WO2024207243A1 - 一种通信方法、装置及存储介质 - Google Patents

一种通信方法、装置及存储介质 Download PDF

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Publication number
WO2024207243A1
WO2024207243A1 PCT/CN2023/086333 CN2023086333W WO2024207243A1 WO 2024207243 A1 WO2024207243 A1 WO 2024207243A1 CN 2023086333 W CN2023086333 W CN 2023086333W WO 2024207243 A1 WO2024207243 A1 WO 2024207243A1
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Prior art keywords
information
terminal
model
network device
rrc
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PCT/CN2023/086333
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English (en)
French (fr)
Inventor
李明菊
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Beijing Xiaomi Mobile Software Co Ltd
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Beijing Xiaomi Mobile Software Co Ltd
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Priority to CN202380008944.0A priority Critical patent/CN116889015A/zh
Priority to PCT/CN2023/086333 priority patent/WO2024207243A1/zh
Priority to EP23931315.8A priority patent/EP4694281A4/en
Publication of WO2024207243A1 publication Critical patent/WO2024207243A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H04B7/06952—Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • the present disclosure relates to the field of communication technology, and in particular to a communication method, device and storage medium.
  • AI artificial intelligence
  • a research project on artificial intelligence technology in wireless air interface was established in the 3rd Generation Partnership Project (3GPP).
  • the project aims to study the introduction of AI technology in wireless air interface and explore how AI technology can assist in improving the transmission technology of wireless air interface.
  • beam prediction based on AI model can reduce the number of beam pairs measured by the terminal.
  • the present disclosure provides a communication method, device and storage medium.
  • a communication method which is executed by a terminal, and includes receiving information sent by a network device, wherein the information is used by the terminal to manage an artificial intelligence (AI) model.
  • AI artificial intelligence
  • managing the AI model includes: operating at least one AI function indicated in the information or operating at least one model of the same AI function.
  • the information includes parameter information, and different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: an urban macro cell, an urban micro cell, an indoor hotspot, a dense city, and a rural area.
  • the network device coverage parameter information includes the distance between network devices.
  • the terminal distribution information includes at least one of the following information:
  • the ratio between the number of outdoor terminals and the number of indoor terminals is the ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set; the first set and the second set include a network device transmitting beam and/or a terminal receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set; the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set; the beam information between the first set and the second set includes position information, and the position information is the position of the beam included in the first set in the beam included in the second set.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is a beam set corresponding to an AI model input value
  • the second set is a beam set corresponding to a model output.
  • the beam information includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes the information of the target cell
  • the target cell is the target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal performs AI model management based on the information in the RRC inactive state or the RRC idle state.
  • the AI model is used for beam prediction.
  • a communication method which is executed by a network device, and the method includes: sending information to a terminal, wherein the information is used by the terminal to manage an artificial intelligence (AI) model.
  • AI artificial intelligence
  • managing the AI model includes: operating at least one AI function indicated in the information or operating at least one model of the same AI function.
  • the information includes parameter information, and different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: an urban macro cell, an urban micro cell, an indoor hotspot, a dense city, and a rural area.
  • the network device coverage parameter information includes the distance between network devices.
  • the terminal distribution information includes at least one of the following information:
  • the ratio between the number of outdoor terminals and the number of indoor terminals is the ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set; the first set and the second set include a network device transmitting beam and/or a terminal device receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set, and the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set; the beam information between the first set and the second set includes position information, and the position information is the position of the beam included in the first set in the beam included in the second set.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is a beam set corresponding to an AI model input value
  • the second set is a beam set corresponding to a model output.
  • the beam information includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes the information of the target cell
  • the target cell is the target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal is in the RRC inactive state or the RRC idle state based on the signal AI model management based on information.
  • the AI model is used for beam prediction.
  • a communication device including: a receiving unit, configured to receive information sent by a network device, wherein the information is used for the terminal to manage an artificial intelligence (AI) model.
  • AI artificial intelligence
  • managing the AI model includes: operating at least one AI function indicated in the information or operating at least one model of the same AI function.
  • different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: an urban macro cell, an urban micro cell, an indoor hotspot, a dense city, and a rural area.
  • the network device coverage parameter information includes the distance between network devices.
  • the terminal distribution information includes at least one of the following information:
  • the ratio between the number of outdoor terminals and the number of indoor terminals is the ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set; the first set and the second set include a network device transmitting beam and/or a terminal device receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set; the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set; the beam information between the first set and the second set includes position information, and the position information is the position of the beam included in the first set in the beam included in the second set.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is a beam set corresponding to an AI model input value
  • the second set is a beam set corresponding to a model output.
  • the beam information includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes the information of the target cell
  • the target cell is the target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal performs AI model management based on the information in the RRC inactive state or the RRC idle state.
  • the AI model is used for beam prediction.
  • a communication device including: a sending unit, used to send information to a terminal, wherein the information is used by the terminal to manage an artificial intelligence (AI) model.
  • AI artificial intelligence
  • managing the AI model includes: operating at least one AI function indicated in the information or operating at least one model of the same AI function.
  • different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: an urban macro cell, an urban micro cell, an indoor hotspot, a dense city, and a rural area.
  • the network device coverage parameter information includes the distance between network devices.
  • the terminal distribution information includes at least one of the following information:
  • the ratio between the number of outdoor terminals and the number of indoor terminals is the ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set; the first set and the second set include a network device transmitting beam and/or a terminal device receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set; the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set;
  • the beam information includes position information, where the position information is the position of the beam included in the first set among the beams included in the second set.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is a beam set corresponding to an AI model input value
  • the second set is a beam set corresponding to a model output.
  • the beam information includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes the information of the target cell
  • the target cell is the target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal performs AI model management based on the information in the RRC inactive state or the RRC idle state.
  • the AI model is used for beam prediction.
  • a communication device including: a processor;
  • a memory for storing processor-executable instructions
  • the processor is configured to: execute the communication method described in the first aspect or any one of the implementations of the first aspect.
  • a communication device including: a processor;
  • a memory for storing processor-executable instructions
  • the processor is configured as the communication method described in the second aspect or any one of the embodiments of the second aspect.
  • a storage medium in which instructions are stored.
  • the instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to execute the communication method described in the first aspect or any one of the embodiments of the first aspect.
  • a storage medium in which instructions are stored.
  • the instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to execute the communication method described in the second aspect or any one of the embodiments of the second aspect.
  • the technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: based on the determination of the specific content of the information used by the terminal to manage the AI model and the determination of the carrying method, the determination of the data transmission method in the process of model management of the AI function is realized.
  • Fig. 1 is a schematic diagram showing a wireless communication system according to an exemplary embodiment.
  • Fig. 2 is a flow chart showing a communication method according to an exemplary embodiment.
  • Fig. 3 is a flow chart showing a communication method according to an exemplary embodiment.
  • Fig. 4 is a structural block diagram showing a communication device according to an exemplary embodiment.
  • Fig. 5 is a structural block diagram showing a communication device according to an exemplary embodiment.
  • Fig. 6 is a schematic diagram showing the architecture of a communication system according to an exemplary embodiment.
  • Fig. 7 is a block diagram of a device for communication according to an exemplary embodiment.
  • Fig. 8 is a block diagram of a device for communication according to an exemplary embodiment.
  • the wireless communication system includes a network device and a terminal.
  • the terminal is connected to the network device through wireless resources and performs data transmission.
  • the wireless communication system shown in FIG1 is only for schematic illustration, and the wireless communication system may also include other network devices, such as core network devices, wireless relay devices, and wireless backhaul devices, which are not shown in FIG1.
  • the embodiments of the present disclosure do not limit the number of network devices and terminals included in the wireless communication system.
  • the wireless communication system of the embodiment of the present disclosure is a network that provides wireless communication functions.
  • the wireless communication system can adopt different communication technologies, such as code division multiple access (code division multiple access, CDMA), wideband code division multiple access (wideband code division multiple access, WCDMA), time division multiple access (time division multiple access, TDMA), frequency division multiple access (frequency division multiple access, FDMA), orthogonal frequency division multiple access (orthogonal frequency-division multiple access, OFDMA), single carrier frequency division multiple access (single carrier FDMA, SC-FDMA), carrier sense multiple access/collision avoidance (Carrier Sense Multiple Access with Collision Avoidance).
  • code division multiple access code division multiple access
  • CDMA code division multiple access
  • wideband code division multiple access wideband code division multiple access
  • WCDMA wideband code division multiple access
  • time division multiple access time division multiple access
  • FDMA frequency division multiple access
  • OFDMA orthogonal frequency division multiple access
  • single carrier frequency division multiple access single carrier frequency division multiple access
  • the network can be divided into 2G (English: generation) network, 3G network, 4G network or future evolution network, such as 5G network, 5G network can also be called new wireless network (New Radio, NR).
  • 2G English: generation
  • 3G network 4G network or future evolution network, such as 5G network
  • 5G network can also be called new wireless network (New Radio, NR).
  • NR New Radio
  • the present disclosure sometimes simply refers to a wireless communication network as a network.
  • the wireless access network device may also be referred to as a wireless access network device.
  • the wireless access network device may be: a base station, an evolved node B (base station), a home base station, an access point (AP) in a wireless fidelity (WIFI) system, a wireless relay node, a wireless backhaul node, a transmission point (TP) or a transmission and reception point (TRP), etc. It may also be a gNB in an NR system, or it may also be a component or a part of a base station. It should be understood that in the embodiments of the present disclosure, the specific technology and specific device form adopted by the network device are not limited.
  • the network device may provide communication coverage for a specific geographical area, and may communicate with a terminal located in the coverage area (cell).
  • the network device may also be a vehicle-mounted device.
  • the terminal involved in the present disclosure may also be referred to as a terminal device, a user equipment (User Equipment, UE), a mobile station (Mobile Station, MS), a mobile terminal (Mobile Terminal, MT), etc., which is a device that provides voice and/or data connectivity to users.
  • the terminal may be a handheld device with a wireless connection function, a vehicle-mounted device, etc.
  • terminals are: a smart phone (Mobile Phone), a customer premises equipment (Customer Premise Equipment, CPE), a pocket computer (Pocket Personal Computer, PPC), a handheld computer, a personal digital assistant (Personal Digital Assistant, PDA), a laptop computer, a tablet computer, a wearable device, or a vehicle-mounted device, etc.
  • the terminal device may also be a vehicle-mounted device.
  • V2X vehicle-to-everything
  • the network equipment will configure a reference signal resource set for beam measurement.
  • the terminal will measure the reference signal resources in the reference signal resource set and then report the X reference signal resource IDs with the corresponding L1-RSRP and/or L1-SINR.
  • the problem with the traditional method is that the reference signal resources configured by the base station are The set contains X reference signals, each of which corresponds to a different transmit beam of the base station.
  • the terminal needs to use multiple receive beams to measure the reference signal, obtain the beam measurement qualities corresponding to the multiple receive beams, and determine the best beam measurement quality.
  • the maximum number of beam pairs that the terminal needs to measure is M*N, where M is the number of transmit beams of the base station and N is the number of receive beams of the terminal. In this way, the terminal needs to measure a large number of beams. It is necessary to reduce the number of beam pairs measured by the terminal.
  • AI artificial intelligence
  • the total number of beam pairs that the terminal needs to measure is M*N (where M is the number of beams sent by the base station and N is the number of beams received by the terminal).
  • M the number of beams sent by the base station
  • N the number of beams received by the terminal.
