WO2025035367A1 - Procédé de gestion de mobilité d'ue, équipement utilisateur et station de base - Google Patents

Procédé de gestion de mobilité d'ue, équipement utilisateur et station de base Download PDF

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Publication number
WO2025035367A1
WO2025035367A1 PCT/CN2023/112982 CN2023112982W WO2025035367A1 WO 2025035367 A1 WO2025035367 A1 WO 2025035367A1 CN 2023112982 W CN2023112982 W CN 2023112982W WO 2025035367 A1 WO2025035367 A1 WO 2025035367A1
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WIPO (PCT)
Prior art keywords
mobility management
radio node
predicted
management method
trajectory
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PCT/CN2023/112982
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English (en)
Inventor
Zhe Chen
Miao Qu
Yincheng Zhang
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Shenzhen TCL New Technology Co Ltd
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Shenzhen TCL New Technology Co Ltd
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Priority to CN202380100931.6A priority Critical patent/CN121646951A/zh
Priority to PCT/CN2023/112982 priority patent/WO2025035367A1/fr
Publication of WO2025035367A1 publication Critical patent/WO2025035367A1/fr
Anticipated expiration legal-status Critical
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/00837—Determination of triggering parameters for hand-off
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
    • H04W36/324—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data by mobility data, e.g. speed data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/34—Reselection control
    • H04W36/36—Reselection control by user or terminal equipment
    • H04W36/362—Conditional handover

Definitions

  • the present disclosure relates to the field of communication systems, and more particularly, to a UE mobility management method, user equipment, and base station.
  • AI Artificial intelligence
  • ML machine learning
  • the wireless communication system performance can be enhanced by AI/ML assisted services.
  • Mobility enhancement such as Mobility Robustness Optimization (MRO)
  • MRO Mobility Robustness Optimization
  • SON Self-Organizing Networks
  • LTE Long-Term Evolution
  • MRO/SON offers a mechanism for the network to automatically adjust mobility parameters to enhance performance, potentially without the need for intervention from Operations, Administration, and Maintenance (OAM) .
  • OAM Operations, Administration, and Maintenance
  • the adjustment of mobility parameters typically should be controlled by OAM.
  • Obtaining mobility parameters by the OAM for mobility management at the network side is relatively less real-time compared to the User Equipment (UE) .
  • UE User Equipment
  • an AI/ML model monitoring method for enhancing the current wireless communication system is desired.
  • An object of the present disclosure is to propose a wireless communication device, such as a user equipment (UE) or a base station, and a UE mobility management method.
  • UE user equipment
  • base station a base station
  • an embodiment of the invention provides a user equipment (UE) mobility management method, executable by a UE, comprising:
  • AI artificial intelligence
  • ML machine learning
  • an embodiment of the invention provides a user equipment (UE) comprising a processor configured to call and run a computer program stored in a memory, to cause a device in which the processor is installed to execute the disclosed method.
  • UE user equipment
  • an embodiment of the invention provides a user equipment (UE) mobility management method, executable by a base station serving as a first radio node, comprising: receiving artificial intelligence (AI) /machine learning (ML) model-inferred one or more parameters of UE-based mobility management to assist a handover operation of a user equipment (UE) , wherein the one or more parameters comprise UE-predicted UE trajectory of a user equipment (UE) .
  • AI artificial intelligence
  • ML machine learning
  • an embodiment of the invention provides a base station comprising a processor configured to call and run a computer program stored in a memory, to cause a device in which the processor is installed to execute the disclosed method.
  • the disclosed method may be implemented in a chip.
  • the chip may include a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the disclosed method.
  • the disclosed method may be programmed as computer-executable instructions stored in non-transitory computer-readable medium.
  • the non-transitory computer-readable medium when loaded to a computer, directs a processor of the computer to execute the disclosed method.
  • the non-transitory computer-readable medium may comprise at least one from a group consisting of: a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a Read Only Memory, a Programmable Read Only Memory, an Erasable Programmable Read Only Memory, EPROM, an Electrically Erasable Programmable Read Only Memory and a Flash memory.
  • the disclosed method may be programmed as a computer program product, which causes a computer to execute the disclosed method.
  • the disclosed method may be programmed as a computer program, which causes a computer to execute the disclosed method.
  • the disclosed feasible procedures, signaling, and corresponding elements can conserve network resources efficiently and reduce signaling overhead.
  • the disclosed method enhances UE mobility management and facilitates faster handover (HO) and accurate HO.
  • ⁇ Faster HO In conventional handover, the UE should report the measurement report to the network, and then the network makes handover decision. In some embodiments of the disclosure, UE makes the decision for handover, and the handover will be conducted faster than conventional handover.
