected from the PSF itself can be aggregated together for data fusion. And thus, data sharing and fusion between different service functions can be achieved.
  • the data packet can include header and payload, where the payload contains the data to be sent, while the data packet used herein can be interchanged with data in the present disclosure, which is not limited herein.
  • the one or more parameters include at least one of following parameters: association information, a data plane tunnel ID, a session ID, a fusion indication, data source information, or an address of the first network function.
  • the association information is for marking the data packets.
  • the data plane tunnel ID is for identifying a data plane tunnel which delivers data
  • the session ID is for identifying a session
  • the session is for representing a connection (e.g., C/M plane connection, C/M plane session) between the TCF and the PSF
  • the fusion indication is for indicating that data fusion is needed for data from C/M plane and data from data plane
  • the data source information is for indicating where or when the second data is obtained
  • the address of the first network function is for indicating where the first network function is located.
  • the PSF can know the parameter (s) used for marking data packets. Different data packets marked with a same parameter or a same set of parameters are correlated, and may be, for example, aggregated together for data fusion. After receiving data packets, the PSF can clearly know, based on the one or more parameters of these data packets, whether they are correlated with each other such as whether one data packet should be aggregated with another data packet for data fusion.
  • the PSF can know that the data fusion is required for different types of data such as first data on the C/M plane and second data on the data plane. Furthermore, by including the data source information, because the data source information can indicate where or when the second data is obtained, the PSF can know which source the different types of data are originated from. In this way, by including one or more parameters in the request, various application scenarios can be accommodated.
  • the association information can be an association ID (AID) , a set of parameters, or an input of a calculation function.
  • AID can be understood as an ID used for associating data packets, and the data packet marked with the same AID should be processed together.
  • the set of parameters can be understood as a series of parameters such as QoS parameters including data quality level, data compression ratio, processing latency/priority, etc., and the data packet marked with the same set of parameters should be processed together.
  • the calculation function can be understood as a mathematical function. In some cases, different input values of a calculation function may lead to a same output results of the calculation function. Then the different data packets should also be processed together.
  • the first network function will directly identify which data packets should be processed together based on the AID under the scenarios with high time delay.
  • the set of parameters can be used for the first network function to indirectly identify which data packets should be processed together under the scenarios with time delay and privacy requirements.
  • the input of the calculation function can be used for the first network function to indirectly identify which data packets should be processed together under the scenarios with high privacy requirements.
  • the data plane tunnel ID can be a GTP-U TEID identifying a GTP-U tunnel, a QUIC connection ID identifying a QUIC connection, or a MoQ session ID identifying a MoQ session.
  • the MoQ session ID is a URI.
  • the session ID can be for identifying a connection, and the connection can be based on one or more protocols of: a HTTP, a TCP, a UDP, a QUIC, a MoQ, or a PFCP.
  • the data plane tunnel ID because the data plane tunnel ID can be different types of ID, the data plane tunnel can be identified according to actual needs, thereby various application scenarios can be accommodated.
  • the QUIC ID can adapt to the application scenarios requiring higher safety and/or higher speed.
  • the TEID can adapt to the application scenarios requiring higher flexibility.
  • the MoQ Session ID can adapt to the application scenarios requiring high scale and low latency data distribution.
  • the session ID can be used for identifying connections based on various protocols (e.g., HTTP-based connection, TCP-based connection, UDP-based connection, QUIC-based connection, MoQ-based connection, PFCP-based connection) , flexibility and diversity of data delivery may be improved.
  • the session ID can be for identifying a connection, and the connection can be based on one or more protocols of:a HTTP, a TCP, a UDP, a QUIC, a MoQ, or a PFCP.
  • the first network function may send a response to the second network function, where the response is for acknowledging a receipt of the request.
  • the response may be for indicating whether the request is accepted or rejected. If accepting the request, the first network function may send the response including an indication for indicating the request is accepted to the second network function. In this case, the response may carry the indication or the response itself can be the indication, such as a field of the response can represent the indication. If rejecting the request, the first network function may send the response including a rejection for indicating the request is rejected to the second network function.
  • the first network function rejects the request from the second network function, such as in busy, and so on, which is not limited herein.
  • the response may carry the rejection or the response itself can be the rejection, such as a field of the response can represent the rejection.
  • FIG. 8 shows a first procedure for data sharing and processing between service A function and service B functions according to one or more example implementations of the present disclosure.
  • FIG. 10 shows a second procedure for data sharing and processing between service A function and service B functions according to one or more example implementations of the present disclosure.
  • the first procedure described in FIG. 8 and FIG. 9 is illustrated, where FIG. 9 shows call flows for data sharing between service A function and service B functions via indirect interface on C/M plane according to one or more example implementations of the present disclosure.
  • the first data can be obtained from the third network function via the second network function through a first interface between the second network function and the third network function and a second interface between the first network function and the second network function.
  • the first network function can be a DAM-PSF
  • the second network function can be a DAM-TCF.
  • the third network function can include one or more of: a NWDAF, a DCCF, a MFAF, a LMF, an ADRF, and a MDAF.
  • the third network function can be deployed on the C/M plane.
  • FIG. 8 and FIG. 10 take the third network function being NWDAF as an example.
  • a connection identified by the session ID is established on the second interface, which can be treated as the solid line between the TCF and the PSF.
  • the connection can be based on one or more protocols of: a HTTP, a TCP, a UDP, a QUIC, a MoQ, or a PFCP.
  • the NWDAF can obtain the first data on C/M plane from the second data source and then send the first data to the TCF through the first interface, which can be treated as the solid line between the NWDAF and the TCF.
  • the TCF After obtaining the first data, the TCF will forward the first data to the PSF through the second interface, where the connection identified by the session ID can be established. In this way, because the first data can be obtained from the NWDAF via the TCF through the first interface and the second interface, the TCF can participate in collecting the second data on the C/M plane from the second data source, thereby facilitating the data management and distribution in a centralized way.
  • the first network function can obtain the second data from a first data source, and obtain the first data from a second data source.
  • the PSF can obtain the first data from the second data source via the NWDAF and TCF, which represents that the TCF can participate in collecting the second data on the C/M plane from the data source.
  • the second data can be obtained from a fourth network function such as UPF via a fourth interface.
  • the fourth network function can be deployed on the data plane and the fourth network function can be a data-TW-GW or a UPF, FIG. 8 and FIG. 10 take the fourth network function being UPF as an example.
  • the PSF can obtain the second data from the first data source via the UPF directly.
  • the request can include the association information, and when the first data is marked with same association information as the second data, the first data is correlated with the second data.
  • the first network function can perform data fusion on the first data marked with the association information and the second data marked with the same association information.
  • different data packets can be marked with the same association to indicate the correlation between them. For example, whether different data packets can be aggregated together for data fusion can be determined based on the association information of the different data packets.
  • the first network function can easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network.
  • the request can include association information (AInf) , and the AInf is for indicating that the first data marked with the AInf and sent to PSF on C/M plane by NWDAF is correlated (e.g., to be aggregated together for data fusion) with the second data collected by PSF itself on data plane marked with the same AInf.
  • the association information can be an AID, a set of parameters, or an input of a calculation function.
  • different data packets marked with the same AID can be aggregated together for data fusion.
  • Different data packets marked with the same set of parameters can be aggregated together for data fusion.
  • different data packets may be marked with a same input value of a calculation function, then the different data packets can be aggregated together for data fusion.
  • the request can include the association information and the data plane tunnel ID, and the first data marked with the association information is correlated with the second data delivered on a data plane tunnel identified by the data plane tunnel ID.
  • the first network function can perform data fusion on the first data marked with the association information and the second data delivered on the data plane tunnel identified by the data plane tunnel ID.
  • the association information and the data plane tunnel ID can be used together for identifying correlated data packets which can be processed together, for example, identifying different data packets that can be aggregated together for data fusion.
  • the first network function can easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets transferred via a data plane tunnel with association information, or encapsulate the association information in a data packet transferred via the data plane tunnel.
  • the request can include AInf and the data plane tunnel ID (e.g., GTP-U tunnel ID, QUIC connection ID, MoQ Session ID)
  • the AInf and the data plane tunnel ID are for indicating that the first data marked with the AInf is correlated with the second data delivered on a data plane tunnel identified by the data plane tunnel ID.
  • they are for indicating that the first data sent to PSF on C/M plane by NWDAF which is marked with the AInf is correlated with the second data collected by PSF itself via a data plane tunnel identified by the data plane tunnel ID.
  • the request can include the association information and the session ID, and the first data delivered on a connection corresponding to the session ID is correlated with the second data marked with the association information.
  • the first network function can perform data fusion on the first data delivered on the connection corresponding to the session ID and the second data marked with the association information.
  • the association information and the session ID can be used together for identifying correlated data packets that can be processed together, for example, for identifying data packets that can be aggregated together for data fusion.
  • the first network function can easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets transferred via a C/M plane connection with association information, or encapsulate the association information in a data packet transferred via the C/M plane connection.
  • the request can include the session ID (e.g., C/M plane session ID) and the AInf
  • the session ID and the AInf are for indicating that the first data delivered on the connection (e.g., C/M plane connection between the TCF and the PSF) is correlated with the second data marked with the AInf.
  • the connection e.g., C/M plane connection between the TCF and the PSF
  • they are for indicating that the first data sent to the DAM-PSF via the connection (e.g., C/M plane connection) is correlated with the second data marked with the AInf and collected by the DAM-PSF itself via the data plane.