  • the terminal for spatial beam prediction, the terminal only needs to measure a part of the M*N beam pairs, such as 1/8, 1/4, etc., and then input the measured beam measurement quality of these beam pairs into the AI model, and the model can output the beam information of the M*N beam pairs.
  • the terminal can measure the beam quality of beam pairs at historical times to predict the beam information of beam pairs at future times.
  • AI-based prediction of beam measurement results has been proposed, including spatial beam prediction and time-domain beam prediction.
  • spatial beam prediction predicts the measurement results of the beam in setA based on the measurement results of the beam in set B.
  • Time-domain beam prediction Based on the measurement results of set B in historical time, predict the beam of setA in the future time.
  • set B is a subset of setA, or set B is wide beam and setA is narrow beam, or the time-domain beam prediction also includes a set B that is the same as setA.
  • the terminal measures the L1-RSRP of set B, inputs it into the AI model, and predicts the L1-RSRP of set A or the best beam in set A.
  • the relationship between set B and set A includes the following two types:
  • Set B is a wide beam and set A is a narrow beam.
  • set A contains 32 reference signals (each reference signal corresponds to a beam direction, and the 32 reference signals cover a 120-degree direction).
  • the terminal measures the L1-RSRP of set B at historical time, inputs it into the AI model, and predicts the L1-RSRP of set A at future time.
  • the terminal measures the L1-RSRP of set B at historical time, inputs it into the AI model, and predicts the L1-RSRP of set A at future time.
  • set B is the same as set A.
  • the reference signal at the future time can be omitted, and the beam information is obtained based on the AI model output and reported to the base station.
  • the reference signal at the future time also needs to be sent, and the terminal measures the reference signal at the future time and obtains the beam information and reports it to the base station.
  • the management of AI models is implemented based on two management methods, including: based on AI functions and model identification.
  • the principle of managing models based on AI functions can be understood as follows: the terminal only needs to inform the network device which AI functions the terminal supports. Then the activation, deactivation and switching of AI functions require the terminal to interact with the network device, and the network device needs to decide or the terminal decides and informs the network device. However, under a certain AI function, the terminal can maintain one or more AI models. If there are multiple AI models, the terminal can switch between different models under an AI function by itself, without the need for the network device to decide or the terminal to decide and inform the network device.
  • Management based on model identification can be understood as follows: the terminal needs to inform the network device which models corresponding to the model identifications supported by the terminal, so the activation, deactivation and switching of AI models require the terminal to interact with the network device, and the network device needs to decide or the terminal decides and informs the network device.
  • the terminal may be pre-deployed with more than one model with the same AI function. Therefore, in the case where the terminal is managed based on the AI function model, if you want to switch between different AI functions, you need to interact with the network device to trigger the switching performance indicators and switching decisions. However, if you want to switch or select different models under the same AI function, the terminal only needs to determine which model to select from the multiple models under the AI function based on some current information. And some of the current information requires the network device to send some auxiliary information to support the terminal's switching or selection of different models under the same AI function. However, what specific information these auxiliary information includes and how to send it needs to be determined first.
  • the embodiments of the present disclosure provide a method for managing models based on the same AI function, determining the auxiliary information used and the method for sending the auxiliary information.
  • FIG2 is a flow chart of a communication method according to an exemplary embodiment. As shown in FIG2 , the communication method is executed by a terminal and includes the following steps:
  • step S11 information sent by the network device is received, and the information is used for terminal management AI model.
  • the AI model managed by the terminal is used for beam prediction.
  • the AI model deployed on the terminal is managed based on the information sent by the network device.
  • the type of information may be system information, etc.
  • the specific information content may be corresponding parameter information, etc.
  • the information sent by the network device for the terminal to manage the AI model can be used as auxiliary information.
  • the auxiliary information sent by the network device to support the terminal to switch or use different models under the same function is expressed as information sent by the network device for the terminal to manage the AI model, also referred to as information.
  • models corresponding to the same AI function are managed. For example, a first model having a spatial beam prediction function is switched to a second model having a spatial beam prediction function, and so on.
  • the terminal can determine the management content for the AI model deployed on the terminal through the information sent by the network device.
  • the managing the AI model includes: operating at least one AI function indicated in the information or operating at least one model of the same AI function;
  • Operating at least one AI function indicated in the information includes at least one of activation, deactivation, and switching between different AI functions of the AI function; operating at least one model of the same AI function includes at least one of activation, deactivation, and switching between different models of the model.
  • operating at least one AI function indicated in the information may include: managing and/or operating at least one AI function including at least one of activation, deactivation, and switching between different AI functions of the AI function.
  • Operating at least one model of the same AI function indicated in the information may include: managing and/or operating at least one model included in the same AI function including at least one of activation, deactivation, and switching between different models of the model.
  • the network device may manage the model using the function corresponding to the model, and the terminal may manage the model using the model identifier corresponding to the model. Therefore, the management of the model is carried out from two aspects: management at the functional level and management at the model level. Management at the functional level may include at least one of the following: activation of at least one AI function, deactivation of at least one AI function, and switching between different AI functions. For example: if the requirement is spatial beam prediction, the AI function corresponding to the spatial beam prediction is enabled, that is, the spatial beam prediction AI function is activated. As for which model corresponds to the specific activated spatial beam prediction AI function, the network device may not indicate.
  • the network device can send corresponding indication information to the terminal, so that the terminal deactivates the AI function corresponding to the activated spatial domain beam prediction, and then activates the AI function corresponding to the time domain beam prediction, that is, switching between different AI functions is achieved.
  • the management at the model level may include at least one of the following: managing the activation of at least one model included in the same AI function, the deactivation of at least one model, and the switching of different models of the same function. For example: If the demand is for spatial beam prediction, and the scenario at this time is a macro cell, the first AI model of the macro cell corresponding to the spatial beam prediction AI function is activated. Further, the network device can send corresponding indication information to the terminal, so that the terminal activates the first AI model of the macro cell corresponding to the spatial beam prediction AI function when the scenario is a macro cell.
  • the network device may send corresponding indication information to the terminal, so that the terminal activates the second AI model of the microcell corresponding to the spatial beam prediction AI function when the scenario is a microcell scenario.
  • the communication method provided in this embodiment defines the specific content of AI model management from two aspects: management at the functional level and management at the model level, making subsequent communication methods clearer.
  • the terminal receives information sent by the network device, including parameter information, and different functions and/or different models correspond to different parameter information.
  • the parameter information corresponding to the model with the spatial beam prediction function (referred to as the first parameter information here for ease of understanding) is used to indicate the applicable parameters, assumptions and/or scenarios corresponding to the spatial beam prediction function. That is, the first parameter information used to indicate the spatial beam prediction function may include parameters, assumptions and/or scenarios.
  • the parameter information corresponding to the model with the time domain beam prediction function (referred to as the second parameter information here for ease of understanding) is used to indicate the applicable parameters, assumptions and/or scenarios corresponding to the time domain beam prediction function. That is, the second parameter information used to indicate the time domain beam prediction function may include parameters, assumptions and/or scenarios.
  • the second parameter information corresponding to the time domain beam prediction function is not completely the same as the first parameter information corresponding to the spatial domain beam prediction function.
  • the parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • different AI functions correspond to different parameter information.
  • the parameter information corresponding to different AI functions is independent of each other and is not exactly the same.
  • any two AI functions among different AI functions are called the first AI function and the second AI function.
  • different models under the same AI function correspond to different parameter information.
  • any two different models among different models are called the first model and the second model.
  • the first AI function and the first model can be used alternately, and the second AI function and the second model can be used alternately.
  • the first function and the second function involved in the following embodiments of the present disclosure can be replaced by the first model and the second model.
  • the first model and the second model can be replaced by the first function and the second function.
  • At least one of the following is different between the first AI function and the second AI function: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the first AI function and the second AI function correspond to different network deployment types
  • the distances between network devices corresponding to the first AI function and the second AI function are different;
  • the terminals corresponding to the first AI function and the second AI function are distributed differently;
  • the beam information corresponding to the first AI function and the second AI function is different.
  • the first AI function and the second AI function may include different parameter items.
  • the parameter information corresponding to the first AI function includes network device coverage parameter information and cell identification information.
  • the parameter information corresponding to the second AI function may include network device coverage parameter information and terminal distribution information.
  • the network device coverage parameter includes at least one of the deployment type of the network device and the distance between the network devices.
  • the deployment type of the network equipment includes at least one of the following: urban macro (Urban macro, Uma), urban micro (Urban micro, Umi), indoor hotspot (indoor), dense urban (dense urban) and rural (rural).
  • the network deployment types corresponding to the first AI function and the second AI function are different.
  • the first AI function corresponds to a macro cell
  • the second AI function corresponds to a micro cell. Then, in the macro cell scenario, the terminal activates the first AI function; in the micro cell scenario, the terminal activates the second AI function.
  • a macro cell is also called a macro cellular cell, that is, a cell using cellular technology is called a macro cellular cell, or a macro cell.
  • An urban macro cell is a macro cell located in an urban area.
  • microcell is a technology developed on the basis of macrocell, and is used to eliminate the "blind spot" in macrocell.
  • Urban microcell is a microcell set up in the urban area.
  • the deployment type of the network device when used as parameter information, only one item may be involved, or a combination of the items may exist.
  • the parameter information includes the deployment of device A in a macro cell in an urban area and the deployment in a rural area.
  • the distance between network devices is also referred to as the inter-site distance (ISD) between base stations.
  • ISD inter-site distance
  • the specific numerical setting of the distance between network devices should be determined in combination with the deployment type selected by the corresponding network devices.
  • the corresponding network device distance can be arranged as 100m, 200m, 500m, 1000m, etc.
  • the ISDs corresponding to the first AI function and the second AI function are different.
  • the first AI function corresponds to an ISD value of 200m
  • the second AI function corresponds to an ISD value of 500m. Then, in the scenario of the ISD value of 200m, the terminal activates the first AI function; in the scenario of the ISD value of 500m, the terminal activates the second AI function.
  • the network device coverage parameter information may include the type of network device deployment and the distance between network devices.
  • the network device is deployed based on the urban macro cell mode, and the ISD is 500m.
  • the cell identification information in the network device coverage parameter information includes at least one of a serving cell identification and a neighboring cell identification.
  • the cell identification information is an identification used to characterize the cell identity, for example, it can be defined as A cell, B cell, and C cell.
  • the serving cell As cell A, it is known that the cells adjacent to cell A are cell B, cell C, and cell D. Then the network device coverage parameter information needs to include at least one of cell A, cell B, cell C, and cell D.
  • the communication method provided in this embodiment makes the subsequent communication method clearer by defining the deployment type corresponding to the network devices and the spacing information between the network devices.
  • the terminal distribution information includes the number of outdoor terminals.
  • the terminal distribution information includes the number of indoor terminals.
  • the terminal distribution information includes the ratio of the number of outdoor terminals to the number of indoor terminals.
  • the terminal distribution information may be the ratio of the number of indoor terminals to the number of outdoor terminals.
  • the number of terminals distributed indoors and outdoors and the corresponding ratio information may be obtained by statistics of the number of terminals connected to the network within the target range through relevant equipment.
  • the number of indoor terminals is 100 and the number of outdoor terminals is 200, and the ratio of the number of outdoor terminals to the number of indoor terminals is 2:1.