  • UE should transmit UE history information regarding locations or movements of the UE to a target NG-RAN node.
  • handover is performed by the UE. Since UE has the UE history information, it is easier to perform a handover to the right cell.
  • UE performs AI/ML model training and model inferencing to enhance UE mobility management.
  • the UE is enabled to use AI/ML model inference to infer the UE predicted UE trajectory and report the UE-predicted UE trajectory to the network, by which the network can have a more accurate predicted UE trajectory;
  • the network provides some kinds of information to the UE to assist the UE in AI/ML model training and handover decision.
  • the target NG-RAN node After each HO, the target NG-RAN node provides feedback information to the UE to help the UE re-train the model.
  • FIG. 1 illustrates a schematic view showing a wireless communication system comprising a user equipment (UE) , a base station, and a network entity.
  • UE user equipment
  • FIG. 2 illustrates a schematic view showing a system with an AI/ML functional framework for executing a UE mobility management method using ML models.
  • FIG. 3 illustrates a schematic view showing an embodiment of the disclosed method.
  • FIG. 4 illustrates a schematic view showing a UE sending UE-predicted UE trajectory to the network.
  • FIG. 5 illustrates a schematic view showing a source NG-RAN node sending a HO request with UE-predicted UE trajectory to a target NG-RAN node.
  • FIG. 6 illustrates a schematic view showing an example of HO in the disclosed method.
  • FIG. 7 illustrates a schematic view showing an example of a UE at a boundary of cells.
  • FIG. 8 illustrates a schematic view showing an example of UE capability reporting and configuration for each UE capability.
  • FIG. 9 illustrates a schematic view showing a system for wireless communication according to an embodiment of the present disclosure.
  • Embodiments of the disclosure are related to artificial intelligence (AI) and machine learning (ML) for wireless communication system, such as LTE or new radio (NR) air interface, and address problems of AI-assisted UE mobility management.
  • AI artificial intelligence
  • ML machine learning
  • NR new radio
  • LCM Life cycle management
  • AI/ML models can be installed and executed in one or more of UE (s) and NW (e.g., base station, LMF, etc. )
  • NW e.g., base station, LMF, etc.
  • one or more AI/ML models may be installed and executed in UE 10 or/and installed and executed in a NW 20, wherein the AI/ML model (s) is used for different feature and/or functions considering one-side model, and/or two side model.
  • a telecommunication system including a UE 10a, a UE 10b, a base station (BS) 20a, and a network entity device 30 executes the disclosed method according to an embodiment of the present disclosure.
  • FIG. 1 is shown for illustrative not limiting, and the system may comprise more UEs, BSs, and CN entities. Connections between devices and device components are shown as lines and arrows in the FIGs.
  • the base station 20a can operate as a gNB for 5G NR networks, an eNB for LTE networks, or a base station for future mobile network systems beyond 5G.
  • a gNB is a 5G radio network node that connects to the core network via the NG interface.
  • An eNB is a 4G radio network node that connects to the evolved packet core via the S1 interface.
  • a base station for beyond 5G may be a smart virtual eNB (SVeNB) that can perform functions of EPS elements and reduce end-to-end delay.
  • SVeNB smart virtual eNB
  • the UE 10a may include a processor 11a, a memory 12a, and a transceiver 13a.
  • the UE 10b may include a processor 11b, a memory 12b, and a transceiver 13b.
  • the base station 20a may include a processor 21a, a memory 22a, and a transceiver 23a.
  • the network entity device 30 may include a processor 31, a memory 32, and a transceiver 33.
  • Each of the processors 11a, 11b, 21a, and 31 may be configured to implement proposed functions, procedures and/or methods described in the description. Layers of radio interface protocol may be implemented in the processors 11a, 11b, 21a, and 31.
  • Each of the memory 12a, 12b, 22a, and 32 operatively stores a variety of programs and information to operate a connected processor.
  • Each of the transceivers 13a, 13b, 23a, and 33 is operatively coupled with a connected processor, and transmits and/or receives radio signals or wireline signals.
  • the UE 10a may be in communication with the UE 10b through a sidelink.
  • the base station 20a may be an eNB, a gNB, or one of other types of radio nodes, and may configure radio resources for the UE 10a and UE 10b.
  • Each of the processors 11a, 11b, 21a, and 31 may include an application-specific integrated circuit (ASICs) , other chipsets, logic circuits and/or data processing devices.
  • ASICs application-specific integrated circuit
  • Each of the memory 12a, 12b, 22a, and 32 may include read-only memory (ROM) , a random-access memory (RAM) , a flash memory, a memory card, a storage medium and/or other storage devices.