  • the request can include the session ID and the data plane tunnel ID, and the first data delivered on a connection corresponding to the session ID is correlated with the second data delivered on a data plane tunnel identified by the data plane tunnel ID.
  • the first network function can perform data fusion on the first data delivered on the connection corresponding to the session ID and the second data delivered on the data plane tunnel identified by the data plane tunnel ID.
  • the session ID and the data plane tunnel ID can be used together for identifying correlated data packets that can be processed together, for example, for identifying data packets that can be aggregated together for data fusion.
  • the first network function can easily and clearly know which data packets should be processed together such as which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network, and being especially suitable for an application scenarios that it is inconvenient to mark the data packets with the association information.
  • the request can include the session ID (e.g., C/M plane session ID) and the data plane tunnel ID (e.g., GTP-U tunnel ID, QUIC connection ID, MoQ session ID) , and the session ID and the data plane tunnel ID are for indicating that the first data delivered on the connection (e.g., C/M plane connection) is correlated with the second data delivered on the data plane tunnel. For example, they are for indicating that the first data sent to the DAM-PSF via the connection (e.g., C/M plane connection) is correlated with the second data collected by the DAM-PSF itself via the data plane tunnel are correlated.
  • the session ID e.g., C/M plane session ID
  • the data plane tunnel ID e.g., GTP-U tunnel ID, QUIC connection ID, MoQ session ID
  • the request can include a fusion indication, and the fusion indication is for indicating that data fusion is to be performed on the first data and the second data.
  • the request can include the fusion indication, and thus, based on the fusion indication, the first network function can clearly know that it is instructed to aggregate the obtained first data on the C/M plane and second data on the data plane together, thereby facilitating implementation of the data fusion on data plane.
  • the request can include an indication, and the indication is for indicating that data fusion is needed for the first data from C/M plane and the second data from data plane.
  • the request can include data source information, and the data source information is for indicating one or more of: an area in which the second data is to be collected, or a time when the second data is to be collected.
  • the PSF can obtain the request from the TCF for configuring the PSF to perform the data processing such as data fusion on the first data and the second data.
  • the request can include data source information, the first network function can clearly know where the second data is to be collected or when the second data is to be collected, thereby the first network function can obtain the second data on the data plane correctly.
  • the request can include data source information, e.g., area, time, etc., and the data source information is for indicating that the second data (e.g., sensing data) should be collected by DAM-PSF in specific area and/or specific time.
  • data source information e.g., area, time, etc.
  • the data source information is for indicating that the second data (e.g., sensing data) should be collected by DAM-PSF in specific area and/or specific time.
  • the data plane tunnel identified by the data plane tunnel ID can be established on a fourth interface between the first network function and the fourth network function.
  • the first network function can easily know which data plane tunnel is established on the fourth interface, thereby facilitating implementation of the data fusion on data plane.
  • FIG. 8 and FIG. 9 will be described as follows in details.
  • NWDAF as an example of Service A function
  • DAM service as an example of Service B
  • NWDAF and DAM-PSF need to share data to each other, e.g., for data fusion.
  • NWDAF need send its collected data (e.g., network data) to DAM-PSF.
  • DAM-PSF perform data fusion of its collected data (e.g., sensing data) and the data sent by NWDAF.
  • the following methods also applied to the cases where DAM-PSF need send its collected data to NWDAF and NWDAF performs data fusion.
  • DAM-PSF collects data (e.g., sensing data) via data plane, e.g., UE, RAN, and UPF+ are involved in the data collection on data plane.
  • NWDAF sends its collected data (e.g., network data) to DAM-PSF via DAM-TCF (as the first way illustrated in FIG. 6: NWDAF ⁇ DAM-TCF ⁇ DAM-PSF) .
  • the call flows are illustrated.
  • DAM-TCF send a message (the request in S710) to DAM-PSF for data collection request.
  • the message includes an AInf.
  • the Association information can be: an Association ID (AID) , a set of parameters, or an input of a calculation function.
  • Different data packets marked with the same AID are correlated (e.g., to be aggregated together for data fusion) .
  • Different data packets marked with the same set of parameters are correlated (e.g., to be aggregated together for data fusion) .
  • Different data packets may be marked with a same input value of a calculation function, Then the different data packets are correlated (e.g., to be aggregated together for data fusion) . In some cases, different input values of a calculation function but the different input values lead to a same output results of the calculation function. Then the different data packets are also correlated (e.g., to be aggregated together for data fusion) .
  • the AInf indicates that the data marked with the AInf and sent to DAM-PSF on C/M plane by NWDAF are correlated (e.g., to be aggregated together for data fusion) with the data collected by DAM-PSF itself on data plane marked with the same AInf (e.g., the same AID, the same set of parameters, or the input value (s) resulting in the same output value of a calculation function) .
  • the AInf may be encapsulated in C/M plane packet (e.g., in HTTP packet header or payload) .
  • the AInf may be encapsulated in data plane packet (e.g., in GTP-U/PSF packet header) .
  • the message includes an AInf and a data plane tunnel ID (e.g., GTP-U tunnel ID, QUIC connection ID, MoQ Session ID) , they are used to indicate that the data marked with the AInf are correlated with the data delivered on a data plane tunnel identified by the data plane tunnel ID. For example, they are used to indicate that the data sent to DAM-PSF on C/M plane by NWDAF which is marked with the AInf are correlated with the data collected by DAM-PSF itself via a data plane tunnel identified by the data plane tunnel ID.
  • the data plane tunnel could be a tunnel between DAM-PSF and Data-TW-GW or UPF+.
  • the message includes a session ID (e.g., C/M plane session ID) and an AInf.
  • the session ID identifies a connection (e.g., C/M plane connection, C/M plane session) between the DAM-TCF and the DAM-PSF.
  • the connection can be based on one or more protocols of: a HTTP, a TCP, a UDP, a QUIC, a MoQ, or a PFCP.
  • the AInf is to be encapsulated in the data to be collected by the DAM-PSF itself via data plane.
  • the session ID and the AInf indicate that the data delivered via the connection (e.g., C/M plane connection) and the data marked with the AInf are correlated.
  • the session ID and the AInf indicate that the data sent to the DAM-PSF via the connection (e.g., C/M plane connection) and the data marked with the AInf and collected by the DAM-PSF itself via the data plane are correlated.
  • the message includes a session ID (e.g., C/M plane session ID) and a data plane tunnel ID (e.g., GTP-U tunnel ID, QUIC connection ID, MoQ session ID) .
  • the session ID identifies a connection (e.g., C/M plane connection) between the DAM-TCF and the DAM-PSF.
  • the data plane tunnel ID identifies a data plane tunnel between the DAM-PSF and a data plane function (e.g., Data-TW-GW or UPF+) .
  • the session ID and the data plane tunnel ID indicate that the data delivered via the connection (e.g., C/M plane connection) and the data delivered via the data plane tunnel are correlated.
  • the session ID and the data plane tunnel ID indicate that the data sent to the DAM-PSF via the connection (e.g., C/M plane connection) and the data collected by the DAM-PSF itself via the data plane tunnel are correlated.
  • the message includes an Indication
  • the indication indicates that data fusion is needed for data from C/M plane and data from data plane.
  • the message includes data source information, e.g., area, time, etc. It indicates that data (e.g., sensing data) should be collected by DAM-PSF in specific area and/or specific time.
  • DAM-PSF sends DAM-TCF a data collection response message.
  • DAM-TCF send a message to NWDAF for data collection request.
  • the message includes an AInf.
  • the Association information can be: an AID, a set of parameters, or an input of a calculation function.
  • the AInf is to be marked together with the data to be sent to DAM-PSF via DAM-TCF on C/M plane.
  • the AInf is encapsulated in HTTP header or payload.
  • the message includes an Indication on whether data should be collected by NWDAF, or only data source discovery without collecting data is need. In this case, the Indication indicates that data should be collected by NWDAF.
  • the message includes an Indication on whether data analytics is needed by NWDAF, e.g., with AI methods, and AI result is output to DAM-PSF for data fusion, instead of the raw data collected by NWDAF.
  • the message includes data source information, e.g., area, time, etc. It indicates that data (e.g., network data) should be collected by NWDAF in specific area and/or specific time.
  • NWDAF performs data discovery based on the data source information for the following up data collection.
  • DAM-PSF collects data (e.g., sensing data) from data source 1.
  • an AInf if an AInf is configured in Step 9001, the data will be marked with the AInf.
  • the AInf will be encapsulated in the data by data source or other intermediate functions (e.g., RAN node, Data-TW-GW, or UPF+) .
  • the data will be collected by DAM-PSF via its dedicated data plane tunnel (e.g., transport network layer (TNL) tunnel (e.g., GTP-U tunnel, QUIC connection, MoQ session) identified by the tunnel ID included in the message of Step 9001.
  • dedicated data plane tunnel e.g., transport network layer (TNL) tunnel (e.g., GTP-U tunnel, QUIC connection, MoQ session) identified by the tunnel ID included in the message of Step 9001.
  • TNL transport network layer
  • NWDAF collects data (e.g., network data) from data source 2, then sends the collected data to the DAM-TCF, then the DAM-TCF sends the data to the DAM-PSF.
  • data e.g., network data
  • Step 9003 if the message in Step 9003 includes an Indication on that data should be collected by NWDAF, the NWDAF collects data from data source 2.
  • Step 9003 the data sent to DAM-TCF by NWDAF will be marked with the AInf.