  • all the terminals in the current area are outdoor terminals, or the ratio of the number of outdoor terminals to the number of indoor terminals in the current area is 4:1, etc.
  • the communication method provided in this embodiment makes the subsequent communication method clearer by defining the terminal distribution information.
  • the beam information includes: beam information between the first set and the second set.
  • the first set is a beam set corresponding to the AI model input value
  • the second set is a beam set corresponding to the model output.
  • the beam information includes a beam type, and the beam type includes a discrete Fourier transform (DFT) beam or a non-DFT beam.
  • DFT discrete Fourier transform
  • the beam types corresponding to the first AI function and the second AI function are different.
  • the first AI function corresponds to the DFT beam
  • the second AI function corresponds to the non-DFT beam. Then, in the DFT beam scenario, the terminal activates the first AI function; in the non-DFT beam scenario, the terminal activates the second AI function.
  • the beam type defines that the applicable type of the beam is all types of beams (i.e., DFT beams or non-DFT beams). Then the corresponding first set beam information, second set beam information, and beam information between the first set and the second set are also applicable to all types of beams. The beam information of the first set, the second set, and the beam information between the first and second sets will not be described in detail later.
  • the beam information between the first set and the second set includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the beam information of the first set includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the beam information of the second set includes a beam type, and the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the first set includes beams sent by the network device and/or beams received by the terminal device.
  • the second set includes beams sent by the network device and/or beams received by the terminal device.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set; the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the input set for the beam prediction model that is, the number of beams corresponding to the first set includes: the number of beams sent by the network device and/or the number of beams received by the terminal device.
  • different AI functions/models correspond to different beam numbers. Any two different numbers among the different beam numbers are referred to as the first beam number and the second beam number.
  • different AI functions/models correspond to second sets of different beam numbers.
  • the first model corresponds to the first set of the first beam number
  • the second model corresponds to the first set of the second beam number.
  • the output set for the beam prediction model that is, the number of beams corresponding to the second set includes: the number of beams sent by the network device and/or the number of beams received by the terminal device.
  • the number of beams is 32.
  • different AI functions/models correspond to second sets of different beam numbers.
  • the first model corresponds to the second set of the first beam number
  • the second model corresponds to the second set of the second beam number. Then, in the scenario of the first beam number corresponding to the second set, the terminal activates the first AI model; in the scenario of the second set corresponding to the second beam number, the terminal activates the second AI function.
  • the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set includes the ratio of the number of beams sent by the network device and/or the number of beams received by the terminal device in the first set to the number of beams sent by the network device and/or the number of beams received by the terminal device in the second set. If the number of beams in the first set is 8 and the number of beams in the second set is 32, the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set is 1/4.
  • the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set is different, and the corresponding AI functions/models are different.
  • the first model corresponds to the first ratio
  • the second model corresponds to the second ratio. Then, in the first ratio scenario, the terminal activates the first AI model; in the second ratio scenario, the terminal activates the second AI function.
  • the beam information between the first set and the second set may include the beam corresponding to the first set.
  • the first beam information includes the number of beams corresponding to the first set and the number of beams corresponding to the second set.
  • the second beam information includes the number of beams corresponding to the second set and the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first beam information corresponds to the first AI function
  • the second beam information corresponds to the second AI function.
  • the beam information between the first set and the second set includes position information.
  • the position information between the first set and the second set is the position of the beam included in the first set in the beam included in the second set.
  • the number of beams included in the second set is 32 beams, for example, numbered 1, 2, 3, ..., 32.
  • the first set includes beams in the second set whose beam numbers are: 1, 5, 9, 13, ..., 29. That is, the positions of the beams included in the first set in the beams included in the second set are the 1st position, the 5th position, the 9th position, and the 13th, ..., 29th position.
  • the first set includes beams in the second set whose beam numbers are: 2, 6, 10, 14, ..., 30. That is, the positions of the beams included in the first set in the beams included in the second set are the 2nd position, the 6th position, the 10th position, and the 14th, ..., 30th position.
  • the beam includes a transmitting beam of a network device and/or a receiving beam of a terminal device.
  • different location information corresponds to different AI functions/models.
  • the first model corresponds to the first position
  • the second model corresponds to the second position.
  • the first location information is that the position of the beam included in the first set in the beam included in the second set is the first position
  • the position of the beam included in the first set in the beam included in the second set is the fifth position. Then, when the position of the beam included in the first set in the beam included in the second set is the first position, the terminal activates the first AI model. When the position of the beam included in the first set in the beam included in the second set is the fifth position, the terminal activates the second AI function.
  • the beam information between the subset and the full set is defined, so that the model's function implementation process of predicting the full set beam information based on the subset beam information is clearer.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping between the beams included in the first set and the beams included in the second set. relation.
  • the beam information between the first set and the second set includes a beam mapping relationship
  • the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is different from the second set. Since the first set is the input set of the model and the second set is the output set of the model, there is a mapping relationship between the first set and the second set. Therefore, when the first set is different from the second set, the corresponding beam information needs to clarify the mapping relationship between the first set and the second set, that is, it is necessary to clarify the mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is different from the second set, including that all elements in the first set are different from elements in the second set and that some elements in the first set are different from elements in the second set.
  • the first set includes 8 reference signals (i.e., wide beams) covering a direction of 120 degrees, wherein each reference signal corresponds to a beam direction; and the second set includes 32 reference signals (i.e., narrow beams), also covering a direction of 120 degrees, wherein each reference signal also corresponds to a beam direction.
  • the first set may include wide beams.
  • the second set includes narrow beams.
  • the mapping relationship between the beams included in the first set and the beams included in the second set may be a mapping relationship between wide beams and narrow beams.
  • the first set is different from the second set, for example, the first set includes wide beams numbered 1 to 8, and the second set includes narrow beams numbered 1 to 32.
  • One mapping relationship is that the first wide beam corresponds to narrow beams numbered 1 to 4, and so on; or the first wide beam corresponds to narrow beams numbered 2 to 5, and so on.
  • mapping relationships between the beams included in the first set and the beams included in the second set correspond to different AI functions, or different mapping relationships between the beams included in the first set and the beams included in the second set correspond to the same AI function.
  • mapping relationships correspond to different AI functions/models.
  • the first model corresponds to the first mapping relationship
  • the second model corresponds to the second mapping relationship.
  • the first mapping relationship and the second mapping relationship are different mapping relationships between the beams included in the first set and the beams included in the second set. Then, in the first mapping relationship scenario, the terminal activates the first AI model; in the second mapping relationship scenario, the terminal activates the second AI function.
  • the mapping relationship beam information between the input set and the output set is defined, so that the model management in the scenario based on different input sets and output sets is clearer.
  • information is carried in at least one of the following ways: system information, Radio Resource Control (RRC) signaling, and RRC release message.
  • RRC Radio Resource Control
  • the RRC signaling includes RRC reconfiguration information.
  • the RRC reconfiguration information includes information of the target cell.
  • the target cell is the cell to which the terminal will switch access.
  • the target cell information includes at least one of the following:
  • the cell identification information of the target cell is the cell identification information of the target cell.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal performs AI model management based on the information in the RRC inactive state or the RRC idle state.
  • the information sent via RRC is used to indicate that the terminal receives the information sent by the network device.
  • the communication method provided by the embodiment of the present disclosure defines information transmission, making the process of a terminal receiving information from a network device clearer.
  • FIG. 3 is a flow chart of a communication method according to an exemplary embodiment.
  • the embodiment of the present disclosure further provides a communication method, which is executed by a network device. The method includes the following steps:
  • step S21 information is sent to the terminal, where the information is used by the terminal to manage the AI model.
  • the AI model managed by the terminal is used for beam prediction.
  • the AI model deployed on the terminal is managed based on the information sent by the network device.
  • the type of information can be system information, etc.
  • the specific information content can be corresponding parameter information, etc.
  • the information sent by the network device is the same as or similar to the information received by the above-mentioned terminal. Therefore, for the information sent by the network device, parameter information, and the relationship between the AI function and the parameter information, please refer to the relevant description in the above-mentioned embodiments, and the embodiments of the present disclosure will not be described in detail here.
  • managing the AI model includes: operating at least one AI function indicated in the information or operating at least one model of the same AI function.
  • the information includes parameter information, and different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information, terminal distribution information, beam information, and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: an urban macro cell, an urban micro cell, an indoor hotspot, a dense city, and a rural area.
  • the network device coverage parameter information includes the distance between the network devices.
  • the terminal distribution information includes at least one of the following information:
  • the ratio between the number of outdoor terminals and the number of indoor terminals is the ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set.
  • the first set and the second set include a network device transmitting beam and/or a terminal device receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set, the number of beams corresponding to the second set, and the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set.
  • the beam information between the first set and the second set includes position information, where the position information is the position of the beam included in the first set in the beam included in the second set.
  • the first set is different from the second set.
  • the beam information between the first set and the second set includes a beam mapping relationship, where the beam mapping relationship is a mapping relationship between beams included in the first set and beams included in the second set.
  • the first set is a beam set corresponding to the AI model input value
  • the second set is a beam set corresponding to the model output.
  • the beam information includes a beam type
  • the beam type includes a DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes information of a target cell, where the target cell is a target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state.
  • the terminal performs AI model management based on the information in the RRC inactive state or the RRC idle state.
  • the AI model is used for beam prediction.
  • the communication method provided in the embodiment of the present disclosure is provided in terms of information transmission for AI management sent by the network device.
  • the definition makes the process of terminals receiving information from network devices clearer.
  • the embodiment of the present disclosure further provides a communication device, which may be a terminal or a component in a terminal.
  • the identification reporting device includes hardware structures and/or software modules corresponding to the execution of each function.
  • the embodiment of the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiment of the present disclosure.
  • Fig. 4 is a structural block diagram of a communication device according to an exemplary embodiment. As shown in Fig. 4 , the communication device includes a receiving unit 101 .
  • the receiving unit 101 is used to receive information sent by a network device, where the information is used for terminal management AI model.
  • managing AI models includes managing at least one AI function and/or managing at least one model included in the same AI function, wherein managing at least one AI function includes at least one of activation, deactivation and switching between different AI functions of the AI function; managing at least one model included in the same AI function includes activation, deactivation and switching of at least one model.
  • different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: urban macrocell, urban microcell, indoor hotspot, dense city, and rural area.
  • the network device coverage parameter information includes the distance between network devices.
  • the terminal distribution information includes at least one of the following information:
  • Number of outdoor terminals Number of indoor terminals; Ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set; the first set and the second set include a network device transmitting beam and/or a terminal device receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set; the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set; the beam information between the first set and the second set includes position information, and the position information is the position of the beam included in the first set in the beam included in the second set.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is a beam set corresponding to an AI model input value
  • the second set is a beam set corresponding to a model output.
  • the beam information includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes the information of the target cell
  • the target cell is the target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal performs AI model management based on the information in the RRC inactive state or the RRC idle state.
  • the AI model is used for beam prediction.
  • the embodiment of the present disclosure also provides a communication device, which can be a network device or a component in a network device.
  • Fig. 5 is a structural block diagram of a communication device according to an exemplary embodiment.
  • the embodiment of the present disclosure provides a communication device, including: a sending unit 201, configured to send information to a terminal, wherein the information is used by the terminal to manage an artificial intelligence AI model.
  • the managing AI model includes managing at least one AI function and/or managing at least one model included in the same AI function, wherein managing at least one AI function includes activating, deactivating, and not activating the AI function. At least one of the switching between the same AI functions; managing at least one model included in the same AI function includes activation, deactivation and switching of at least one model.