  • Each of the transceivers 13a, 13b, 23a, and 33 may include baseband circuitry and radio frequency (RF) circuitry to process radio frequency signals.
  • RF radio frequency
  • the network entity device 30 may be a node in a CN.
  • CN may include LTE CN or 5G core (5GC) which includes user plane function (UPF) , session management function (SMF) , access and mobility management function (AMF) , unified data management (UDM) , policy control function (PCF) , control plane (CP) /user plane (UP) separation (CUPS) , authentication server (AUSF) , network slice selection function (NSSF) , and the network exposure function (NEF) .
  • LMF user plane function
  • SMF session management function
  • AMF access and mobility management function
  • UDM unified data management
  • PCF policy control function
  • PCF control plane
  • CP control plane
  • UP user plane
  • CUPS authentication server
  • NSSF network slice selection function
  • NEF network exposure function
  • An example of the UE in the description may include one of the UE 10a or UE 10b.
  • An example of the base station in the description may include the base station 20a.
  • the term network (NW) may be a gNB or a base station of any other types of base stations, such as an eNB or a base station for beyond 5G.
  • Uplink (UL) transmission of a control signal or data may be a transmission operation from a UE to a base station.
  • Downlink (DL) transmission of a control signal or data may be a transmission operation from a base station to a UE.
  • a DL control signal may comprise downlink control information (DCI) or a radio resource control (RRC) signal, from a base station to a UE.
  • DCI downlink control information
  • RRC radio resource control
  • a general functional framework of AIML is used to show the logical relationship among LCM actions.
  • a possible AI/ML functional framework is show in FIG. 2.
  • a system 100 for the disclosed method comprises units of data collection 101, model training unit 102, actor 103, and model inference 104.
  • FIG. 2 does not necessarily limit the wireless communication method to the instant example.
  • the wireless communication method is applicable to any design based on machine learning.
  • the general steps comprise data collection and/or model training and/or model inference and/or (an) actor (s) .
  • the data collection unit 101 is a function that provides input data to the model training unit 102 and the model inference unit 104.
  • AI/ML algorithm-specific data preparation e.g., data pre-processing and cleaning, formatting, and transformation
  • data pre-processing and cleaning e.g., data pre-processing and cleaning, formatting, and transformation
  • Examples of input data may include measurements from UEs or different network entities, feedback from Actor 103, and output from an AI/ML model.
  • Training data is data needed as input for the AI/ML Model training unit 102.
  • Inference data is data needed as input for the AI/ML Model inference unit 104.
  • the model training unit 102 is a function that performs the ML model training, validation, and testing.
  • the Model training unit 102 is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on training data delivered by the data collection unit 101, if required.
  • Model Deployment/Update between units 102 and 104 involves deployment or update of an AI/ML model (e.g., a trained machine learning model 105a or 105b) to the model inference unit 104.
  • the model training unit 102 uses data units as training data to train a machine learning model 105a and generates a trained machine learning model 105b from the machine learning model 105a.
  • the model inference unit 104 is a function that provides AI/ML model inference output (e.g., predictions or decisions) .
  • the AI/ML model inference output is the output of the machine learning model 105b.
  • the Model inference unit 104 is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on inference data delivered by the data collection unit 101, if required.
  • the output shown between unit 103 and unit 104 is the inference output of the AI/ML model produced by the model inference unit 104.
  • An Actor 103 is a function that receives the output from the model inference unit 104 and triggers or performs corresponding actions.
  • the actor 103 may trigger actions directed to other entities or to itself.
  • Feedback between unit 103 and unit 101 is information that may be needed to derive training or inference data or performance feedback.
  • the system 100 enables operators to train custom AI/ML models and integrate third-party AI/ML models under their control.
  • This system architecture allows network-based AI/ML services to support a wider range of partners and deliver services that meet market needs.
  • the disclosed system and methods can improve service quality and increase market competitiveness.
  • FIG. 3 shows an embodiment of the disclosed method.
  • At least one wireless communication device such as a UE 10 and a serving NG-RAN 210, executes a UE mobility management method.
  • An example of the UE e.g., the UE 10 in the description may include one of the UE 10a or UE 10b.
  • An example of the base station e.g., a source NG-RAN node 201 in FIG. 4 or a target NG-RAN node 202 in FIG. 5) in the description may include the base station 20a.
  • the source NG-RAN node 201 or target NG-RAN node may be one or more entities in a radio access network (RAN) , such as a gNB or base station.
  • RAN radio access network
  • the first radio node (e.g., source NG-RAN node) 201 transmits configuration 401 of UE-based mobility management (S001) .