  • the AInf will be encapsulated in the data by data source or NWDAF.
  • the data will be sent to DAM-PSF by DAM-TCF via dedicated connection (e.g., HTTP-based connection, HTTP-based C/M plane session. TCP-based connection, QUIC-based connection, MoQ-based connection, PFCP-based connection) identified by the session ID included in the message of Step 9001.
  • dedicated connection e.g., HTTP-based connection, HTTP-based C/M plane session.
  • the data will be sent to DAM-PSF by DAM-TCF via dedicated C/M plane connection (e.g., HTTP-based connection, HTTP-based C/M plane session. TCP-based connection, QUIC-based connection, MoQ-based connection, PFCP-based connection) identified by the C/M plane session ID included in the message of Step 9001.
  • C/M plane connection e.g., HTTP-based connection, HTTP-based C/M plane session.
  • the NWDAF collects raw data from data source, performs analytics on the raw data and then sends the analytics results (e.g., AI result) to DAM-PSF via DAM-TCF.
  • the NWDAF collects raw data from data source, performs analytics on the raw data and then sends the analytics results (e.g., AI result) to DAM-PSF via DAM-TCF.
  • the data sent by NWDAF to the DAM-TCF is not marked with the AInf, and the DAM-TCF marks the data sent to the DAM-PSF via the connection (e.g., C/M plane connection) between the DAM-TCF and DAM-PSF with the AInf.
  • the connection e.g., C/M plane connection
  • DAM-PSF performs data processing (e.g., data fusion) .
  • DAM-PSF performs data fusion, e.g., to aggregate data sent by NWDAF on C/M plane via DAM-TCF and data collected by itself on data plane.
  • DAM-PSF performs data fusion, e.g., to aggregate data sent by NWDAF on C/M plane via DAM-TCF and data collected by itself on data plane marked with the same AInf.
  • DAM-PSF performs data fusion, e.g., to aggregate data sent by NWDAF on C/M plane marked with the AInf included in the message of Step 9001 and data collected by itself via a dedicated data plane tunnel identified by the tunnel ID included in the message of Step 9001.
  • DAM-PSF performs data fusion, e.g., to aggregate data sent by NWDAF on C/M plane via a dedicated C/M plane connection (e.g., C/M plane session) identified by the C/M plane session ID included in the message of Step 9001 and data collected by itself on data plane marked with the AInf included in the message of Step 9001.
  • a dedicated C/M plane connection e.g., C/M plane session
  • DAM-PSF performs data fusion, e.g., to aggregate data sent by NWDAF on C/M plane via a dedicated C/M plane connection (e.g., C/M plane session) identified by the C/M plane session ID included in the message of Step 9001 and data collected by itself via a dedicated data plane tunnel identified by the tunnel ID included in the message of Step 9001.
  • a dedicated C/M plane connection e.g., C/M plane session
  • FIG. 11 shows another call flow for data sharing between service A function and service B functions via direct interface according to one or more example implementations of the present disclosure.
  • the first data can be obtained from a third network function through a third interface between the first network function and the third network function.
  • the third interface can be seen as the solid line between the NWDAF and the PSF in FIG. 10, or the third interface can be seen as the dashed line between the NWDAF and the PSF in FIG. 10 and Figure 8. Similar with the procedure described in FIG. 8 and FIG.
  • the NWDAF can obtain the first data on C/M plane from the second data source and then send the first data to the PSF through the third interface, thus it is not required for the TCF to participate in collecting the first data on the C/M plane from the second data source, thereby reducing the processing overhead of the TCF.
  • a connection identified by the session ID can be established on the third interface.
  • the connection can be based on one or more protocols of: a HTTP, a TCP, a UDP, a QUIC, a MoQ, or a PFCP.
  • the first network function can obtain the second data from a first data source, and obtain the first data from a second data source.
  • the PSF can obtain the first data from the second data source directly, which represents that it is not required for the TCF to participate in collecting the first data on the C/M plane from the second data source.
  • the second data can be obtained from a fourth network function such as UPF via a fourth interface.
  • the third network function can be deployed on the C/M plane, and in some possible implementations, the fourth network function can be deployed on the data plane.
  • the PSF can obtain the second data from the first data source via the UPF directly.
  • the association information included in the request can be seen from implementation of the first procedure, which is not repeated herein.
  • the request can include the AInf
  • the AInf is for indicating that the first data marked with the AInf and sent to PSF by NWDAF directly is correlated (e.g., to be aggregated together for data fusion) with the second data collected by PSF itself on data plane marked with the same AInf.
  • the association information can be an AID, a set of parameters, or an input of a calculation function.
  • different data packets marked with the same AID can be aggregated together for data fusion.
  • Different data packets marked with the same set of parameters can be aggregated together for data fusion.
  • different data packets may be marked with a same input value of a calculation function, then the different data packets can be aggregated together for data fusion.
  • the request can include AInf and the data plane tunnel ID (e.g., GTP-U tunnel ID, QUIC connection ID, MoQ Session ID) , and the AInf and the data plane tunnel ID are for indicating that the first data marked with the AInf is correlated with the second data delivered on a data plane tunnel identified by the data plane tunnel ID. For example, they are for indicating that the first data sent to PSF by NWDAF directly which is marked with the AInf is correlated with the second data collected by PSF itself via a data plane tunnel identified by the data plane tunnel ID.
  • the data plane tunnel ID e.g., GTP-U tunnel ID, QUIC connection ID, MoQ Session ID
  • the request can include the session ID (e.g., C/M plane session ID) and the AInf, and the session ID and the AInf are for indicating that the first data delivered on the connection is correlated with the second data marked with the AInf. For example, they are for indicating that the first data sent to the DAM-PSF via the connection is correlated with the second data marked with the AInf and collected by the DAM-PSF itself via the data plane.
  • the session ID e.g., C/M plane session ID
  • the session ID and the AInf are for indicating that the first data delivered on the connection is correlated with the second data marked with the AInf.
  • they are for indicating that the first data sent to the DAM-PSF via the connection is correlated with the second data marked with the AInf and collected by the DAM-PSF itself via the data plane.
  • the request can include the session ID (e.g., C/M plane session ID) and the data plane tunnel ID (e.g., GTP-U tunnel ID, QUIC connection ID, MoQ Session ID) , and the session ID and the data plane tunnel ID are for indicating that the first data delivered on the connection is correlated with the second data delivered on the data plane tunnel. For example, they are for indicating that the first data sent to the DAM-PSF via the connection is correlated with the second data collected by the DAM-PSF itself via the data plane tunnel are correlated.
  • the session ID e.g., C/M plane session ID
  • the data plane tunnel ID e.g., GTP-U tunnel ID, QUIC connection ID, MoQ Session ID
  • the fusion indication and/or the data source information included in the request can be seen from implementation of the first procedure, which is not repeated herein.
  • the data plane tunnel identified by the data plane tunnel ID can be established on a fourth interface between the first network function and the fourth network function.
  • FIG. 10 and FIG. 11 will be described as follows in details.
  • NWDAF as an example of Service A function
  • DAM service as an example of Service B
  • NWDAF and DAM-PSF need to share data to each other, e.g., for data fusion.
  • NWDAF need send its collected data (e.g., network data) to DAM-PSF.
  • DAM-PSF perform data fusion of its collected data (e.g., sensing data) and the data sent by NWDAF.
  • the following methods also applied to the cases where DAM-PSF need send its collected data to NWDAF and NWDAF performs data fusion.
  • DAM-PSF collects data (e.g., sensing data) via data plane, e.g., UE, RAN, and UPF+ are involved in the data collection on data plane.
  • NWDAF sends its collected data (e.g., network data) to DAM-PSF directly via a direct interface between them (as the second way illustrated in FIG. 6: NWDAF ⁇ DAM-PSF) .
  • NWDAF ⁇ DAM-PSF the call flows are illustrated.
  • DAM-TCF send a message (the request in S710) to DAM-PSF for data collection request.
  • the message includes an Association information (AInf) .
  • the Association information can be: an AID, a set of parameters, or an input of a calculation function.
  • Different data packets marked with the same AID are correlated (e.g., to be aggregated together for data fusion) .
  • Different data packets marked with the same set of parameters are correlated (e.g., to be aggregated together for data fusion) .
  • Different data packets may be marked with a same input value of a calculation function, Then the different data packets are correlated (e.g., to be aggregated together for data fusion) . In some cases, different input values of a calculation function but the different input values lead to a same output results of the calculation function. Then the different data packets are also correlated (e.g., to be aggregated together for data fusion) .
  • the AInf indicates that the data marked with the AInf and sent to DAM-PSF by NWDAF directly are correlated (e.g., to be aggregated together for data fusion) with the data collected by DAM-PSF itself on data plane marked with the same AInf (e.g., the same AID, the same set of parameters, or the input value (s) resulting in the same output value of a calculation function) .
  • the AInf may be encapsulated in packet (e.g., in HTTP packet header or payload) .
  • the AInf may be encapsulated in data plane packet (e.g., in GTP-U/PSF packet header) .
  • the message includes an Association information (AInf) and a data plane tunnel ID (e.g., GTP-U tunnel ID, QUIC connection ID, MoQ Session ID) , they are used to indicate that the data marked with the AInf are correlated with the data delivered via a data plane tunnel identified by the data plane tunnel ID. For example, they are used to indicate that the data sent to DAM-PSF by NWDAF directly which is marked with the AInf are correlated with the data collected by DAM-PSF itself via a data plane tunnel identified by the data plane tunnel ID.