  • different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: urban macrocell, urban microcell, indoor hotspot, dense city, and rural area.
  • the network device coverage parameter information includes the distance between network devices.
  • the terminal distribution information includes at least one of the following information: the number of outdoor terminals; the number of indoor terminals; and the ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set; the first set and the second set include a network device transmitting beam and/or a terminal device receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set; the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set; the beam information between the first set and the second set includes position information, and the position information is the position of the beam included in the first set in the beam included in the second set.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is a beam set corresponding to an AI model input value
  • the second set is a beam set corresponding to a model output.
  • the beam information includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes the information of the target cell
  • the target cell is the target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the serving cell.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal performs AI model management based on the information in the RRC inactive state or the RRC idle state.
  • the AI model is used for beam prediction.
  • the embodiment of the present disclosure also provides a communication system.
  • the communication system includes a terminal and a network device.
  • the terminal includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with a communication function, a smart car, a tablet computer (Pad), a computer with a wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.
  • VR virtual reality
  • AR augmented reality
  • the access network equipment is, for example, a node or device that accesses a terminal to a wireless network.
  • the access network equipment may include an evolved Node B (eNB), a next generation evolved Node B (ng-eNB), a next generation Node B (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (CloudRAN), a base station in other communication systems, and at least one of an access node in a wireless fidelity (WiFi) system, but is not limited thereto.
  • eNB evolved Node B
  • ng-eNB next generation evolved Node B
  • gNB next generation Node B
  • FIG6 is a schematic diagram of the architecture of a communication system shown in an exemplary embodiment of the present disclosure. As shown in FIG6 , the present disclosure embodiment relates to a communication method, the method comprising:
  • Step S1000 The network device sends information to the terminal, and the information is used by the terminal to manage the AI model.
  • the management of the AI model includes managing at least one AI function and/or managing at least one model included in the same AI function, wherein managing at least one AI function includes at least one of activation, deactivation and switching between different AI functions of the AI function; managing at least one model included in the same AI function includes activation, deactivation and switching between at least one model.
  • the information includes parameter information, and different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: an urban macro cell, an urban micro cell, an indoor hotspot, a dense city, and a rural area.
  • the network device coverage parameter information includes the distance between network devices.
  • the terminal distribution information includes at least one of the following information: the number of outdoor terminals; the number of indoor terminals; and the ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set; the first set and the second set include a network device transmitting beam and/or a terminal device receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set, and the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set; the beam information between the first set and the second set includes position information, and the position information is the position of the beam included in the first set in the beam included in the second set.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is a beam set corresponding to an AI model input value
  • the second set is a beam set corresponding to a model output.
  • the beam information includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes the information of the target cell
  • the target cell is the target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal is in the RRC inactive state or the RRC idle state based on the signal AI model management based on information.
  • the AI model is used for beam prediction.
  • Step S1001 The terminal receives information sent by the network device, which is used by the terminal to manage the AI model.
  • the management of the AI model includes managing at least one AI function and/or managing at least one model included in the same AI function, wherein managing at least one AI function includes at least one of activation, deactivation and switching between different AI functions of the AI function; managing at least one model included in the same AI function includes activation, deactivation and switching between different models of the model.
  • the information includes parameter information, and different functions and/or different models correspond to different parameter information.
  • the different parameter information includes at least one of the following information: network equipment coverage parameter information; terminal distribution information; beam information and cell identification information.
  • the network device coverage parameter information includes a deployment type, and the deployment type is at least one of the following: an urban macro cell, an urban micro cell, an indoor hotspot, a dense city, and a rural area.
  • the network device coverage parameter information includes the distance between network devices.
  • the terminal distribution information includes a ratio between the number of outdoor terminals and the number of indoor terminals.
  • the beam information includes beam information between a first set and a second set; the first set and the second set include a network device transmitting beam and/or a terminal device receiving beam.
  • the beam information between the first set and the second set includes at least one of the following: the number of beams corresponding to the first set; the number of beams corresponding to the second set, and the ratio of the number of beams corresponding to the first set to the number of beams corresponding to the second set.
  • the first set is a subset of the second set; the beam information between the first set and the second set includes position information, and the position information is the position of the beam included in the first set in the beam included in the second set.
  • the first set is different from the second set;
  • the beam information between the first set and the second set includes a beam mapping relationship, and the beam mapping relationship is a mapping relationship between the beams included in the first set and the beams included in the second set.
  • the first set is a beam set corresponding to an AI model input value
  • the second set is a beam set corresponding to a model output.
  • the beam information includes a beam type
  • the beam type includes a discrete Fourier transform DFT beam or a non-DFT beam.
  • the cell identification information includes at least one of a serving cell identification and a neighboring cell identification.
  • the information is carried in at least one of the following ways: system information, radio resource control RRC signaling, and RRC release message.
  • the RRC signaling includes RRC reconfiguration information
  • the RRC reconfiguration information includes the information of the target cell
  • the target cell is the target cell to which the terminal will switch access.
  • the network device to which the target cell belongs sends the information of the target cell to the network device to which the serving cell belongs.
  • the RRC release message is used for parameter configuration when the terminal is converted from an RRC connected state to an RRC inactive state or an RRC idle state; the terminal performs AI model management based on the information in the RRC inactive state or the RRC idle state.
  • the AI model is used for beam prediction.
  • Fig. 7 is a block diagram of a device 300 for communication according to an exemplary embodiment.
  • the device 300 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.
  • apparatus 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input/output (I/O) interface 312 , a sensor component 314 , and a communication component 316 .
  • the processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations.
  • the processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above-mentioned method.
  • the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components.
  • the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the processing component 302.
  • the memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc.
  • the memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable programmable read-only memory
  • PROM programmable read-only memory
  • ROM read-only memory
  • magnetic memory flash memory
  • flash memory magnetic disk or optical disk.
  • the power component 306 provides power to the various components of the device 300.
  • the power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 300.
  • the multimedia component 308 includes a screen that provides an output interface between the device 300 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, The screen can be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
  • the multimedia component 308 includes a front camera and/or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.
  • the audio component 310 is configured to output and/or input audio signals.
  • the audio component 310 includes a microphone (MIC), and when the device 300 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal.
  • the received audio signal can be further stored in the memory 304 or sent via the communication component 313.
  • the audio component 310 also includes a speaker for outputting audio signals.
  • I/O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.
  • the sensor assembly 314 includes one or more sensors for providing various aspects of the status assessment of the device 300.
  • the sensor assembly 314 can detect the open/closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300, the sensor assembly 314 can also detect the position change of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration/deceleration of the device 300, and the temperature change of the device 300.
  • the sensor assembly 314 can include a proximity sensor configured to detect the presence of a nearby object without any physical contact.
  • the sensor assembly 314 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor assembly 314 can also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • the communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices.
  • the device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof.
  • the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel.
  • the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the apparatus 300 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • controllers microcontrollers, microprocessors or other electronic components to perform the above method.
  • a non-transitory computer-readable storage medium including instructions is also provided, such as a
  • the non-transitory computer-readable storage medium may include a memory 304 containing instructions, which may be executed by a processor 320 of the apparatus 300 to perform the above method.
  • the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
  • FIG8 is a block diagram of an apparatus 400 for communication according to an exemplary embodiment.
  • the apparatus 400 may be provided as a network device.
  • the apparatus 400 includes a processing component 422, which further includes one or more processors, and a memory resource represented by a memory 432 for storing instructions executable by the processing component 422, such as an application.
  • the application stored in the memory 432 may include one or more modules, each corresponding to a set of instructions.
  • the processing component 422 is configured to execute instructions to perform the above method.
  • the device 400 may also include a power supply component 426 configured to perform power management of the device 400, a wired or wireless network interface 450 configured to connect the device 400 to a network, and an input/output (I/O) interface 458.
  • the device 400 may operate based on an operating system stored in the memory 432, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
  • a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 432 including instructions, which can be executed by the processing component 422 of the device 400 to perform the above method.
  • the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.
  • plural refers to two or more than two, and other quantifiers are similar thereto.
  • “And/or” describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B may represent: A exists alone, A and B exist at the same time, and B exists alone.
  • the character “/” generally indicates that the associated objects before and after are in an “or” relationship.
  • the singular forms “a”, “the” and “the” are also intended to include plural forms, unless the context clearly indicates other meanings.
  • first, second, etc. are used to describe various information, but such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other, and do not indicate a specific order or degree of importance. In fact, the expressions “first”, “second”, etc. can be used interchangeably.
  • the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information.