  • the UE receives the configuration 401 of UE-based mobility management (S002) and transmits artificial intelligence (AI) /machine learning (ML) model-inferred one or more parameters 402 of UE-based mobility management based on the configuration to assist a handover operation of the UE (S003) .
  • the one or more parameters comprise UE-predicted UE trajectory of the UE.
  • the handover operation comprises a conditional handover (CHO) .
  • the first radio node e.g., source NG-RAN node
  • the AI/ML model-inferred one or more parameters of UE-based mobility management are transmitted in a handover request to the first radio node (i.e., the source NG-RAN node 201) .
  • the first radio node e.g., source NG-RAN node
  • the UE-predicted UE trajectory comprises a list of cells and predicted time of stay for each cell in cells surrounding the UE, the cells surrounding the UE comprise a serving cell and candidate target cells, and the candidate target cells are selected by the UE based on AI/ML model inference.
  • the UE-predicted UE trajectory is generated by the UE based on AI/ML model inference that uses historical mobility data of the UE. Transmission of the UE-predicted UE trajectory to the first radio node (e.g., source NG-RAN node) can be triggered under one of the following conditions:
  • the UE intends to update predicted UE trajectory stored in the first radio node (e.g., source NG-RAN node) by the UE-predicted UE trajectory.
  • the first radio node e.g., source NG-RAN node
  • UE performs cell measurements for one or more cells in a list of cells that surrounds the UE.
  • the UE sends a first request message (e.g., handover resource preparation request) to the first radio node (e.g., source NG-RAN node) to request radio resource of UE-suggested candidate target cells for the handover operation
  • the first request message includes a cell ID (e.g., a physical cell ID or a global cell ID) of a candidate target base station, UE-suggested candidate target cells, UE-suggested data radio bearer (DRB) for dual active protocol stack (DAPS) , and the UE-predicted UE trajectory.
  • a cell ID e.g., a physical cell ID or a global cell ID
  • DAPS dual active protocol stack
  • the first radio node sends a HO request with the UE-predicted UE trajectory to the candidate target base station.
  • the candidate target base station sends a HO request acknowledgement to the first radio node (e.g., source NG-RAN node) .
  • the first radio node e.g., source NG-RAN node
  • sends to the UE a response message e.g., RRCReconfiguration message
  • the UE may interpret the cause value to understand the reason that why the second radio node (e.g., target NG-RAN node) 202 rejects establishment of a certain QoS flow or PDU session and may use the cause value to retrain the model.
  • the UE makes a handover decision based on AI/ML model inference and sends a notification message (e.g., RRCReconfigurationComplelete message) that conveys the handover decision to the first radio node (e.g., source NG-RAN node) .
  • a notification message e.g., RRCReconfigurationComplelete message
  • the AI/ML model inference is performed based on the cell measurement
  • the UE receives assistance information for model training sent from the first radio node (e.g., source NG-RAN node) , and the assistance information includes one or more types of the following information:
  • the assistance information is broadcast or groupcast periodically.
  • the UE sends a request for the assistance information to the first radio node (e.g., source NG-RAN node) .
  • the first radio node receives a request for the assistance information from the UE and sends the assistance information in a unicast message to the UE in response to the request.
  • the UE receives the assistance information in a unicast message from the first radio node (e.g., source NG-RAN node) in response to the request.
  • a target base station provides feedback information for model retraining to the UE, the feedback information includes one or more of parameters of UE performance and parameters of UE handover failure cause.
  • the parameters of UE performance include UE quality of service (QoS) , throughput, and Maximum Bit Rate (MBR) .
  • the parameters of UE handover failure cause include “too early HO” , “too late HO” , or “HO to wrong cell” .
  • the UE transmits a UE capability report that conveys UE capability of AI/ML enhancement to the first radio node (e.g., source NG-RAN node) .
  • the UE capability of AI/ML enhancement indicates one or more of:
  • the first radio node e.g., source NG-RAN node
  • the first radio node provides and transmits configuration for each UE capability reported by the UE in UE capability report to the UE, and the configuration for each UE capability indicates:
  • the UE is enabled to transmit the UE-predicted UE trajectory to the first radio node (e.g., source NG-RAN node) ;
  • reporting of the UE-predicted UE trajectory from the UE to the first radio node is triggered based on a scheme of event triggered reporting or a scheme of a periodic reporting;
  • trigger events of the event triggered reporting comprises HO activation events or events generated by AI/ML model inference (e.g., an update of cell ID of a candidate target base station, UE-suggested candidate target cells, or the UE-predicted UE trajectory) ; and/or
  • the periodicity of the periodic reporting is configured by the first radio node (e.g., source NG-RAN node) .