  • the data plane tunnel could be a tunnel between DAM-PSF and Data-TW-GW/UPF+.
  • the message includes a session ID (e.g., a C/M plane session ID) and an AInf.
  • the session ID identifies a connection between the NWDAF and the DAM-PSF.
  • the connection can be based on one or more protocols of: a HTTP, a TCP, a UDP, a QUIC, a MoQ, or a PFCP.
  • the AInf is to be encapsulated in the data to be collected by the DAM-PSF itself via data plane.
  • the session ID and AInf indicate that the data delivered via the connection and the data marked with the AInf are correlated. For example, the session ID and AInf indicate that the data sent to the DAM-PSF via the connection and the data marked with the AInf and collected by the DAM-PSF itself via the data plane are correlated.
  • the message includes a session ID (e.g., a C/M plane session ID) and a data plane tunnel ID (e.g., GTP-U tunnel ID, QUIC connection ID, MoQ Session ID) .
  • the session ID identifies a connection between the NWDAF and the DAM-PSF.
  • the data plane tunnel ID identifies a data plane tunnel between the DAM-PSF and a data plane function (e.g., Data-TW-GW/UPF+) .
  • the session ID and the data plane tunnel ID indicate that the data delivered via the connection and the data delivered via the data plane tunnel are correlated.
  • the session ID and the data plane tunnel ID indicate that the data sent to the DAM-PSF via the connection and the data collected by the DAM-PSF itself via the data plane tunnel are correlated.
  • the message includes an Indication
  • the Indication indicates that data fusion is needed for data from C/M plane and data from data plane.
  • the message includes data source information, e.g., area, time, etc. It indicates that data (e.g., sensing data) should be collected by DAM-PSF in specific area and/or specific time.
  • DAM-PSF sends DAM-TCF a data collection response message.
  • DAM-TCF send a message to NWDAF for data collection request.
  • the message includes an Association information (AInf) .
  • the Association information can be: an AID, a set of parameters, or an input of a calculation function.
  • the AInf is to be marked together with the data to be sent to DAM-PSF directly.
  • the AInf is encapsulated in HTTP header or payload.
  • the message includes an Indication on whether data should be collected by NWDAF, or only data source discovery without collecting data is need. In this case, the Indication indicates that data should be collected by NWDAF.
  • the message includes an Indication on whether data analytics is needed by NWDAF, e.g., with AI methods, and AI result is output to DAM-PSF for data fusion, instead of the raw data collected by NWDAF.
  • the message includes data source information, e.g., area, time, etc. It indicates that data (e.g., network data) should be collected by NWDAF in specific area and/or specific time.
  • data e.g., network data
  • the message includes a DAM-PSF address to notify NWDAF the address of the DAM-PSF.
  • the NWDAF uses the address to directly connect and/or send data to the DAM-PSF.
  • the NWDAF receives a session ID, the session ID identifies a connection between the NWDAF and the DAM-PSF.
  • the session ID can be included in the message or other messages sent by the DAM-TCF or other functions (e.g., C/M plane function, or DAM-PSF) .
  • the connection can be a C/M plane connection (e.g., C/M plane session) .
  • the connection can be based on one or more protocols of: a HTTP, a TCP, a UDP, a QUIC, a MoQ, or a PFCP.
  • the NWDAF sends data via the connection to the DAM-PSF.
  • DAM-PSF collects data (e.g., sensing data) from data source 1.
  • an AInf if an AInf is configured in Step 1101, the data will be marked with the AInf.
  • the AInf will be encapsulated in the data by data source or other intermediate functions (e.g., RAN node, Data-TW-GW, or UPF+) .
  • the data will be collected by DAM-PSF via its dedicated data plane tunnel (e.g., transport network layer (TNL) tunnel (e.g., GTP-U tunnel, QUIC connection, MoQ Session) identified by the tunnel ID included in the message of Step 1101.
  • dedicated data plane tunnel e.g., transport network layer (TNL) tunnel (e.g., GTP-U tunnel, QUIC connection, MoQ Session) identified by the tunnel ID included in the message of Step 1101.
  • TNL transport network layer
  • NWDAF collects data (e.g., network data) from data source 2, then sends the collected data to the DAM-PSF via the direct interface between the NWDAF and the DAM-PSF.
  • data e.g., network data
  • the NWDAF collects data from data source 2.
  • an AInf is configured in Step 1103, the data sent to DAM-PSF by NWDAF will be marked with the AInf.
  • the AInf will be encapsulated in the data by data source or NWDAF.
  • the data will be sent to DAM-PSF by NWDAF via a dedicated connection (e.g., HTTP-based connection, HTTP-based C/M plane session) identified by the session ID included in the message of Step 1101.
  • a dedicated connection e.g., HTTP-based connection, HTTP-based C/M plane session
  • the NWDAF collects raw data from data source, performs analytics on the raw data and then sends the analytics results (e.g., AI result) to DAM-PSF via the direct interface.
  • the NWDAF collects raw data from data source, performs analytics on the raw data and then sends the analytics results (e.g., AI result) to DAM-PSF via the direct interface.
  • DAM-PSF performs data processing (e.g., data fusion) .
  • DAM-PSF performs data fusion, e.g., to aggregate data directly sent by NWDAF and data collected by itself. In some cases, DAM-PSF performs data fusion, e.g., to aggregate data directly sent by NWDAF and data collected by itself on data plane marked with the same AInf.
  • DAM-PSF performs data fusion, e.g., to aggregate data directly sent by NWDAF marked with the AInf included in the message of Step 1101 and data collected by itself via a dedicated data plane tunnel identified by the tunnel ID included in the message of Step 1101.
  • DAM-PSF performs data fusion, e.g., to aggregate data directly sent by NWDAF via a dedicated connection (e.g., C/M plane session) identified by the session ID included in the message of Step 1101 and data collected by itself on data plane marked with the AInf included in the message of Step 1101.
  • a dedicated connection e.g., C/M plane session
  • DAM-PSF performs data fusion, e.g., to aggregate data directly sent by NWDAF via a dedicated connection (e.g., C/M plane session) identified by the session ID included in the message of Step 1101 and data collected by itself via a dedicated data plane tunnel identified by the tunnel ID included in the message of Step 1101.
  • a dedicated connection e.g., C/M plane session
  • the first network function and the second network function can be for providing a first service, which is similar as service A mentioned above, and the third network function can be for providing a second service, which is similar as service B mentioned above.
  • the first service can include an ISAC service.
  • the first service can be for one or more of types of data: sensing data, sensor data, network data, UE subscription data, data from third party, or AI related data
  • the second service is for one or more of types of data: sensing data, sensor data, network data, UE subscription data, data from third party, or AI related data.
  • sensing data, sensor data, network data, UE subscription data, data from third party, and/or AI related data may occur during the procedure for the first network function to provide the first service.
  • sensing data, sensor data, network data, UE subscription data, data from third party, and/or AI related data may occur during the procedure for the second network function to provide the second service.
  • sensing data, sensor data, network data, UE subscription data, data from third party, and/or AI related data may occur.
  • the type of data for the first service may be same as that for the second service or may be different from that for the second service.
  • the first network function, the second network function, and the third network function can provide services for a variety of data consumers.
  • the third network function of control plane can be enhanced to support the service related to the first network function and the second network function in an evolutionary solution.
  • FIG. 12 shows another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • the method can be implemented by the second network function such as the TCF.
  • the second network function could be other device that has similar function (for example, the second network function can be a computer machine in a data center, a communication module, a chip, or any network element in the network) , which is not limited herein.
  • the second network function could also be a whole device.
  • the method may include the following steps.
  • S1210 sending a request to a first network function, where the request is for configuring the first network function to perform data processing on first data on a C/M plane and second data on a data plane; where the data processing is performed on the first data and the second data based on one or more parameters in the request.
  • FIG. 13 shows yet another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • the method can be implemented by a first network function such as a DAM.
  • the first network function could be other device that has similar function (for example, the first network function can be a computer machine in a data center, a communication module, a chip, or any network element in the network) , which is not limited herein.
  • the first network function could also be a whole device. As shown in FIG. 13, the method may include the following steps.
  • the first network function can be a DAM.
  • the TCF and PSF mentioned above can be treated as a whole DAM.
  • the second network function can include one or more of: a NWDAF, a DCCF, a MFAF, a LMF, an ADRF, and a MDAF. Therefore, throughout the implementation of FIG. 13 and FIG. 14, the first network function can be the DAM and the second network function can be the NWDAF, which is different from that in the implementation of FIG. 7 and FIG. 12. Then, referring back to FIG.
  • the first network function such as the DAM can send, to the second network function such as the NWDAF, the request for configuring the NWDAF to process first data on the C/M plane.
  • the DAM can let the NWDAF know that it is requested to process the first data, such as data collection, data source discovery, data analysis, and obtain one or more parameters from the request which may be required for processing the first data.
  • the NWDAF can share the data on the C/M plane with the DAM, so that different types of data at least including the first data on the C/M plane can be collected and processed at the DAM.
  • the detailed actions or steps performed by the TCF and PSF for collecting and processing the data can be seem from the above implementation related to the TCF and PSF, which is not repeated herein.
  • the one or more parameters can include at least one of following parameters: association information, an indication on whether to collect data, an indication on whether to analyze data, data source information, an address of a first network function, or a session ID.
  • association information, the data source information, the address of the first network function, and the session ID are similar as that defined in the request in S710, which is not repeated herein for brevity.