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Abstract

本公开是关于一种通信方法、装置及存储介质。方法包括：接收网络设备发送的信息，所述信息用于所述终端管理人工智能AI模型。基于终端管理AI模型所用信息的具体内容确定以及承载方式的确定，实现了对AI功能的模型管理过程中数据传输方式的确定。

Description

一种通信方法、装置及存储介质 技术领域
本公开涉及通信技术领域，尤其涉及一种通信方法、装置及存储介质。
背景技术
近年来，人工智能(Artificial Intelligence，AI)技术在多个领域取得不断突破，AI技术正逐步与其他学科领域交叉渗透，为不同学科的发展提供了新的方向和方法。
在第三代合作伙伴计划(3rdGeneration Partnership Project，3GPP)中设立了关于人工智能技术在无线空口中的研究项目。该项目旨在研究在无线空口中引入AI技术，同时探讨AI技术如何对无线空口的传输技术进行辅助提高。例如，基于AI模型进行波束预测，减少终端测量的波束对的数量。
发明内容
为克服相关技术中存在的问题，本公开提供一种通信方法、装置及存储介质。
根据本公开实施例的第一方面，提供一种通信方法，由终端执行，包括接收网络设备发送的信息，所述信息用于所述终端管理人工智能AI模型。
一种实施方式中，所述管理AI模型包括：对所述信息中指示的至少一个AI功能进行操作或同一AI功能的至少一个模型进行操作。
一种实施方式中，所述信息包括参数信息，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，所述不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
一种实施方式中，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，所述网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，所述终端分布信息包括以下信息中的至少一项：
室外终端数量；
室内终端数量；
室外终端数量与室内终端数量之间的比值。
一种实施方式中，所述波束信息包括第一集合与第二集合之间的波束信息；所述第一集合和所述第二集合中包括网络设备发送波束和/或终端接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应 的波束数量；第二集合对应的波束数量；第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，所述第一集合为所述第二集合的子集；第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
一种实施方式中，所述第一集合不同于所述第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
一种实施方式中，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
一种实施方式中，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
一种实施方式中，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，所述信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
一种实施方式中，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
一种实施方式中，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；所述终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
一种实施方式中，所述AI模型用于波束预测。
根据本公开实施例的第二方面，提供一种通信方法，由网络设备执行，所述方法包括：发送信息至终端，所述信息用于所述终端管理人工智能AI模型。
一种实施方式中，所述管理AI模型包括：对所述信息中指示的至少一个AI功能进行操作或同一AI功能的至少一个模型进行操作。
一种实施方式中，所述信息包括参数信息，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，所述不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
一种实施方式，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，所述网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，所述终端分布信息包括以下信息中的至少一项：
室外终端数量；
室内终端数量；
室外终端数量与室内终端数量之间的比值。
一种实施方式中，所述波束信息包括第一集合与第二集合之间的波束信息；所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量；第二集合对应的波束数量以及第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，所述第一集合为所述第二集合的子集；第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
一种实施方式中，所述第一集合不同于所述第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
一种实施方式中，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
一种实施方式中，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
一种实施方式中，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，所述信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
一种实施方式中，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
一种实施方式中，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；所述终端在RRC非激活态或RRC空闲态基于所述信 息进行AI模型管理。
一种实施方式中，所述AI模型用于波束预测。
根据本公开实施例的第三方面，提供一种通信装置，包括：接收单元，用于接收网络设备发送的信息，所述信息用于所述终端管理人工智能AI模型。
一种实施方式中，所述管理AI模型包括：对所述信息中指示的至少一个AI功能进行操作或同一AI功能的至少一个模型进行操作。
一种实施方式中，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，所述不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
一种实施方式中，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，所述网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，所述终端分布信息包括以下信息中的至少一项：
室外终端数量；
室内终端数量；
室外终端数量与室内终端数量之间的比值。
一种实施方式中，所述波束信息包括第一集合与第二集合之间的波束信息；所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量；第二集合对应的波束数量；第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，所述第一集合为所述第二集合的子集；第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
一种实施方式中，所述第一集合不同于所述第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
一种实施方式中，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
一种实施方式中，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
一种实施方式中，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，所述信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
一种实施方式中，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
一种实施方式中，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；所述终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
一种实施方式中，所述AI模型用于波束预测。
根据本公开实施例的第四方面，提供一种通信装置，包括：发送单元，用于发送信息至终端，所述信息用于所述终端管理人工智能AI模型。
一种实施方式中，所述管理AI模型包括：对所述信息中指示的至少一个AI功能进行操作或同一AI功能的至少一个模型进行操作。
一种实施方式中，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，所述不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
一种实施方式中，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，所述网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，所述终端分布信息包括以下信息中的至少一项：
室外终端数量；
室内终端数量；
室外终端数量与室内终端数量之间的比值。
一种实施方式中，所述波束信息包括第一集合与第二集合之间的波束信息；所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量；第二集合对应的波束数量；第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，所述第一集合为所述第二集合的子集；第一集合与第二集合之间的 波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
一种实施方式中，所述第一集合不同于所述第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
一种实施方式中，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
一种实施方式中，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
一种实施方式中，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，所述信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
一种实施方式中，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
一种实施方式中，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；所述终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
一种实施方式中，所述AI模型用于波束预测。
根据本公开的第五方面，提供了一种通信装置，包括：处理器；
用于存储处理器可执行指令的存储器；
其中，所述处理器被配置为：执行第一方面或第一方面中任意一种实施方式所述的通信方法。
根据本公开的第六方面，提供了一种通信装置，包括：处理器；
用于存储处理器可执行指令的存储器；
其中，所述处理器被配置为第二方面或第二方面中任意一种实施方式所述的通信方法。
根据本公开的第七方面，提供了一种存储介质，所述存储介质中存储有指令，当所述存储介质中的指令由终端的处理器执行时，使得终端能够执行第一方面或第一方面中任意一种实施方式所述的通信方法。
根据本公开的第八方面，提供了一种存储介质，所述存储介质中存储有指令，当所述存储介质中的指令由终端的处理器执行时，使得终端能够执行第二方面或第二方面中任意一种实施方式所述的通信方法。
本公开的实施例提供的技术方案可以包括以下有益效果：基于终端管理AI模型所用信息的具体内容确定以及承载方式的确定，实现了对AI功能的模型管理过程中数据传输方式的确定。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
图1是根据一示例性实施例示出的一种无线通信系统的示意图。
图2是根据一示例性实施例示出的一种通信方法的流程图。
图3是根据一示例性实施例示出的一种通信方法的流程图。
图4是根据一示例性实施例示出的一种通信装置的结构框图。
图5是根据一示例性实施例示出的一种通信装置的结构框图。
图6是根据一示例性实施例示出的一种通信系统的架构示意图。
图7是根据一示例性实施例示出的一种用于通信的装置框图。
图8是根据一示例性实施例示出的一种用于通信的装置框图。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开相一致的所有实施方式。
本公开实施例的通信方法可以应用于图1所示的无线通信系统中。参阅图1所示，该无线通信系统中包括网络设备和终端。终端通过无线资源与网络设备相连接，并进行数据传输。
可以理解的是，图1所示的无线通信系统仅是进行示意性说明，无线通信系统中还可包括其它网络设备，例如还可以包括核心网络设备、无线中继设备和无线回传设备等，在图1中未画出。本公开实施例对该无线通信系统中包括的网络设备数量和终端数量不做限定。
进一步可以理解的是，本公开实施例无线通信系统，是一种提供无线通信功能的网络。无线通信系统可以采用不同的通信技术，例如码分多址(code division multiple access,CDMA)、宽带码分多址(wideband code division multiple access，WCDMA)、时分多址(time division multiple access，TDMA)、频分多址(frequency division multiple access，FDMA)、正交频分多址(orthogonal frequency-division multiple access，OFDMA)、单载波频分多址(single Carrier FDMA，SC-FDMA)、载波侦听多路访问/冲突避免(Carrier Sense Multiple Access with CollisionAvoidance)。根据不同网络的容量、速率、时延等因素可以将网络分为2G(英文：generation)网络、3G网络、4G网络或者未来演进网络，如5G网络，5G网络也可称为是新无线网络(New Radio，NR)。为了方便描述，本公开有时会将无线通信网络简称为网络。
进一步的，本公开中涉及的网络设备也可以称为无线接入网设备。该无线接入网设备可以是：基站、演进型基站(evolved node B，基站)、家庭基站、无线保真(wireless fidelity，WIFI)系统中的接入点(access point，AP)、无线中继节点、无线回传节点、传输点(transmission point，TP)或者发送接收点(transmission and reception point，TRP)等，还可以为NR系统中的gNB，或者，还可以是构成基站的组件或一部分设备等。应理解，本公开的实施例中，对网络设备所采用的具体技术和具体设备形态不做限定。在本公开中，网络设备可以为特定的地理区域提供通信覆盖，并且可以与位于该覆盖区域(小区)内的终端进行通信。此外，当为车联网(V2X)通信系统时，网络设备还可以是车载设备。