  • adjusting and reporting of the parameters for UE-based mobility management from the UE to the first radio node is triggered based on a scheme of event triggered reporting.
  • Trigger events of the event triggered reporting comprises HO activation events or events generated by AI/ML model inference (e.g., an update of cell ID of a candidate target base station, UE-suggested candidate target cells, or the UE-predicted UE trajectory) .
  • Embodiment 1 predicted UE trajectory generated by UE
  • the network has the capability to generate a predicted UE trajectory based on the UE's mobility history. Subsequently, the first radio node (e.g., source NG-RAN node) transmits the predicted UE trajectory in a HO request along with the predicted time of stay for each cell to the second radio node (e.g., target NG-RAN node) .
  • the predicted UE trajectory is comprised of a list of cells and their corresponding time of stay, as defined in TS 38.423.
  • UE may generate predicted UE trajectory which may be more accurate than predicted UE trajectory generated by gNB and send predicted UE trajectory to the network, such as the serving NG-RAN node (e.g., source NG-RAN node) and/or the second radio node (e.g., target NG-RAN node) . Consequently, the UE can thus enhance UE mobility management.
  • the predicted UE trajectory generated by UE may be referred to as UE-predicted UE trajectory.
  • the UE 10 transmits the UE-predicted UE trajectory along with predicted time of stay for each cell to a first radio node (e.g., source NG-RAN node) 201.
  • the first radio node (e.g., source NG-RAN node) 201 can also be a serving NG-RAN node in an HO operation.
  • the serving NG-RAN node e.g., source NG-RAN node
  • the serving NG-RAN node can prepare the radio resources for an intra-gNB target cell of the UE (in the UE-predicted UE trajectory) or send UE-predicted UE trajectory to a second radio node (e.g., target NG-RAN node) 202.
  • a second radio node e.g., target NG-RAN node
  • the first radio node (e.g., source NG-RAN node) 201 transmits the UE-predicted UE trajectory in a HO request along with the predicted time of stay for each cell to a second radio node (e.g., target NG-RAN node) 202.
  • the UE-predicted UE trajectory is comprised of a list of cells and their corresponding predicted time of stay for each cell, as defined in TS 38.423.
  • the first radio node (e.g., source NG-RAN node) 201 sends the handover request that conveys the UE-predicted UE trajectory to the second radio node (e.g., target NG-RAN node) 202.
  • the UE-predicted UE trajectory includes a cell list and the predicted time of stay of each cell.
  • Transmission of the UE-predicted UE trajectory to the network can be triggered under one of the following conditions:
  • Embodiment 2 Procedure of UE decided handover
  • UE In the legacy HO procedure, UE (e.g., the UE 10) performs cell measurement and reports a result of the cell measurement (referred to as measurement result) to the network, such as the first radio node (e.g., source NG-RAN node) 201.
  • the network makes the HO decision.
  • the HO decision is up to network implementation, which is not standardized.
  • the UE In the legacy CHO procedure, the UE (e.g., the UE 10) also reports measurement result to the network only, such as the first radio node (e.g., source NG-RAN node) 201, and the network selects a number of candidate target cells for the UE (e.g., the UE 10) . After that, the network configures handover condition (s) to the UE (e.g., the UE 10) . When one of the conditions is satisfied, the UE detaches from a serving cell that currently serves the UE and attaches to a target cell by sending a RRCReconfigurationComplete message to the target cell to notify that the HO is completed successfully.
  • the key issue is how the network configures the cell measurement and measurement report for the UE. For example, the configuration of cell measurement specifies which cells should be measured and reported accordingly, etc.
  • TS 38.331 defines the procedure for conducting Conditional Handover (CHO) in both the user plane and control plane.
  • conditional handover is controlled by configuration (e.g., handover condition (s) and candidate target cell (s) ) .
  • a UE e.g., UE 10
  • a Conditional Handover refers to a handover executed by the User Equipment (UE) when one or more specific handover execution conditions are met.
  • the UE Upon receiving the CHO configuration from the network (e.g., a gNB) , the UE begins evaluating the execution condition (s) , and this evaluation ceases once a handover is executed.
  • the network e.g., a gNB
  • the CHO configuration comprises the configuration of CHO candidate cell (s) generated by the candidate gNB (s) (e.g., target NG-RAN node 202) and execution condition (s) generated by the first radio node (e.g., source NG-RAN node) .
  • An execution condition may include one or two trigger conditions (CHO events A3/A5, as defined in the 3GPP standards) . Only a single Reference Signal (RS) type is supported in the evaluation, and a maximum of two different trigger quantities (e.g., RSRP and RSRQ, RSRP and SINR, etc. ) can be configured simultaneously for evaluating the CHO execution condition of a single candidate cell.