  • the indication on whether to collect data is for instructing the second network function to collect data or not. In some cases, the second network function needs to collect data. In some other cases, the second network function is not required to collect data while it is required to discover the data source of the data.
  • the indication on whether to analyze data is for instructing the second network function to analyze data or not.
  • the first network function can let the second network function know the parameter (s) used for marking data packets.
  • the second network function such as the NWDAF is required to mark the collected data with the association information, and thus, by using the association information, the second network can assist in the data fusion. Different data packets marked with same association information can be sent to the first network function together.
  • the first network function can let the second network function know what it should do for the first data.
  • the first network function can let the second network function know which source the first data is originated from. And by including the address of the first network function, the first network function can let the second network function know where the first data should be sent. Then by including the session ID, the first network function can let the second network function know which connection between the first network function and the second network function the data should be delivered to. Therefore, by including one or more parameters in the request, various application scenarios can be accommodated.
  • the first network function can send the request to the second network function for configuring the second network function to collect the first data, to discover a second data source (which is the same as the second data source as mentioned above) , and/or to analyze the first data.
  • the second network function may collect the first data by itself from the second data source, while there is also option that the second network function only perform data source discovery without collecting data (for example, referring to FIG. 6, option 4 indicates that the data is not collected by the NWDAF) .
  • data analytics is needed by the second network function, e.g., with AI methods, and AI result is output to the first network function for data fusion, instead of the raw data collected by the second network function.
  • the response may be for indicating whether the request is accepted or rejected.
  • the second network function may send the response including an indication for indicating the request is accepted to the first network function.
  • the response may carry the indication or the response itself can be the indication, such as a field of the response can represent the indication.
  • rejecting the request the second network function may send the response including a rejection for indicating the request is rejected to the first network function.
  • the second network function rejects the request from the first network function, such as in busy, and so on, which is not limited herein.
  • the response may carry the rejection or the response itself can be the rejection, such as a field of the response can represent the rejection.
  • the first network function can obtain second data on a data plane from a first data source (which is the same as the first data source as mentioned above) , obtain the first data from a second data source, and perform data fusion on the first data and the second data.
  • the first network function such as the DAM can obtain the second data from the first data source and obtain the first data from the second data source.
  • the first data described herein is collected by the second network function and may be processed by the second network function.
  • the first network function can perform data fusion on the first data and the second data. In this way, the second data on the data plane and the first data on the C/M plane can be shared and processed within network.
  • the first network function can perform data fusion on the first data marked with the association information and the second data marked with the same association information, perform data fusion on the first data marked with the association information and the second data delivered on a data plane tunnel identified by a data plane tunnel ID, perform data fusion on the first data delivered on a connection corresponding to the session ID and the second data marked with the association information, or perform data fusion on the first data delivered on a connection corresponding to the session ID and the second data delivered on a data plane tunnel identified by a data plane tunnel ID.
  • the first network function can easily and clearly know which data packets should be aggregated together for data fusion, thereby assisting in data sharing and data processing of the first network function within network.
  • the details procedure for performing data fusion based on different parameters can be seem from the above implementation related to the TCF and PSF, which is not repeated herein.
  • FIG. 14 shows yet another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • the method can be implemented by a second network function such as a NWDAF.
  • the second network function could be other device that has similar function (for example, the second network function can be a computer machine in a data center, a communication module, or a chip, sensor, or actuator) , which is not limited herein.
  • the second network function could also be a whole device.
  • the method may include the following steps.
  • the first network function can obtain second data on a data plane from a first data source, obtain the first data from a second data source, and perform data fusion on the first data and the second data.
  • the second network function can obtain the first data from a second data source, and send the first data to the first network function.
  • the data packets can be marked by the association information and the connection between the first network function and the second network function can be identified by the session ID, there are many situations for the second network function to send the first data to the first network function.
  • the second network function can send the first data, where the first data is marked with the association information, send the first data via a connection corresponding to the session ID, send an analytics result of the first data, where the analytics result is marked with the association information, and/or send an analytics result of the first data via a connection corresponding to the session ID.
  • the second network function can send the first data and/or the analytics result of the first data in a clear way.
  • FIG. 15 shows yet another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • the method can be implemented by a system including a first network function such as a PSF and a second network function such as a TCF.
  • the first network function performing the implementation as described in FIG. 7.
  • the second network function performing the implementation as described in FIG. 12.
  • the method may include the following steps.
  • a second network function sends a request to a first network function, where the request is for configuring the first network function to perform data processing on first data on a C/M plane and second data on a data plane.
  • the first network function obtains the request from the second network function.
  • the first network function performs the data processing on the first data and the second data based on one or more parameters in the request.
  • Some messages may be exchanged between the first network function and the second network function, and through these messages, different types of data including first data on the C/M plane and second data on the data plane can be collected and processed at the first network function, and thus, data sharing and data processing can be performed within the network on C/M plane and data plane for different types of data, e.g., network data from control plane and new types of data, such as sensing data and AI related data, from data plane.
  • the procedure for exchanging messages and other steps are similar as described above, which is not repeated herein for brevity.
  • FIG. 16 shows yet another schematic flowchart of a method for data processing according to one or more example implementations of the present disclosure.
  • the method can be implemented by a system including a first network function such as a DAM and a second network function such as a NWDAF.
  • the first network function performing the implementation as described in FIG. 13.
  • the second apparatus performing the implementation as described in FIG. 14.
  • the method may include the following steps.
  • a first network function sends a request to a second network function, where the request is for configuring the second network function to process first data on a C/M plane.
  • the second network function obtains the request from the first network function.
  • the second network function sends a response to the first network function, where the response is for acknowledging a receipt of the request.
  • the first network function obtains the response from the second network function.
  • the second network function processes the first data based on one or more parameters in the request.
  • Some messages may be exchanged between the first network function and the second network function, and through these messages, the second network function can share the data on the C/M plane with the first network function, so that different types of data at least including the first data on the C/M plane can be collected and processed at the first network function.
  • the procedure for exchanging messages and other steps are similar as described above, which is not repeated herein for brevity.
  • TCF Transmission Control Function
  • PSF PSF of service B
  • DAM service e.g., DAM service
  • FIG. 17 shows a schematic flowchart of a procedure of data collection and pre-processing according to one or more example implementations of the present disclosure.
  • the service B e.g., DCaaS of DAM service
  • DCaaS collects data from data source, and it will perform data pre-processing if the collected data is non-directly useable, useless or redundant, and obtain the final target data for the service requester.
  • One or multiple DC-PSFs may participate into data pre-processing in sequence or parallel to obtain the final target data.
  • data pre-processing types e.g., data cleaning, data normalization, data filtering, data stitch, data compression, data synthetization, feature engineering, dataset classification, data structuring, and data aggregation.
  • DCaaS maintains different kinds of data pre-processing methods and deploys the execution resources and environments, and provides service using suitable data pre-processing method for specific service requirement (QoS) from Service Requester.
  • QoS service requirement
  • FIG. 17 it illustrates the procedure to collect data from data source, and perform data pre-processing if the collected data is non-directly useable, useless or redundant, to obtain the final target data.
  • DAM service request (Ndam_DataCollection_Request) message from Service Requester to DAM for data collection.
  • the service request is to collect raw data from Data Sources, (if necessary) pre-process raw data into target data, and deliver the target data to a Data Storage.
  • Service Requester could be a device, a XaaS service (e.g., MM) , another DAM service (e.g., DAaaS) , or a 3 rd party.
  • a XaaS service e.g., MM
  • another DAM service e.g., DAaaS
  • 3 rd party e.g., a 3 rd party.
  • the message includes one or more parameters: data source information (e.g., data source ID, location) , data information (e.g. data representation, data feature) indicating what data is required, target data requirement (e.g., data format, data structure) indicating the requirement on the final target data, data quality requirement, DAM QoS, data valid information (e.g., valid time, valid area (e.g., cell, TAC, location, network) ) , number of required datasets, correlation between required datasets, dataset size, application category (identified by category ID) indicating the application the required data will be used for.
  • data source information e.g., data source ID, location
  • data information e.g. data representation, data feature
  • target data requirement e.g., data format, data structure
  • DAM QoS data quality requirement
  • data valid information e.g., valid time, valid area (e.g., cell, TAC, location, network)
  • number of required datasets e.g., correlation between required datasets
  • the message may be included in a container of a mission session establishment request message.
  • the DC-TCF may check whether there is reusable data which was already collected and stored before, if there is, Steps 1702-1704 and 1708 could be skipped.
  • DC-TCF performs data source discovery based on the constructed Data Information and Data Correlation Information.
  • Step 1703 Data collection require (Nds_DataCollection_Require) message from DC-TCF to Data Source (s) .
  • the Data Source (s) is selected in Step 1702. Some of the information received in Step 1701 may be included this message.
  • DC-TCF selects one or more DC-PSFs to perform data collection and (optional) data pre-processing, and constructs Data Query Statement, Data Collection and Pre-processing (DCP) Rules, and Requirement on final target data.
  • DCP Data Collection and Pre-processing
  • the PSF selection may be performed with the consideration of locations and contextual information of data sources.
  • the locations or contextual information of data sources may be retrieved by DAM from Connectivity Management (CM) service.
  • CM Connectivity Management
  • Some of the selected DC-PSF are border PSFs to connect with external entities (e.g., Data-TW-GW) , some of them are internal PSFs.