进一步的，本公开中涉及的终端，也可以称为终端设备、用户设备(User Equipment，UE)、移动台(Mobile Station，MS)、移动终端(Mobile Terminal，MT)等，是一种向用户提供语音和/或数据连通性的设备，例如，终端可以是具有无线连接功能的手持式设备、车载设备等。目前，一些终端的举例为：智能手机(Mobile Phone)、客户前置设备(Customer Premise Equipment，CPE)，口袋计算机(Pocket Personal Computer，PPC)、掌上电脑、个人数字助理(Personal Digital Assistant，PDA)、笔记本电脑、平板电脑、可穿戴设备、或者车载设备等。此外，当为车联网(V2X)通信系统时，终端设备还可以是车载设备。应理解，本公开实施例对终端所采用的具体技术和具体设备形态不做限定。
在NR中，特别是通信频段在frequency range 2时，由于高频信道衰减较快，为了保证覆盖范围，需要使用基于波束(beam)的发送和接收。
传统的波束管理过程中，网络设备会配置用于波束测量的参考信号资源集合，终端对该参考信号资源集合中的参考信号资源进行测量，然后上报其中比较强的X个参考信号资源ID和对应的L1-RSRP和/或L1-SINR。传统方法的问题在于，基站配置的参考信号资源 集合中包含的X个参考信号，每个参考信号对应基站不同的发送波束，那针对每个参考信号，终端需要使用多个接收波束来针对该参考信号进行测量，并获得多个接收波束分别对应的波束测量质量，并确定一个最好的波束测量质量。所以终端需要测量的波束对的最大数量为M*N，其中M为基站发送波束数量，N为终端接收波束数量。此种方式终端需要测量的波束数量较多。需要减少终端测量的波束对数量。
近几年，人工智能(Artificial Intelligence，AI)技术在多个领域取得不断突破。给人们生活带来便利同时，也在促进各个行业进行产业升级。随着AI技术与其他学科领域交叉渗透，在其发展融合不同学科知识的同时，也未不同学科的发展提供了新的方向和方法。
以通信行业为例，在第三代伙伴计划(3rd Generation Partnership Project，3GPP)标准组织的Release18的阶段中，在RAN1工作组讨论并确定的潜在新立项的内容中设计了，关于人工智能技术在无线空口中的研究项目。该项目旨在研究如何在无线空口中引入人工智能技术，同时探讨人工智能技术如何对无线空口的传输技术进行辅助提高。
为了减少终端测量的波束对的数量，提出基于AI的预测方法。比如终端本来一共需要测量的波束对的数量为M*N(其中M为基站发送波束数量，N为终端接收波束数量)，但由于有了AI模型，对于空域波束预测，终端只需要测量M*N个波束对中的其中一部分，比如1/8,1/4等，然后将测得的这些波束对的波束测量质量输入到AI模型中，模型即可输出M*N个波束对的波束信息。对于时域波束预测，终端可以测量历史时间的波束对的波束质量，来预测未来时刻的波束对的波束信息。
目前已提出基于AI的波束测量结果的预测，包括空域波束预测和时域波束预测。其中，空域波束预测基于set B中波束的测量结果预测setA中波束的测量结果。时域波束预测：基于历史时间中set B的测量结果，预测未来时间中setA的波束。其中set B为setA的子集，或set B为wide beam而setA为narrow beam，或时域波束预测中还包括一种set B和setA一样。
以下说明一下AI模型的波束预测原理如下：
1.对于空域预测：终端测量set B的L1-RSRP，输入到AI模型，预测setA的L1-RSRP或setA中的最佳波束。Set B和setA关系包含如下两种：
(一)，set B是setA的子集，比如setA包含32个参考信号(每个参考信号对应一个波束方向)，那么set B包含其中N个参考信号，比如N＝8；
(二)，set B为宽波束，setA为窄波束。比如setA包含32个参考信号(每个参考信号对应一个波束方向，32个参考信号覆盖120度的方向)。而set B包含另外N个参考信号，比如N＝8，而这N个参考信号同样覆盖120度的方向，即set B中每个参考信号的波 束方向覆盖了setA中多个参考信号的波束方向。可以理解为setA中的32/N个参考信号与set B中的同一个参考信号为QCL(quasi co location，准共站址)Type D的关系。
2.对于时域预测，终端测量历史时间set B的L1-RSRP，输入到AI模型，预测未来时刻setA的L1-RSRP。而set B和setA的关系除了上述两种外，还有一种是set B和setA一样。
其中，如果基于AI模型，则未来时刻的参考信号是可以不发送，基于AI模型输出获得波束信息，上报给基站。而传统方法的话，未来时刻的参考信号也需要发送，终端测量未来时刻的参考信号并获得波束信息上报给基站
相关技术中，基于两种管理方式实现对AI模型的管理，包括：基于AI功能以及模型标识。基于AI功能来对模型进行管理的原理可以理解为：终端只需要告知网络设备终端支持哪些AI功能即可。那么AI功能的激活，去激活以及切换等这些操作是需要终端与网络设备进行交互，并且需要网络设备来决定或终端决定并告知网络设备的。但是在某个AI功能下，终端可以维护一个或多个AI模型，若有多个AI模型，那么终端可以自行在一个AI功能下的不同模型之间进行切换，无需网络设备来决定或终端决定之后无需告知网络设备。而基于模型标识的管理可以理解为：终端需要告知网络设备终端支持哪些模型标识对应的模型，那么AI模型的激活，去激活以及切换等这些操作是需要终端与网络设备进行交互，并且需要网络设备来决定或终端决定并告知网络设备的。
可以理解的是，在使用基于AI功能的模型管理时，如上述内容所说，终端可能被预先部署了数量大于1个的具有相同AI功能的模型。所以，在终端以基于AI功能模型管理的情况下，若要实现不同AI功能之间的切换，那么需要与网络设备交互触发切换的性能指标以及切换决定等。但若要实现相同AI功能下的不同模型的切换或者选用，只需要终端根据当前的一些信息来确定选择该AI功能下的多个模型中的哪个模型。而当前的一些信息是需要由网络设备发送一些辅助信息来支持终端对相同AI功能下的不同模型的切换或选用。但是，这些辅助信息具体包括哪些信息，以及如何发送，是需要优先确定的。
有鉴于此，本公开实施例提供一种针对基于同一AI功能的模型管理，确定所用的辅助信息以及辅助信息的发送方式。
图2是根据一示例性实施例示出的一种通信方法的流程图，如图2所示，通信方法，由终端执行，包括以下步骤：
在步骤S11中，接收网络设备发送的信息，信息用于终端管理AI模型。
本公开实施例中，终端管理的AI模型用于波束预测。
本公开实施例中，对于部署在终端的AI模型，基于网络设备发出的信息进行管理。 对于信息的类型以及具体信息内容可以有多种实施方式，例如，信息的类型可以为系统信息等，具体信息内容可以为相应的参数信息等。
本公开实施例中，对于网络设备发送的用于终端管理AI模型的信息，即可以作为辅助信息。以下为描述方便，将网络设备发送用来支持终端对相同功能下的不同模型的切换或使用的辅助信息表述为网络设备发送的用于终端管理AI模型的信息，亦简称为信息。
示例性的，对于部署在终端的AI模型，对于对应相同AI功能的模型进行管理。例如，具有空域波束预测功能的第一模型切换至具有空域波束预测功能的第二模型等。
本实施例提供的通信方法，终端可以通过网络设备发送的信息确定针对部署在终端的AI模型的管理内容。
所述管理AI模型包括：对所述信息中指示的至少一个AI功能进行操作或同一AI功能的至少一个模型进行操作；
对所述信息中指示的至少一个AI功能进行操作包括AI功能的激活，去激活以及不同AI功能之间的切换中的至少一项；对同一AI功能的至少一个模型进行操作包括模型的激活，去激活以及不同模型之间的切换中的至少一项。示例性的，对信息中指示的至少一个AI功能进行操作可以包括：管理和/或操作至少一个AI功能包括AI功能的激活，去激活以及不同AI功能之间的切换中的至少一项。对信息中指示同一AI功能的至少一个模型进行操作可以包括：管理和/或同一AI功能所包括的至少一个模型包括模型的激活，去激活以及不同模型的切换中的至少一项。本公开实施例中，对于部署在终端的AI模型，网络设备可采用模型对应的功能进行管理，而终端可采用模型对应的模型标识进行管理。因此，对于模型的管理从功能层面进行管理以及模型层面进行管理两方面进行。对于功能层面进行管理，可以包括以下至少一项：对于至少一个AI功能的激活，至少一个AI功能的去激活，以及不同AI功能之间的切换。例如：需求为空域波束预测，则启用空域波束预测对应的AI功能，即激活空域波束预测AI功能，至于具体激活空域波束预测AI功能对应的哪个模型，网络设备可以不指示。
示例性的，在波束预测过程中若需求更改为进行时域波束预测，则应当将已激活的空域波束预测对应的AI功能进行去激活，再激活时域波束预测对应的AI功能，即实现不同AI功能的切换。例如，网络设备可以发送相应的指示信息至终端，使终端将已激活的空域波束预测对应的AI功能进行去激活，再激活时域波束预测对应的AI功能，即实现不同AI功能的切换。
示例性的，对于模型层面进行管理，可以包括以下至少一项：管理同一AI功能所包括的至少一个模型的激活，至少一个模型的去激活，以及同功能的不同模型的切换。例如： 需求为空域波束预测，且此时的场景为宏小区，则激活空域波束预测AI功能对应的宏小区的第一AI模型。进一步的，网络设备可以发送相应的指示信息至终端，使终端在场景为宏小区的情况下，激活空域波束预测AI功能对应的宏小区的第一AI模型。
示例性的，若需求为变更空域波束预测，且场景为微小区场景，则去激活空域波束预测AI功能对应的宏小区的第一AI模型，激活空域波束预测AI功能对应的微小区的第二AI模型。例如，网络设备可以发送相应的指示信息至终端，使终端在场景为微小区场景的情况下，激活空域波束预测AI功能对应的微小区的第二AI模型。
本实施例提供的通信方法，对于AI模型管理的具体内容从功能层面进行管理以及模型层面进行管理两方面进行了定义，使后续通信方法更明确。
本公开实施例中，终端接收到网络设备发送的信息，包括参数信息，不同功能和/或不同模型对应不同的参数信息。
示例性的，具有空域波束预测功能的模型对应的参数信息(为方便理解此处简称第一参数信息)，用于指示空域波束预测功能对应适用的参数、假设和/或场景等。即，用于指示空域波束预测功能的第一参数信息可以包括参数、假设和/或场景。具有时域波束预测功能的模型对应的参数信息(为方便理解此处简称第二参数信息)，用于指示时域波束预测功能对应适用的参数、假设和/或场景等。即，用于指示时域波束预测功能的第二参数信息可以包括参数、假设和/或场景。
可以理解的是，第二参数信息对应时域波束预测功能，则相较于对应空域波束预测功能的第一参数信息并不是完全相同。
本公开实施例中，对于参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
其中，不同的AI功能对应不同的参数信息。不同的AI功能各自对应的参数信息相互独立，并且并非完全相同。假设不同AI功能中任意两个AI功能称为第一AI功能，和第二AI功能。其中，同一AI功能下不同模型对应不同的参数信息。其中，不同模型中任意两个不同的模型称为第一模型和第二模型。可以理解的是，本公开实施例以下实施例中，第一AI功能、第一模型可以交替使用，第二AI功能、第二模型可以交替使用。也可以理解为本公开以下实施例中涉及的第一功能和第二功能，可以替换为第一模型和第二模型。第一模型和第二模型可以替换为第一功能和第二功能。
本公开实施例中，第一AI功能和第二AI功能的以下至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
比如第一AI功能和第二AI功能对应的网络部署类型不同；
比如第一AI功能和第二AI功能对应的网络设备之间的间距不同；
比如第一AI功能和第二AI功能对应的终端分布不同；
比如第一AI功能和第二AI功能对应的波束信息不同。
其中，本公开实施例中，第一AI功能和第二AI功能可以彼此包括的参数项不同。
示例性的，第一AI功能对应的参数信息包括网络设备覆盖参数信息以及小区标识信息。第二AI功能对应的参数信息可以包括网络设备覆盖参数信息以及终端分布信息。
本公开实施例中，网络设备覆盖参数包括网络设备的部署类型以及网络设备之间的间距中的至少一项。
网络设备的部署类型包括以下至少一项：市区宏小区(Urban macro，Uma)、市区微小区(Urban micro，Umi)、室内热点(indoor)、密集城市(dense urban)以及乡村(rural)。
其中，第一AI功能和第二AI功能对应的网络部署类型不同。
比如第一AI功能对应宏小区，第二AI功能对应微小区。那么在宏小区场景下，终端激活第一AI功能；在微小区场景下，终端激活第二AI功能。
应理解，宏小区又叫宏蜂窝，即采用蜂窝技术的小区都被称为宏蜂窝小区，或宏小区。市区宏小区即被设置在市区中的宏小区。
应理解，微小区是在宏小区的基础上发展起来的技术，用于消除宏蜂窝中的“盲点”。市区微小区即被设置在市区中的微小区。
示例性的，对于网络设备的部署类型作为参数信息时，可以仅涉及其中一项，也可以存在相互组合的可能。例如，参数信息中包括了设备A在市区宏小区的部署以及乡村的部署情况。
本公开实施例中，网络设备之间的间距也称为基站之间的站间距(inter-site distance，ISD)。
本公开实施例中，网络设备之间的间距具体数值设定应结合相应的网络设备选取的部署类型确定。以选用市区宏小区为例，则对应的网络设备间距可以被布置为100m，200m，500m，1000m等。
其中，第一AI功能和第二AI功能对应的ISD不同。
比如第一AI功能对应ISD值200m，第二AI功能对应ISD值500m。那么在ISD值200m的场景下，终端激活第一AI功能；在ISD值500m的场景下，终端激活第二AI功能。
本公开实施例中，网络设备覆盖参数信息可以包括网络设备部署类型以及网络设备之间的间距。例如网络设备基于市区宏小区模式部署，ISD为500m。
本公开实施例中，网络设备覆盖参数信息中小区标识信息包括服务小区标识和邻小区标识中的至少一项。小区标识信息即用于表征小区身份的标识，例如可以定义为A小区，B小区以及C小区等。
示例性的，以服务小区为A小区为例，已知和A小区相邻的小区为B小区，C小区以及D小区。那么网络设备覆盖参数信息中需要包括A小区，B小区，C小区以及D小区中的至少一项。
本实施例提供的通信方法，通过对网络设备对应的部署类型以及网络设备之间的间距信息的定义，使后续通信方法更明确。
本公开实施例中，对于终端分布信息包括室外终端数量。
本公开实施例中，对于终端分布信息包括室内终端数量。
本公开实施例中，对于终端分布信息包括室外终端数量与室内终端数量的比值。
其中，终端分布信息可以为室内终端数量与室外终端数量的比值。示例性的，关于终端室内外分布的数量以及相应的比值信息，可以通过相关设备基于目标范围内终端连接到网络的数量统计得到。例如，统计得到室内终端数量100台，室外终端200台，室外终端与室内终端数量比值即为2:1。例如，当前区域内分布全为室外终端，或当前区域内室外终端数量与室内终端数量的比例为4:1等。
本实施例提供的通信方法，通过对终端分布信息的定义，使后续通信方法更明确。
本公开实施例中，对于波束信息包括：第一集合与第二集合之间的波束信息。
示例性的，本公开实施例中，第一集合为AI模型输入值对应的波束集合，第二模型为模型输出对应的波束集合。
本公开实施例中，波束信息包括波束类型，波束类型包括离散傅里叶变换(Discrete Fourier Transform，DFT)波束，或非DFT波束。
其中，第一AI功能和第二AI功能对应的波束类型不同。
比如第一AI功能对应DFT波束，第二AI功能对应非DFT波束。那么在DFT波束场景下，终端激活第一AI功能；在非DFT波束场景下，终端激活第二AI功能。
应理解，波束类型中定义了波束的适用类型为全部类型的波束(即DFT波束，或非DFT波束)。那么对应的第一集合波束信息，第二集合波束信息以及第一集合与第二集合之间的波束信息同样适用类型为全部类型的波束。后续对于第一集合、第二集合以及第一和与第二集合之间的波束信息不再对此赘述。
本公开实施例中，第一集合与第二集合之间的波束信息包括波束类型，波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
本公开实施例中，第一集合的波束信息包括波束类型，波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
本公开实施例中，第二集合的波束信息包括波束类型，波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