  • RS Reference Signal
  • UE Before any CHO execution condition is satisfied, if the UE receives a Handover (HO) command (without CHO configuration) , it shall execute the HO procedure as described in TS 38.331 clause 9.2.3.2, disregarding any previously received CHO configuration.
  • HO Handover
  • N-C Next-Generation Core
  • both the preparation and execution phases of a conditional handover procedure occur without the direct involvement of the 5G Core (5GC) .
  • the preparation messages are directly exchanged between gNBs (base stations) .
  • the second radio node e.g., target NG-RAN node, i.e., the gNB to which the User Equipment (UE) is being handed over
  • the first radio node e.g., source NG-RAN node
  • RAN3 has introduced the inclusion of predicted trajectory within a handover request message over the Xn interface.
  • AI/ML Artificial Intelligence/Machine Learning
  • the Next-Generation Radio Access Network is the radio access network for 5G networks.
  • An NG-RAN node can be split into an NG-RAN centralized unit and one or more NG-RAN distributed units.
  • the NG-RAN access node establishes an association with the access and mobility management function (AMF) .
  • An NG-RAN may comprise a base station in the description.
  • a first radio node e.g., source NG-RAN node
  • may transmit a handover request i.e., a HANDOVER REQUEST message
  • a second radio node e.g., target NG-RAN node
  • This HANDOVER REQUEST message is sent by the first radio node (e.g., source NG-RAN node) to the second radio node (e.g., target NG-RAN node) to request the preparation of resources for a handover.
  • the second radio node e.g., target NG-RAN node
  • a handover request acknowledge i.e., a HANDOVER REQUEST ACKNOWLEDGE message
  • the second radio node e.g., target NG-RAN node
  • the first radio node e.g., source NG-RAN node
  • the second radio node e.g., target NG-RAN node
  • UE-decided HO scheme is utilized.
  • UE e.g., the UE 10
  • makes HO decisions for a handover operation e.g., the UE 10.
  • the UE selects a candidate target cell, and the criteria for selecting candidate target cell is up to UE decision.
  • the UE makes HO decision to select one candidate target cell from the candidate target cells.
  • the UE When UE has selected candidate target cell, the UE notifies the network regarding the candidate target cell (s) .
  • the first radio node (e.g., source NG-RAN node) 201 prepares the radio resources for the handover to the candidate second radio node (e.g., target NG-RAN node) 202s.
  • the first radio node (e.g., source NG-RAN node) 201 configures the UE with the radio resources for handover.
  • the UE detaches from the serving cell and attaches to the target cell.
  • the UE thus enhance a legacy CHO procedure.
  • DAPS Dual Active Protocol Stack
  • the UE e.g., UE 10 performs the following operations to enhance HO.
  • Step S101 The UE performs cell measurements for one or more cells in the list of cells that surrounds the UE; however, the UE will not report the measurement results as the handover decision is solely determined by the UE.
  • Step S102 The UE determines UE-predicted UE trajectory based on AI/ML model inferencing.
  • the UE sends a HO resource preparation request to the first radio node (e.g., source NG-RAN node) 201 to request radio resource for handover.
  • the HO resource preparation request includes physical cell ID (s) of selected candidate target NG-RAN node (s) (e.g., a physical cell ID of a candidate target NG-RAN node 202) and UE-predicted UE trajectory.
  • Step S103 The first radio node (e.g., source NG-RAN node) 201 sends a HO request with the UE-predicted UE trajectory to the second radio node (e.g., target NG-RAN node) 202.
  • the first radio node e.g., source NG-RAN node
  • the second radio node e.g., target NG-RAN node
  • Step S104 The second radio node (e.g., target NG-RAN node) 202 sends a HO request acknowledgement to the first radio node (e.g., source NG-RAN node) 201 as usual.
  • the first radio node e.g., source NG-RAN node
  • Step S105 The first radio node (e.g., source NG-RAN node) 201 sends to the UE a RRCReconfiguration message that conveys acknowledgement of the first request message (i.e., HO resource preparation request ack) , a cause value, and configuration of each resource block (RB) accepted by the second radio node (e.g., target NG-RAN node) 202 in step S104. If not accepting any HO resource preparation request, the first radio node (e.g., source NG-RAN node) 201 forwards the cause value of each unaccepted QoS flow and PDU session to the UE.
  • a RRCReconfiguration message that conveys acknowledgement of the first request message (i.e., HO resource preparation request ack) , a cause value, and configuration of each resource block (RB) accepted by the second radio node (e.g., target NG-RAN node) 202 in step S104. If not accepting any HO resource preparation request, the first radio node
  • Step S106 The UE makes a handover decision.