  • Data Query Statement is to indicate what raw data should be collect from which data sources, including one or more parameters: data information (e.g., data representation) indicating the data to be collected, data (source) address.
  • data information e.g., data representation
  • data (source) address e.g., data (source) address
  • DCP Rule is to indicate how the collected raw data should be pre-processed to get the final target data, e.g., the rule to modify and clean non-directly useable data, to filter useless or redundant data.
  • DCP rule includes one or more parameters: data preprocessing function indicating the function with which the raw data should be pre-processed, data information (e.g., data representations indexing the data to be pre-processed) , Data Correlations Information (e.g., the correlation between different involved datasets to help DC-PSF pre-process data) , and condition (e.g. step sequence, timestamp, trigger event) .
  • the DCP Rule may be indicated with a Mission Selection Assistance ID (MSAI) or a Mission Block ID (MBID) .
  • MSAI Mission Selection Assistance ID
  • MBID Mission Block ID
  • Data pre-processing function indicates: data pre-processing type, and data pre-processing algorithm.
  • Requirement on final target data could include data type (e.g. data format) , (optional) number of datasets, and (optional) dependency of datasets (e.g., for data ID or feature alignment) .
  • data type e.g. data format
  • number of datasets e.g., number of datasets
  • dependency of datasets e.g., for data ID or feature alignment
  • the DC-TCF configures the selected DC-PSFs to execute data collection and pre-processing.
  • DC-TCF may send messages to all or parts of the selected DC-PSFs, e.g., to each of the involved DC-PSF, or only to the firstly involved DC-PSF.
  • the message may include Tunnel information on DC-PSF side.
  • DC-TCF contacts with Data Source to setup the data plane connection between the data source and border DC-PSF.
  • the data plane connection may be established with the format of Mission Session.
  • DAM session may be one type of mission session, or a DAM session is a part of a mission session, e.g., a mission session includes a collection of DAM sessions.
  • the data source could be the Service Requester itself.
  • the Data Source and/or the Service Requester may be configured by the DC-TCF to perform data pre-processing cooperatively with DC-PSF.
  • Data Storage could be a DAM local data storage, a storage provided by NET4Data, a storage of Service Requester, or other data destinations.
  • DAM service response (Ndam_DataCollection_Response) message from DC-TCF to the Service Requester.
  • the message indicates the successful preparation of resources for data collection and data pre-processing. Tunnel information on DC-PSF side may be included in the message.
  • One or more DC-PSFs collect raw data from Data Source via data plane, and (optional) pre-process the raw data into target data as configured in Step 1706.
  • Data Source, DC-PSF and even Service Requester may cooperate to pre-process data.
  • the Data Source is configured in Step 1708 to pre-process data cooperatively, the intermediate pre-processed data instead of the raw data may be collected from the data source.
  • the final target data is outputted by DC-PSF to Data Storage.
  • the service B (e.g., DAM service) includes Data Analytics as a Service (DAaaS) .
  • DAM service includes Data Analytics as a Service (DAaaS) .
  • the purposes of DAaaS is to mine Data Information, and Data Correlation Information. They can be used for data discovery and data retrieval.
  • the data information may be raw data’s metadata, data representation, data name, data title, key word, semantics, data attribute, data feature, application category, Hash value of data, etc.
  • the raw data could be network data, sensing data, IoT data, and data for AI, etc.
  • the data information may also include the information on its data source, e.g. data source ID, data source type, application category that the data source’s data can be used for, location of data source, ability (e.g., power, channel state) of data source, contextual information of data source, etc.
  • Data Correlation Information indicates the coherency of different data items or datasets. The correlation may be equality/inequality, similarity/dissimilarity, inclusion/exclusion dependency, transitive correlation, etc.
  • the DAaaS service is to generate data model of raw data or synthetic data of raw data.
  • the data model instead of the raw data can be transmitted to reduce the overhead of data collection and transmission.
  • the data model or the synthetic data instead of the raw data can be exposed to data consumer to protect data privacy.
  • Data model includes: e.g. generative model, data synthetic model, data statistical model, data compression model, data encoder model (e.g., auto-encode model) , data semantic model, data significance pre-evaluation model.
  • the original realistic data can be compressed or synthetic data (which has the similar feature (e.g., statistical feature) with the raw data) can be generated, and the necessary information (e.g. semantic feature, statistical feature, and even original data) on the realistic data can be inferred and reconstructed from the data model; or, based on data model (e.g., data significance pre-evaluation model) , some data without significance can be filtered and not transmitted further, or their retransmission is not needed.
  • data model e.g., data significance pre-evaluation model
  • some data without significance can be filtered and not transmitted further, or their retransmission is not needed.
  • DAaaS service can be performed with AI methods, e.g., with AI training, or AI inference.
  • DAaaS maintains different kinds of data analytics methods and deploys the execution resources and environments, and provides service using suitable data analytics method for specific service requirement (QoS) from Service Requester.
  • QoS service requirement
  • FIG. 18 shows a schematic flowchart of a procedure of data analytics according to one or more example implementations of the present disclosure. As in FIG. 18, it illustrates the procedure to mine and maintain Data Information on raw data, and/or Data Correlation Information on raw data, or to generate data model of raw data or synthetic data of raw data.
  • DAM service request (Ndaaas_DataAnalytics_Request) message from Service Requester to DA-TCF to request for Data Analytics.
  • the message includes one or more of: Data Information Analytics Requirement (e.g. DAaaS QoS, analytics delay, Data Information accuracy level, and privacy level) , Data Correlation Information Analytics Requirement (e.g., DAaaS QoS, correlation type, correlation level, analytics latency, accuracy level, and privacy level) , Data Model Requirement (e.g., DAaaS QoS, accuracy level, privacy level, data compression ratio) .
  • Data Information Analytics Requirement e.g. DAaaS QoS, analytics delay, Data Information accuracy level, and privacy level
  • Data Correlation Information Analytics Requirement e.g., DAaaS QoS, correlation type, correlation level, analytics latency, accuracy level, and privacy level
  • Data Model Requirement e.g., DA
  • Service Requester could be a device, a XaaS service (e.g., MM) , another DAM service (e.g., DCaaS) , or a 3 rd party.
  • a XaaS service e.g., MM
  • another DAM service e.g., DCaaS
  • 3 rd party e.g., a 3 rd party.
  • Data information should be mined and extracted from raw data if the Data Information Analytics Requirement is included in the message.
  • Data Information can be mined and extracted from the raw data, e.g., with technologies of representation learning (embedding) or data profiling (e.g. auto-encoder) .
  • Data Correlation Information should be mined and evaluated from raw data if the Data Correlation Information Analytics Requirement is included in the message.
  • Data model or Synthetic data should be generated from raw data if the Data Model Requirement is in the message.
  • DA-TCF performs DA-PSF selection, and performs Data Analytics Rule selection from rule library.
  • Data Analytics Rule indicates how to mine insight and knowledge (e.g., Data Information, Data Correlation Information, and Data Model) from raw data.
  • Data Analytics Rule may include data analytics model, algorithm (e.g., GANs, GNN and VAE) of data analytics, metrics (e.g., analytics accuracy) , etc.
  • Data analytics request message from DA-TCF to DA-PSF includes the selected Data Analytics Rule (e.g., data analytics model identified by a model ID) . It configures the selected DA-PSFs for data analytics.
  • Data Analytics Rule e.g., data analytics model identified by a model ID
  • DA-PSF may invoke detailed parameters (e.g., model hyper-parameter, programming code, script) of the selected Data Analytics Rule from DA-SCF.
  • DA-TCF may send messages to all or parts of the selected DA-PSFs, e.g., to each of the involved DA-PSF, or only to the firstly involved DA-PSF.
  • the tunnel information on DA-PSF side to received data from Service Requester may be included in the message.
  • Service Requester may be configured by the DA-TCF to perform data analytics cooperatively with DA-PSF.
  • Data Storage could be a DAM local data storage, a storage provided by NET4Data, a storage of Service Requester, or other data destinations.
  • Data analytics response (Ndaaas_DataAnalytics_Response) from DA-TCF to Service Requester to indicate the successful resource preparation for data analytics.
  • Tunnel information on DA-PSF side may be included in message.
  • Service Request sends the data to be analyzed to one or more DA-PSFs via data plane, and DA-PSF performs data analytics to get the Data Information, Data Correlation Information, Data Model, or Synthetic data as configured in Step 1806.
  • Service Requester and DA-PSF may cooperate to analyze data as configured in Step 1805. There may be forth and back data transmissions and cooperative computing between DAaaS and Service Requester to obtain the final data analytics results.
  • DAaaS can be also assisted by other XaaS, e.g., NET4AI.
  • the analytics result is outputted by DA-PSF to the Data Storage.
  • the service B (e.g., DAM service) includes Data De-privatization as a Service (DPaaS) .
  • DAM service includes Data De-privatization as a Service (DPaaS) .
  • Data de-privatization method includes one or more of: Obfuscation-based method (e.g., K-anonymity, differential privacy, pseudonym) , Cryptography-based method (e.g., homomorphic encryption, functional encryption, multi-party computation, zero knowledge) , Hardware-based method (e.g., secure enclave, TEE) , and, AI-based method (e.g., with schemes of generative adversarial network (GAN) , auto-encoder) .
  • Obfuscation-based method e.g., K-anonymity, differential privacy, pseudonym
  • Cryptography-based method e.g., homomorphic encryption, functional encryption, multi-party computation, zero knowledge
  • Hardware-based method e.g., secure enclave, TEE
  • AI-based method e.g., with schemes of generative adversarial network (GAN) , auto-encoder
  • DPaaS maintains different kinds of DP methods and deploy the execution resources and environments, and provides service using suitable DP method for specific service requirement (QoS) from Service Requester.