本公开实施例中，第一集合包括网络设备发送的波束和/或终端设备接收到的波束。
本公开实施例中，第二集合包括网络设备发送的波束和/或终端设备接收到的波束。
本公开实施例中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量；第二集合对应的波束数量；第一集合对应的波束数量与第二集合对应的波束数量的比值。
示例性的，用于波束预测模型的输入集，即，第一集合对应的波束数量包括：网络设备发送的波束和/或终端设备接收到的波束数量。
本公开实施例中，不同AI功能/模型对应不同波束数量。不同波束数量中的任意两个不同数量称为第一波束数量和第二波束数量。
本公开实施例中，不同AI功能/模型对应不同波束数量的第二集合。例如，第一模型对应第一波束数量的第一集合，第二模型对应第二波束数量的第一集合。那么在第一集合对应的第一波束数量场景下，终端激活第一AI模型；在第一集合对应第二波束数量场景下，终端激活第二AI功能。
示例性的，用于波束预测模型的输出集，即，第二集合对应的波束数量包括：网络设备发送的波束和/或终端设备接收到的波束数量。例如波数数量是32。
本公开实施例中，不同AI功能/模型对应不同波束数量的第二集合。例如，第一模型对应第一波束数量的第二集合，第二模型对应第二波束数量的第二集合。那么在第二集合对应的第一波束数量场景下，终端激活第一AI模型；在第二集合对应第二波束数量场景下，终端激活第二AI功能。
示例性的，第一集合对应的波束数量与第二集合对应的波束数量的比值。包括，第一集合中网络设备发送的波束和/或终端设备接收到的波束数量与第二集合中网络设备发送的波束和/或终端设备接收到的波束数量的比值。若第一集合波束数量是8，第二集合波束数量是32，则第一集合对应的波束数量与第二集合对应的波束数量的比值为1/4。
本公开实施例中，第一集合对应的波束数量与第二集合对应的波束数量的比值不同，对应的AI功能/模型不同。例如，第一模型对应第一比值，第二模型对应第二比值。那么在第一比值场景下，终端激活第一AI模型；在第二比值场景下，终端激活第二AI功能。
本公开实施例中，第一集合与第二集合之间的波束信息可以包括第一集合对应的波束 数量以及第二集合对应的波束数量。或者包括第一集合对应的波束数量、以及第一集合对应的波束数量与第二集合对应的波束数量的比值。或者包括第二集合对应的波束数量、以及第一集合对应的波束数量与第二集合对应的波束数量的比值。
其中，不同波束信息对应不同的AI功能。例如，第一波束信息包括第一集合对应的波束数量以及第二集合对应的波束数量。第二波束信息包括第二集合对应的波束数量、以及第一集合对应的波束数量与第二集合对应的波束数量的比值。第一波束信息对应的第一AI功能，第二波束信息对应的第二AI功能。
本公开实施例中，当第一集合为第二集合的子集时，第一集合与第二集合之间的波束信息包括位置信息。
本公开实施例中，当第一集合为第二集合的子集时，第一集合与第二集合之间的位置信息为第一集合所包括波束在第二集合所包括波束中的位置。
示例性的，第二集合包括的波束数量为32个波束，比如编号为1，2，3，……，32。第一集合包括第二集合中波束编号分别为：1，5，9，13，……，29的波束。即第一集合所包括的波束在第二集合所包括波束的位置分别为第1个位置，第5个位置，第9个位置，以及第13个，……第29个位置。或第一集合包括第二集合中波束编号分别为：2，6，10，14，……，30。即第一集合所包括的波束在第二集合所包括波束的位置分别为第2个位置，第6个位置，第10个位置，以及第14个，……第30个位置。可以理解的是，第一集合所包括波束在第二集合所包括波束中的位置不同时，可以对应不同的AI功能；或第一集合所包括波束在第二集合所包括波束中的位置不同时，可以对应相同的AI功能。其中，波束包括网络设备的发送波束和/或终端设备的接收波束。
本公开实施例中，不同位置信息，对应不同的AI功能/模型。例如，第一模型对应第一位置，第二模型对应第二位置。例如，第一位置信息为第一集合所包括的波束在第二集合所包括波束的位置为第1个位置，第一集合所包括的波束在第二集合所包括波束的位置为第5个位置。那么在第一集合所包括的波束在第二集合所包括波束的位置为第1个位置下，终端激活第一AI模型。在第一集合所包括的波束在第二集合所包括波束的位置为第5个位置下，终端激活第二AI功能。
本实施例提供的通信方法中，在基于子集波束(输入集)预测全集波束(输出集)的模型使用场景中，定义了子集与全集之间的波束信息，使模型在进行基于子集波束信息预测全集波束信息的功能实现过程更明确。
本公开实施例中，第一集合不同于第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，波束映射关系为第一集合所包括波束与第二集合所包括波束之间的映射 关系。
本公开实施例中，当第一集合不同于第二集合，第一集合与第二集合之间的波束信息包括波束映射关系，波束映射关系为第一集合所包括波束与第二集合所包括波束之间的映射关系。
应理解，第一集合不同于第二集合。由于第一集合为模型的输入集，第二集合为模型输出集，第一集合与第二集合之间是具有映射关系的。因此，在第一集合不同第二集合时，对应的波束信息需要明确第一集合与第二集合之间的映射关系，即，需要明确第一集合所包括波束与第二集合所包括波束之间的映射关系。
示例性的，第一集合不同于第二集合，包括第一集合中的元素全部与第二集合中的元素不同和第一集合中的元素部分与第二集合中的元素不同。
示例性的，假定第一集合包括8个参考信号(即宽波束)覆盖了120度的方向，其中每个参考信号对应一个波束方向；第二集合包括32个参考信号(即窄波束)，同样覆盖了120度的方向，其中每个参考信号同样对应一个波束方向。
示例性的，第一集合可以包括宽波束。第二集合包括窄波束。第一集合所包括波束与第二集合所包括波束之间的映射关系可以是宽波束与窄波束之间的映射关系。例如，第一集合不同于第二集合，例如第一集合包括编号为1～8的宽波束，第二集合包括编号为1～32的窄波束。一种映射关系是第一个宽波束对应编号为1～4的窄波束，依次下去；或第一个宽波束对应编号为2～5的窄波束，依次下去。
可以理解的是，第一集合所包括波束与第二集合所包括波束之间的不同映射关系，对应不同的AI功能。或者第一集合所包括波束与第二集合所包括波束之间的不同映射关系，对应相同的AI功能。
本公开实施例中，不同映射关系，对应不同的AI功能/模型。例如，第一模型对应第一映射关系，第二模型对应第二映射关系。第一映射关系和第二映射关系为第一集合所包括波束与第二集合所包括波束之间的不同映射关系。那么在第一映射关系场景下，终端激活第一AI模型；在第二映射关系场景下，终端激活第二AI功能。
本公开实施例提供的通信方法中，在基于输入集与输出集不同的模型使用场景中，定义了输入集与输出集之间的映射关系波束信息，使在进行基于输入集与输出集不同的场景下的模型管理更明确。
本公开实施例中，信息通过以下至少一种方式承载：系统信息、无线资源控制(Radio Resource Control，RRC)信令、以及RRC释放消息。
本公开实施例中，对于RRC信令包括RRC重配置信息。
本公开实施例中，RRC重配置信息包括目标小区的信息。
应理解，目标小区即为终端将切换接入的小区。
示例性的，目标小区的信息包括以下至少一项：
目标小区的网络设备覆盖参数信息；
目标小区的终端分布信息；
目标小区的波束信息；
目标小区的小区标识信息。
本公开实施例中，目标小区所属的网络设备发送目标小区的信息给服务小区所属网络设备。
本公开实施例中，RRC释放消息用于终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
应理解，通过RRC为载体进行发送的信息，是用于指示对于终端接收网络设备所发送的信息。
本公开的实施例提供的通信方法，在信息传递方面做出了定义，使终端接收网络设备的信息过程更明确。
基于相同的构思，如图3所示，图3是根据一示例性实施例示出的一种通信方法的流程图，本公开实施例还提供一种通信方法，由网络设备执行，所述方法包括如下步骤：
在步骤S21中，发送信息至终端，所述信息用于所述终端管理AI模型。
本公开实施例中，终端管理的AI模型用于波束预测。
本公开实施例中，对于部署在终端的AI模型，基于网络设备发出的信息进行管理。对于信息的类型以及具体信息内容可以有多种实施方式，例如，信息的类型可以为系统信息等，具体信息内容可以为相应的参数信息等。
本公开实施例中，网络设备发送的信息，与上述终端接收的信息相同或相类似，故，对于网络设备发送的信息、参数信息以及AI功能与参数信息之间的关系，可以参阅上述实施例中相关描述，本公开实施例在此不再详述。
例如，一种实施方式中管理AI模型包括：对所述信息中指示的至少一个AI功能进行操作或同一AI功能的至少一个模型进行操作。
一种实施方式中，信息包括参数信息，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息。终端分布信息。波束信息以及小区标识信息。
一种实施方式，网络设备覆盖参数信息包括部署类型，部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，终端分布信息包括以下信息中的至少一项：
室外终端数量。
室内终端数量。
室外终端数量与室内终端数量之间的比值。
一种实施方式中，波束信息包括第一集合与第二集合之间的波束信息。第一集合和第二集合中包括网络设备发送波束和/或终端设备接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量。第二集合对应的波束数量以及第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，第一集合为第二集合的子集。第一集合与第二集合之间的波束信息包括位置信息，位置信息为第一集合所包括波束在第二集合所包括波束中的位置。
一种实施方式中，第一集合不同于第二集合。第一集合与第二集合之间的波束信息包括波束映射关系，波束映射关系为第一集合所包括波束与第二集合所包括波束之间的映射关系。
一种实施方式中，第一集合为AI模型输入值对应的波束集合，第二集合为模型输出对应的波束集合。
一种实施方式中，波束信息包括波束类型，波束类型包括DFT波束，或非DFT波束。
一种实施方式中，小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，RRC信令包括RRC重配置信息，RRC重配置信息包括目标小区的信息，目标小区为终端将切换接入的目标小区。
一种实施方式中，目标小区所属网络设备发送目标小区的信息给服务小区所属网络设备。
一种实施方式中，RRC释放消息用于终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置。终端在RRC非激活态或RRC空闲态基于信息进行AI模型管理。
一种实施方式中，AI模型用于波束预测。
本公开实施例提供的通信方法，在网络设备发送的用于AI管理的信息传递方面做出 了定义，使终端接收网络设备的信息过程更明确。
需要说明的是，本领域内技术人员可以理解，本公开实施例上述涉及的各种实施方式/实施例中可以配合前述的实施例使用，也可以是独立使用。无论是单独使用还是配合前述的实施例一起使用，其实现原理类似。本公开实施中，部分实施例中是以一起使用的实施方式进行说明的。当然，本领域内技术人员可以理解，这样的举例说明并非对本公开实施例的限定。
基于相同的构思，本公开实施例还提供了一种通信装置。该通信装置可以是终端，也可以是终端中的部件。
可以理解的是，本公开实施例提供的标识上报装置为了实现上述功能，其包含了执行各个功能相应的硬件结构和/或软件模块。结合本公开实施例中所公开的各示例的单元及算法步骤，本公开实施例能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。本领域技术人员可以对每个特定的应用来使用不同的方法来实现所描述的功能，但是这种实现不应认为超出本公开实施例的技术方案的范围。
图4是根据一示例性实施例示出的一种通信装置的结构框图。如图4所示，通信装置包括接收单元101。
接收单元101用于接收网络设备发送的信息，所述信息用于终端管理AI模型。
一种实施方式中，管理AI模型包括管理至少一个AI功能和/或管理同一AI功能所包括的至少一个模型，其中，管理至少一个AI功能包括AI功能的激活，去激活以及不同AI功能之间的切换中的至少一项；管理同一AI功能所包括的至少一个模型包括模型的激活，去激活以及至少一个模型的切换。
一种实施方式中，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，所述不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
一种实施方式中，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，所述网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，所述终端分布信息包括以下信息中的至少一项：
室外终端数量；室内终端数量；室外终端数量与室内终端数量之间的比值。
一种实施方式中，所述波束信息包括第一集合与第二集合之间的波束信息；所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量；第二集合对应的波束数量；第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，所述第一集合为所述第二集合的子集；第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
一种实施方式中，所述第一集合不同于所述第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
一种实施方式中，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
一种实施方式中，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
一种实施方式中，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，所述信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
一种实施方式中，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
一种实施方式中，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；所述终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
一种实施方式中，所述AI模型用于波束预测。
基于相同的构思，本公开实施例还提供了一种通信装置。该装置可以是网络设备也可以是网络设备中的部件。
图5是根据一示例性实施例示出的一种通信装置的结构框图。如图5所示，本公开实施例提供一种通信装置，包括：发送单元201，用于发送信息至终端，所述信息用于所述终端管理人工智能AI模型。
一种实施方式中，所述管理AI模型包括管理至少一个AI功能和/或管理同一AI功能所包括的至少一个模型，其中，管理至少一个AI功能包括AI功能的激活，去激活以及不 同AI功能之间的切换中的至少一项；管理同一AI功能所包括的至少一个模型包括模型的激活，去激活以及至少一个模型的切换。
一种实施方式中，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，所述不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
一种实施方式中，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，所述网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，所述终端分布信息包括以下信息中的至少一项：室外终端数量；室内终端数量；室外终端数量与室内终端数量之间的比值。
一种实施方式中，所述波束信息包括第一集合与第二集合之间的波束信息；所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量；第二集合对应的波束数量；第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，所述第一集合为所述第二集合的子集；第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