  • Step S107 The UE sends a RRCReconfiguration message to the second NG-RAN node 202, notify the network of the handover decision.
  • the RRCReconfiguration message conveys the handover decision.
  • the UE operation thus enhances a conditional handover procedure.
  • the UE e.g., UE 10
  • the first radio node e.g., source NG-RAN node
  • the network including source NG-RAN node 201 has to prepare radio resources in the candidate target cell C1, C2, and C3.
  • the UE selects candidate target cell in a HO decision based on AI/ML model inferencing. For example, the UE can only select candidate target cell C1 and C2 as the UE is moving to the south, because UE knows its trajectory better than the network, and radio resource of the candidate target cell C3 can be saved.
  • Embodiment 3 assistant information provided by network.
  • the model training should collect data for the purpose of model training.
  • the network can provide assistance information to the UE.
  • Network e.g., the source NG-RAN node 201 or the target NG-RAN node 202 can provide assistance information to the UE for model training on the UE side and to assist the UE to make a right handover decision.
  • the assistance information is detailed in the following:
  • the assistance information sent from the network (e.g., the source NG-RAN node 201 or the target NG-RAN node 202) to the UE (e.g., the UE 10) can include one or more types of the following information:
  • the assistance information can be sent from the network (e.g., the source NG-RAN node 201 or the target NG-RAN node 202) to the UE (e.g., the UE 10) periodically through broadcast or groupcast.
  • the network sends the assistance information in a system information block (SIB) to the UE so that the UE uses the assistance information from time to time for model training.
  • SIB system information block
  • Embodiment 4 UE trajectory/performance feedback from the network
  • the second radio node (e.g., target NG-RAN node) 202 provides feedback information to the UE to help the UE re-train the model.
  • the feedback information may be used as the feedback in system 100.
  • the feedback information from the network includes one or more of the following parameters:
  • the UE performance for example, includes UE quality of service (QoS) , throughput, or Maximum Bit Rate (MBR) ; and
  • the UE handover failure cause for example, includes “too early HO” , “too late HO” , or “HO to wrong cell” .
  • Embodiment 5 configuration of UE-based mobility management.
  • the UE behavior should be controlled by the network.
  • the network should be able to configure the UE behavior regarding e.g., whether the UE is allowed to infer parameters of the UE-based mobility management and whether the UE is allowed/requested to report the inferred parameters of UE-based mobility management.
  • the UE transmits a UE capability report that conveys UE capability of AI/ML enhancement to the network (e.g., the source NG-RAN node 201) (S301) .
  • the UE capability of AI/ML enhancement indicates one or more of:
  • the network receives the UE capability report. Based on the UE capability report, the network (e.g., the source NG-RAN node 201) provides and transmits configuration for each UE capability reported by the UE in UE capability report to the UE (S302) .
  • the network configures the UE in the configuration regarding:
  • the UE is enabled to transmit UE-predicted UE trajectory to the first radio node (e.g., source NG-RAN node) ;
  • the reporting of the UE-predicted UE trajectory from the UE may be triggered based on a scheme of event triggered reporting or a scheme of a periodic reporting.
  • Trigger events of the event triggered reporting may comprise HO activation events or events generated by AI/ML model inference.
  • the first radio node (e.g., source NG-RAN node) 201 may configure periodicity of the periodic reporting.
  • the parameters for UE-based mobility management can be changed by UE autonomously. When one or more of handover execution conditions are satisfied, the UE will execute the handover. These parameters are defined in TS38.331. For example, the parameters comprise what is listed in the following table.
  • Trigger events of the adjusting and reporting of the parameters for UE-based mobility management to the first radio node (e.g., source NG-RAN node) 201 may comprise HO activation events or events generated by AI/ML model inference.
  • FIG. 9 is a block diagram of an example system 700 for wireless communication according to an embodiment of the present disclosure. Embodiments described herein may be implemented into the system using any suitably configured hardware and/or software.
  • FIG. 9 illustrates the system 700 including a radio frequency (RF) circuitry 710, a baseband circuitry 720, a processing unit 730, a memory/storage 740, a display 750, a camera 760, a sensor 770, and an input/output (I/O) interface 780, coupled with each other as illustrated.
  • RF radio frequency
  • the processing unit 730 may include circuitry, such as, but not limited to, one or more single-core or multi-core processors.
  • the processors may include any combinations of general-purpose processors and dedicated processors, such as graphics processors and application processors.
  • the processors may be coupled with the memory/storage and configured to execute instructions stored in the memory/storage to enable various applications and/or operating systems running on the system.
  • the radio control functions may include, but are not limited to, signal modulation, encoding, decoding, radio frequency shifting, etc.