  • QoS service requirement
  • FIG. 19 shows a schematic flowchart of a procedure of data de-privatization according to one or more example implementations of the present disclosure. As in FIG. 19, it illustrates the procedure to perform data de-privatization (sanitization) .
  • DAM service request (Ndam_DataDeprivatization_Request) message from Service Requester to DAM.
  • the message includes one or more of: DP service requirement, (opt. ) DP capability of the Service Requester.
  • DP service requirement may indicate the DP metrics (e.g., XQoS, data privacy level) and (optional) required DP method (e.g., algorithm) .
  • DP metrics e.g., XQoS, data privacy level
  • required DP method e.g., algorithm
  • DP capability indicate the DP methods (e.g., DP algorithms) supported by the Service Requester.
  • Service Requester could be a device, data source, data owner, a XaaS service (e.g., MM) , another DAM service (e.g., DCaaS) , or a 3 rd party.
  • a XaaS service e.g., MM
  • another DAM service e.g., DCaaS
  • 3 rd party e.g., a 3 rd party.
  • DP-TCF checks whether DPaaS can complete data de-privatization alone or need other XaaS service’s assistance.
  • DPaaS may be not able to execute AI model (e.g., GAN model, or parts of GAN model) . It needs assistance of DAaaS or NET4AI to execute AI model.
  • AI model e.g., GAN model, or parts of GAN model
  • DPaaS may provide a Trusted Execution Environment (TEE)
  • TEE Trusted Execution Environment
  • the Service Requester sends the raw data
  • NET4AI sends AI model parameters to TEE.
  • Inference result is computed in the TEE using the raw data and AI model, and then outputted.
  • DAM may retrieve other XaaS services’A ccess Control Policy on the raw data, e.g., from NET4Data or Service Requester. Access Control Policy indicates whether other XaaS services have the authorization to access the raw data to be de-privatized.
  • DP-TCF performs selection of DP-PSFs, and selects DP methods.
  • the DP-TCF selects and invokes suitable DP method from the DP method library managed by DP-SCF.
  • DP-TCF Based on the DP capability supported by Service Requester (received in Step 1901) and that supported by DP-PSF, DP-TCF selects the overlapped DP capability for data de-privatization.
  • DP-TCF may setup different configurations to DP-PSF.
  • AI-based DP method e.g., GAN
  • the raw data can be delivered to NET4AI/DAaaS
  • the whole AI model e.g., both generative model and discriminative model
  • NET4AI/DAaaS e.g., both generative model and discriminative model
  • NET4AI/DAaaS e.g., both generative model and discriminative model
  • Data de-privatization request message from DP-TCF to DP-PSF, to configure PSF to de-privatize data includes one or more of: DP method ID, DP algorithm ID, security mode ID, ciphering Key material.
  • the DP method ID indicates a DP method (e.g., obfuscation-based method, cryptography-based, hardware-based method, and AI-based method) .
  • a DP method e.g., obfuscation-based method, cryptography-based, hardware-based method, and AI-based method
  • the DP algorithm ID indicate a DP algorithm (e.g., cyphering algorithm) of the corresponding DP method (e.g., cryptography-based method) .
  • ciphering Key material may be indicated for the corresponding DP algorithm.
  • AI-based method e.g., Data synthetization with GAN, VAE
  • the data model e.g., generative model, synthetic model
  • the data model e.g., generative model, synthetic model
  • synthetic model of raw data or synthetic data of raw data are exposed instead of raw data.
  • the DP-PSF may invoke the DP tools (e.g., DP algorithm parameter, programming code, and script) from DP-PSF local storage or from DP-SCF based on the information received in the message.
  • DP tools e.g., DP algorithm parameter, programming code, and script
  • DP-TCF may interact with other XaaS services for corresponding configurations to de-privatize data cooperatively.
  • the Service Requester may be configured by the DP-TCF to perform data de-privatization cooperatively with DP-PSF. For example, if AI-based method is used to de-privatize data, parts of AI model (e.g., generative model of GAN) without touching the raw data may be executed in DP-PSF, and other parts of AI model (e.g., discriminative model of) touching the raw data may be executed in Service provider. As another example, the selected DP method, algorithm and related key materials may be configured to the Service Requester.
  • Data Storage could be a DAM local data storage, a storage provided by NET4Data, a storage of Service Requester, or other data destinations.
  • the Service Requester sends the data to be de-privatized to one or more DP-PSFs via data plane, and DP-PSF performs data de-privatization.
  • Service Requester and DP-PSF may cooperate to de-privatize data as configured in Step 1906. There may be forth and back data transmissions and cooperative computing between DPaaS and Service Requester to obtain the final de-privatized data.
  • the de-privatized data is outputted by DP-PSF to the Data Storage.
  • the service B (e.g., DAM service) includes Data Delivery as a Service (DDaaS) .
  • DAM service includes Data Delivery as a Service (DDaaS) .
  • DDaaS is to deliver data to one or multiple destinations.
  • the data to be delivered can be raw data, or post-processed data (e.g., posted analytics data) .
  • DDaaS may also provide a unified data query service.
  • DDaaS maintains different Data Delivery Rules, and provides service using suitable Data Delivery Rule for specific service requirement (QoS) from Service Requester.
  • QoS Quality of Service
  • the Data Delivery Rule indicates how the data can be delivered to one or multiple destinations. It is used for traffic steering, data forwarding, data aggregation, routing, etc.
  • the Data Delivery Rule may include data forwarding Path Selection Information, Data Forwarding Treatment Information, etc.
  • Data forwarding path may be scheduled and programmed by C/M plane.
  • the path may include radio link and wired tunnels, and multiple segments in sequence and parallel.
  • Path Selection Information may be configured to each of the involved DD-PSFs, e.g., via control message by the C/M plane, or only configured to parts of the involved DD-PSFs (e.g., the firstly involved DD-PSF) and encapsulated in packet header, and the other DD-PSFs perform data delivery based on the instruction of Path Selection Information in the packet header.
  • Data Forwarding Treatment Information includes input and output data format, ciphering method and algorithm for secured tunnel, loss rate, delivery latency, etc.
  • Different Data Formats for input and output data may be defined, and each is identified by a Data Format ID.
  • Service Requester could be a device, a XaaS service (e.g., MM) , another DAM service (e.g., DCaaS) , or a 3 rd party.
  • a XaaS service e.g., MM
  • another DAM service e.g., DCaaS
  • 3 rd party e.g., data delivery between DAM local storage and a data consumer is supported by DDaaS.
  • Data delivery among internal PSFs (e.g., among DC-PSFs, among DC-PSFs and DP-PSFs, etc. ) of DAM may be supported by DDaaS.
  • Data delivery between device and 3 rd party may be supported by DDaaS.
  • FIG. 20 shows a schematic flowchart of a procedure of data delivery from service requester to data destination according to one or more example implementations of the present disclosure. As in FIG. 20, it illustrates the procedure to deliver data to one or multiple data destinations.
  • DAM service request (Ndam_DataDelivery_Request) message from Service Provider to DD-TCF.
  • the message includes Requirement (e.g., data format, data loss rate, delay) on data delivery, data destination.
  • Service Requester could be a device, a XaaS service (e.g., MM) , another DAM service (e.g., DCaaS) , or a 3 rd party.
  • a XaaS service e.g., MM
  • another DAM service e.g., DCaaS
  • 3 rd party e.g., a 3 rd party.
  • DD-PSF could be an internal deployment in DAM service with the similar scheme of Data-TW-GW.
  • DD-TCF selects involved DD-PSFs, and generates Data Delivery Rule based on information received in Step 2001.
  • Data Deliver Rule may be indicated to each of the involved DD-PSFs via control messages from DD-TCF, or via being encapsulated into packet header.
  • the message in this step may be sent to each of the involved DD-PSFs, while for the latter way, the message in this step may only sent to parts of the of the involved DD-PSFs (e.g., the firstly involved PSF, the leaf PSFs) .
  • the parts of the involved DD-PSFs may encapsulate the data delivery rule into packet header, and the other involved DD-PSFs forward data to one or multiple next-hop DD-PSFs based on the instruction of data delivery rule encapsulated in the packet header.
  • S2004 Data delivery response message from DD-PSF to DD-TCF to indicate the successful configuration.
  • the message may include Tunnel information on DD-PSF side.
  • the DD-TCF may trigger the Connection Management (CM) function to setup the radio bearer terminated at the data destination or service provider.
  • CM Connection Management
  • the DD-TCF may trigger the Mission Management (MM) function to setup the data sessions between DD-PSF and the PSF of the XaaS service.
  • MM Mission Management
  • DAM service response (Ndam_DataDelivery_Response) message from DD-TCF to Service Provider to indicate the successful resource preparation for data delivery.
  • the message may include Tunnel information on DD-PSF sides.
  • Service provider sends the data to DD-PSF and the DD-PSF delivers the data to one or multiple destinations.
  • One or multiple DD-PSFs deliver data via a specific data forwarding path as configured in Step 2003.
  • DD-PSF may perform packet header encapsulation and decapsulation on the path selection information. For example, DD-PSF may update the information included in the packet header and delete expired information in the packet header.
  • the data forwarding configuration information may be encapsulated into packet header together with the path selection information.
  • the DAM-TCF in FIG. 5, FIG. 6, FIG. 8, FIG. 9, FIG. 10, FIG. 11, FIG. 17, FIG. 18, FIG. 19, and FIG. 20 can be replaced by other functions.
  • the other functions could be logical functions, e.g., NWDAF, DCCF, MFAF, LMF or other logical function in previous networks (e.g., 5G network) .
  • the service B e.g., DAM service
  • the other functions perform the corresponding functionality of the DAM-TCF described as above.
  • the NWDAF+ can replace DAM-TCF and perform the corresponding functionality of DAM-TCF.
  • FIG. 21 shows a simplified schematic illustration of network architecture to implement DAM service in evolutionary way according to one or more example implementations of the present disclosure.
  • FIG. 21 illustrates the network architecture to implement Service B (e.g., DAM service) in an evolutionary way.
  • Previous network logical function e.g., NWDAF+, DCCF+, MFAF+
  • NWDAF+, DCCF+, MFAF+ Previous network logical function
  • DCCF+ DCCF+
  • MFAF+ new data plane function of Service B
  • They can be deployed in hierarchical framework, e.g., with distributed or central manner in device, RAN, CN and DN.
  • the data plane function of Service B (e.g., DAM-PSF) are functionalities which are decoupled from previous network logical function (e.g., NWDAF+, DCCF+, MFAF+) but which are deployed on data plane instead of on control plane.
  • DAM-PSF data plane function of Service B
  • other network functions e.g., SCP+ on control plane and UPF+ on data plane
  • SCP+ is deployed as C/M-TW-GW
  • the UPF+ is deployed as Data-TW-GW.
  • NWDAF+ implements the functionality of DAM-TCFs to control and configure the DAM-PSFs to setup connectivity resources (e.g., tunnels between DAMF-PSFs, and between DAM-PSF and data source/consumer) and data processing resources. All types of data (e.g., both sensing data and network data) to be collected are sent to DAM-PSF instead of NWDAF+ (or DCCF+, MFAF+) .
  • FIG. 22 shows a simplified schematic illustration of network architecture to implement DAM service in revolutionary way according to one or more example implementations of the present disclosure.
  • FIG. 22 illustrates network architecture to implement Service B (e.g., DAM service) in a revolutionary way.
  • Service B e.g., DAM service
  • C/M plane function DAM-TCF and data plane function DAM-PSF are deployed.
  • DAM functions can be deployed in hierarchical framework, e.g., with distributed or central manner in device, RAN, CN and DN.
  • other network functions e.g., NET4CON Data-TW-GW and NET4CON C/M-TW-GW, are also deployed in revolutionary way.
  • the C/M-TW-GW is deployed as SCP+
  • the Data-TW-GW is deployed as UPF+.
  • different DAM-PSFs in the same or different network domains can be connected directly (e.g., in mesh topology) or indirectly (e.g., via Data-TW-GW) .
  • DAM-TCFs control and configure DAM-PSFs to perform in-network data processing.
  • previous network logical functions e.g., NWDAF, MFAF, DCCF, or LMF
  • Service B functions e.g., without deploying previous network logical functions (e.g., NWDAF, MFAF, DCCF, or LMF) .
  • Service B (e.g., DAM service) is aimed at data processing instead of solely data forwarding, e.g., to execute one or multiple of data collection, data pre-processing, data de-privatization, data analytics, and data delivery.
  • New quality of service (QoS) on DAM may be defined as in one or more of the parameters in Table 1 to provide particular DAM service.
  • DAM QoS flow may be defined to be the finest granularity of QoS differentiation in DAM service. Traffic (e.g., in a DAM QoS flow) with the same DAM QoS receives the same data processing treatment and data forwarding treatment.
  • New radio bearers (e.g., XRB) dedicated for Service B may be established over the air on C/M plane and data plane to provide DAM service with particular QoS.
  • Table 1 QoS parameters on Service B (e.g., DAM service)
  • a possible implementation of the present disclosure provides a communication apparatus 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000 configured to perform the method according to any one of the above methods, which is not repeated herein.
  • FIG. 23 shows a schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • the communication apparatus 2300 includes: an obtaining unit 2310, configured to obtain a request from a second network function, where the request is for configuring a first network function to perform data processing on first data on a C/M plane and second data on a data plane; and a data processing unit 2320, configured to perform the data processing on the first data and the second data based on one or more parameters in the request.
  • FIG. 24 shows another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • the communication apparatus 2400 includes: a sending unit 2410, configured to send a request to a first network function, where the request is for configuring the first network function to perform data processing on first data on a C/M plane and second data on a data plane; where the data processing is performed on the first data and the second data based on one or more parameters in the request.
  • FIG. 25 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • the communication apparatus 2500 includes: a sending unit 2510, configured to send a request to a second network function, where the request is for configuring the second network function to process first data on a C/M plane; and an obtaining unit 2520, configured to obtain a response from the second network function, where the response is for acknowledging a receipt of the request; where the first data is processed based on one or more parameters in the request.
  • FIG. 26 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • the communication apparatus 2600 includes: an obtaining unit 2610, configured to obtain a request from a first network function, where the request is for configuring a second network function to process first data on a C/M plane; a sending unit 2620, configured to send a response to the first network function, where the response is for acknowledging a receipt of the request; and a data processing unit 2630, configured to process the first data based on one or more parameters in the request.
  • FIG. 27 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • the communication apparatus 2700 includes: an interface circuit 2710, configured to: obtain a request from a second network function, where the request is for configuring a first network function to perform data processing on first data on a C/M plane and second data on a data plane; and one or more processors 2720, configured to perform the data processing on the first data and the second data based on one or more parameters in the request.
  • FIG. 28 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • the communication apparatus 2800 includes: one or more processors 2810; and an interface circuit 2820, configured to: send a request to a first network function, where the request is for configuring the first network function to perform data processing on first data on a C/M plane and second data on a data plane; where the data processing is performed on the first data and the second data based on one or more parameters in the request.
  • FIG. 29 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • the communication apparatus 2900 includes: one or more processors 2910; and an interface circuit 2920, configured to: send a request to a second network function, where the request is for configuring the second network function to process first data on a C/M plane; and obtain a response from the second network function, where the response is for acknowledging a receipt of the request; where the first data is processed based on one or more parameters in the request.
  • FIG. 30 shows yet another schematic structural diagram of a communication apparatus according to one or more example implementations of the present disclosure.
  • the communication apparatus 3000 includes: an interface circuit 3010, configured to: obtain a request from a first network function, where the request is for configuring a second network function to process first data on a C/M plane; and send a response to the first network function, where the response is for acknowledging a receipt of the request; one or more processors 3020, configured to process the first data based on one or more parameters in the request .
  • the interface circuit 2710, 2820, 2920, 3010 includes one or more transceivers 27110, 28210, 29210, 30110.
  • FIG. 31 shows a schematic structural diagram of an apparatus according to one or more example implementations of the present disclosure.
  • a possible implementation of the present disclosure provides an apparatus 3100 including: one or more processors 3110; and a memory 3120 storing instructions which, when executed by the one or more processors, cause the apparatus to perform the method according to any one of the above methods, which is not repeated herein.
  • a possible implementation of the present disclosure provides a communication system, where the communication system includes multiple communication apparatuses configured to perform the method according to the above method as described in FIG. 15.
  • a possible implementation of the present disclosure provides another communication system, where the communication system includes multiple communication apparatuses configured to perform the method according to the above method as described in FIG. 16.
  • a possible implementation of the present disclosure provides a computer-readable storage medium having instructions stored thereon which, when executed by one or more processors, cause the one or more processors to perform the method according to any one of the above methods, which is not repeated herein.
  • a possible implementation of the present disclosure provides a computer program product storing instructions which, when executed, cause an apparatus to perform the method according to any one of the above methods, which is not repeated herein.

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Abstract

L'invention concerne un procédé, un appareil et un système de traitement de données, la demande de configuration de la première fonction de réseau pour effectuer un traitement de données sur des premières données sur le plan C/M et des secondes données sur le plan de données pouvant être obtenue par la première fonction de réseau à partir de la seconde fonction de réseau. Par l'intermédiaire de la demande, la première fonction de réseau peut savoir qu'il est demandé d'effectuer un traitement de données, tel qu'une fusion de données, sur les premières données sur le plan C/M et les secondes données sur le plan de données, et peut obtenir un ou plusieurs paramètres à partir de la demande qui peuvent être requis pour effectuer le traitement de données. Ensuite, en effectuant le traitement de données sur les premières données et les secondes données sur la base d'un ou de plusieurs paramètres dans la demande, la première fonction de réseau peut traiter différents types de données. De cette manière, sur la base de la configuration provenant de la seconde fonction de réseau, différents types de données comprenant des premières données sur le plan C/M et des secondes données sur le plan de données peuvent être collectés et traités au niveau de la première fonction de réseau, et ainsi, un partage de données et un traitement de données peuvent être effectués à l'intérieur du réseau sur un plan C/M et un plan de données pour différents types de données, par exemple des données de réseau provenant d'un plan de commande et des nouveaux types de données, telles que des données de détection et des données associées à l'IA, à partir d'un plan de données.
PCT/CN2024/134379 2024-08-13 2024-11-25 Procédé, appareil et système de traitement de données Pending WO2026036563A1 (fr)

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US20240201318A1 (en) * 2021-09-01 2024-06-20 Huawei Technologies Co., Ltd. Communication method and apparatus
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