  • the baseband circuitry may provide for communication compatible with one or more radio technologies.
  • the baseband circuitry may support communication with 5G NR, LTE, an evolved universal terrestrial radio access network (EUTRAN) and/or other wireless metropolitan area networks (WMAN) , a wireless local area network (WLAN) , a wireless personal area network (WPAN) .
  • EUTRAN evolved universal terrestrial radio access network
  • WMAN wireless metropolitan area networks
  • WLAN wireless local area network
  • WPAN wireless personal area network
  • Embodiments in which the baseband circuitry is configured to support radio communications of more than one wireless protocol may be referred to as multi-mode baseband circuitry.
  • the baseband circuitry 720 may include circuitry to operate with signals that are not strictly considered as being in a baseband frequency.
  • baseband circuitry may include circuitry to operate with signals having an intermediate frequency, which is between a baseband frequency and a radio frequency.
  • the system 700 may be a mobile computing device such as, but not limited to, a laptop computing device, a tablet computing device, a netbook, an ultrabook, a smartphone, etc.
  • the system may have more or less components, and/or different architectures.
  • the methods described herein may be implemented as a computer program.
  • the computer program may be stored on a storage medium, such as a non-transitory storage medium.
  • the embodiment of the present disclosure is a combination of techniques/processes that can be adopted in 3GPP specification to create an end product.
  • the software function unit is realized and used and sold as a product, it can be stored in a readable storage medium in a computer.
  • the technical plan proposed by the present disclosure can be essentially or partially realized as the form of a software product.
  • one part of the technical plan beneficial to the conventional technology can be realized as the form of a software product.
  • the software product in the computer is stored in a storage medium, including a plurality of commands for a computational device (such as a personal computer, a server, or a network device) to run all or some of the steps disclosed by the embodiments of the present disclosure.
  • the storage medium includes a USB disk, a mobile hard disk, a read-only memory (ROM) , a random-access memory (RAM) , a floppy disk, or other kinds of media capable of storing program codes.
  • Embodiments of the disclosure describe a comprehensive method for UE handover operation.
  • the disclosure enhances AI/ML models for wireless communication systems.
  • the disclosed feasible procedures, signaling, and corresponding elements can conserve network resources efficiently and reduce signaling overhead.
  • the disclosed method enhances UE mobility management and facilitates faster handover (HO) and accurate HO.
  • ⁇ Faster HO In conventional handover, the UE should report the measurement report to the network, and then the network makes handover decision. In some embodiments of the disclosure, UE make the decision for handover, and the handover will be conducted faster than conventional handover.
  • UE should transmit UE history information regarding locations or movements of the UE to a second radio node (e.g., target NG-RAN node) .
  • handover is performed by the UE. Since UE has the UE history information, it is easier to perform a handover to the right cell.
  • UE performs AI/ML model training and model inferencing to enhance UE mobility management.
  • the UE is enabled to infer the UE-predicted UE trajectory using AI/ML model inferencing and report the UE-predicted UE trajectory to the network, by which the network can have a more accurate predicted UE trajectory;
  • the network provides some kinds of information to the UE to assist the UE in AI/ML model training and handover decision.
  • the second radio node (e.g., target NG-RAN node) provides feedback information to the UE to help the UE re-train the model.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne un procédé de gestion de mobilité d'équipement utilisateur (UE) exécutable par un UE. L'UE reçoit une configuration de gestion de mobilité basée sur un UE et transmet un ou plusieurs paramètres d'intelligence artificielle (IA)/d'apprentissage automatique (ML) déduits d'un ou de plusieurs paramètres de gestion de mobilité basée sur un UE sur la base de la configuration pour aider une opération de transfert intercellulaire de l'UE. Le ou les paramètres comprennent une trajectoire d'UE prédite par l'UE de l'UE.
PCT/CN2023/112982 2023-08-14 2023-08-14 Procédé de gestion de mobilité d'ue, équipement utilisateur et station de base Pending WO2025035367A1 (fr)

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CN202380100931.6A CN121646951A (zh) 2023-08-14 2023-08-14 用户设备移动性管理方法、用户设备和基站
PCT/CN2023/112982 WO2025035367A1 (fr) 2023-08-14 2023-08-14 Procédé de gestion de mobilité d'ue, équipement utilisateur et station de base

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WO2025212285A1 (fr) * 2024-04-01 2025-10-09 Interdigital Patent Holdings, Inc. Procédés de prédiction et de rapport de trajectoire d'équipement utilisateur
CN120730406A (zh) * 2025-08-14 2025-09-30 荣耀终端股份有限公司 通信方法、装置、系统、计算机程序产品及可读存储介质

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