一种实施方式中，所述第一集合不同于所述第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
一种实施方式中，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
一种实施方式中，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
一种实施方式中，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，所述信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
一种实施方式中，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小 区所属网络设备。
一种实施方式中，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；所述终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
一种实施方式中，所述AI模型用于波束预测。
基于相同的构思，本公开实施例还提供了一种通信系统。通信系统包括终端以及网络设备。其中终端例如包括手机(mobile phone)、可穿戴设备、物联网设备、具备通信功能的汽车、智能汽车、平板电脑(Pad)、带无线收发功能的电脑、虚拟现实(virtual reality，VR)终端设备、增强现实(augmented reality，AR)终端设备、工业控制(industrial control)中的无线终端设备、无人驾驶(self-driving)中的无线终端设备、远程手术(remote medical surgery)中的无线终端设备、智能电网(smart grid)中的无线终端设备、运输安全(transportation safety)中的无线终端设备、智慧城市(smart city)中的无线终端设备、智慧家庭(smart home)中的无线终端设备中的至少一者，但不限于此。
其中接入网设备例如是将终端接入到无线网络的节点或设备，接入网设备可包括5G通信系统中的演进节点B(evolvedNodeB，eNB)、下一代演进节点B(next generation eNB，ng-eNB)、下一代节点B(next generation NodeB，gNB)、节点B(node B，NB)、家庭节点B(home node B，HNB)、家庭演进节点B(home evolved nodeB，HeNB)、无线回传设备、无线网络控制器(radio network controller，RNC)、基站控制器(base station controller，BSC)、基站收发台(base transceiver station，BTS)、基带单元(base band unit，BBU)、移动交换中心、6G通信系统中的基站、开放型基站(Open RAN)、云基站(CloudRAN)、其他通信系统中的基站、无线保真(wireless fidelity，WiFi)系统中的接入节点中的至少一者，但不限于此。
图6是本公开一示例性实施例示出的一种通信系统的架构示意图。如图6所示，本公开实施例涉及一种通信方法，上述方法包括：
步骤S1000：网络设备发送信息至终端，该信息用于终端管理AI模型。
一种实施方式中，所述管理AI模型包括管理至少一个AI功能和/或管理同一AI功能所包括的至少一个模型，其中，管理至少一个AI功能包括AI功能的激活，去激活以及不同AI功能之间的切换中的至少一项；管理同一AI功能所包括的至少一个模型包括模型的激活，去激活以及至少一个模型之间的切换。
一种实施方式中，所述信息包括参数信息，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，所述不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
一种实施方式，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，所述网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，所述终端分布信息包括以下信息中的至少一项：室外终端数量；室内终端数量；室外终端数量与室内终端数量之间的比值。
一种实施方式中，所述波束信息包括第一集合与第二集合之间的波束信息；所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量；第二集合对应的波束数量以及第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，所述第一集合为所述第二集合的子集；第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
一种实施方式中，所述第一集合不同于所述第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
一种实施方式中，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
一种实施方式中，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
一种实施方式中，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，所述信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
一种实施方式中，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
一种实施方式中，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；所述终端在RRC非激活态或RRC空闲态基于所述信 息进行AI模型管理。
一种实施方式中，所述AI模型用于波束预测。
步骤S1001：终端接收网络设备发送的信息，该信息用于终端管理AI模型。
一种实施方式中，所述管理AI模型包括管理至少一个AI功能和/或管理同一AI功能所包括的至少一个模型，其中，管理至少一个AI功能包括AI功能的激活，去激活以及不同AI功能之间的切换中的至少一项；管理同一AI功能所包括的至少一个模型包括模型的激活，去激活以及不同模型之间的切换。
一种实施方式中，所述信息包括参数信息，不同功能和/或不同模型对应不同的参数信息。
一种实施方式中，所述不同的参数信息包括以下信息中的至少一项不同：网络设备覆盖参数信息；终端分布信息；波束信息以及小区标识信息。
一种实施方式，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：市区宏小区、市区微小区、室内热点、密集城市以及乡村。
一种实施方式中，所述网络设备覆盖参数信息包括网络设备之间的间距。
一种实施方式中，所述终端分布信息包括室外终端数量与室内终端数量之间的比值。
一种实施方式中，所述波束信息包括第一集合与第二集合之间的波束信息；所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
一种实施方式中，第一集合与第二集合之间的波束信息以下至少一项：第一集合对应的波束数量；第二集合对应的波束数量以及第一集合对应的波束数量与第二集合对应的波束数量的比值。
一种实施方式中，所述第一集合为所述第二集合的子集；第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
一种实施方式中，所述第一集合不同于所述第二集合；第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
一种实施方式中，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
一种实施方式中，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
一种实施方式中，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
一种实施方式中，所述信息通过以下至少一种方式承载：系统信息、无线资源控制RRC信令、以及RRC释放消息。
一种实施方式中，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
一种实施方式中，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
一种实施方式中，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；所述终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
一种实施方式中，所述AI模型用于波束预测。
图7是根据一示例性实施例示出的一种用于通信的装置300的框图。例如，装置300可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等。
参照图7，装置300可以包括以下一个或多个组件：处理组件302，存储器304，电力组件306，多媒体组件308，音频组件310，输入/输出(I/O)接口312，传感器组件314，以及通信组件316。
处理组件302通常控制装置300的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件302可以包括一个或多个处理器320来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件302可以包括一个或多个模块，便于处理组件302和其他组件之间的交互。例如，处理组件302可以包括多媒体模块，以方便多媒体组件308和处理组件302之间的交互。
存储器304被配置为存储各种类型的数据以支持在装置300的操作。这些数据的示例包括用于在装置300上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电力组件306为装置300的各种组件提供电力。电力组件306可以包括电源管理系统，一个或多个电源，及其他与为装置300生成、管理和分配电力相关联的组件。
多媒体组件308包括在所述装置300和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板， 屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件308包括一个前置摄像头和/或后置摄像头。当装置300处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件310被配置为输出和/或输入音频信号。例如，音频组件310包括一个麦克风(MIC)，当装置300处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器304或经由通信组件313发送。在一些实施例中，音频组件310还包括一个扬声器，用于输出音频信号。
I/O接口312为处理组件302和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件314包括一个或多个传感器，用于为装置300提供各个方面的状态评估。例如，传感器组件314可以检测到装置300的打开/关闭状态，组件的相对定位，例如所述组件为装置300的显示器和小键盘，传感器组件314还可以检测装置300或装置300一个组件的位置改变，用户与装置300接触的存在或不存在，装置300方位或加速/减速和装置300的温度变化。传感器组件314可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件314还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件314还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件316被配置为便于装置300和其他设备之间有线或无线方式的通信。装置300可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在一个示例性实施例中，通信组件316经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件316还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，装置300可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种包括指令的非临时性计算机可读存储介质，例如包 括指令的存储器304，上述指令可由装置300的处理器320执行以完成上述方法。例如，所述非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁带、软盘和光数据存储设备等。
图8是根据一示例性实施例示出的一种用于通信的装置400的框图。例如，装置400可以被提供为一网络设备。参照图8，装置400包括处理组件422，其进一步包括一个或多个处理器，以及由存储器432所代表的存储器资源，用于存储可由处理组件422的执行的指令，例如应用程序。存储器432中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件422被配置为执行指令，以执行上述方法.
装置400还可以包括一个电源组件426被配置为执行装置400的电源管理，一个有线或无线网络接口450被配置为将装置400连接到网络，和一个输入输出(I/O)接口458。装置400可以操作基于存储在存储器432的操作系统，例如Windows ServerTM，Mac OS XTM，UnixTM,LinuxTM，FreeBSDTM或类似。
在示例性实施例中，还提供了一种包括指令的非临时性计算机可读存储介质，例如包括指令的存储器432，上述指令可由装置400的处理组件422执行以完成上述方法。例如，所述非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁带、软盘和光数据存储设备等。
进一步可以理解的是，本公开中“多个”是指两个或两个以上，其它量词与之类似。“和/或”，描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。字符“/”一般表示前后关联对象是一种“或”的关系。单数形式的“一种”、“所述”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。
进一步可以理解的是，本公开中涉及到的“响应于”“如果”等词语的含义取决于语境以及实际使用的场景，如在此所使用的词语“响应于”可以被解释成为“在……时”或“当……时”或“如果”。
进一步可以理解的是，术语“第一”、“第二”等用于描述各种信息，但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开，并不表示特定的顺序或者重要程度。实际上，“第一”、“第二”等表述完全可以互换使用。例如，在不脱离本公开范围的情况下，第一信息也可以被称为第二信息，类似地，第二信息也可以被称为第一信息。
进一步可以理解的是，本公开实施例中尽管在附图中以特定的顺序描述操作，但是不应将其理解为要求按照所示的特定顺序或是串行顺序来执行这些操作，或是要求执行全部 所示的操作以得到期望的结果。在特定环境中，多任务和并行处理可能是有利的。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本公开的其它实施方案。本申请旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。
应当理解的是，本公开并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利范围来限制。

Claims (42)

  1. 一种通信方法，其特征在于，由终端执行，所述方法包括：
    接收网络设备发送的信息，所述信息用于所述终端管理人工智能AI模型。
  2. 根据权利要求1所述的方法，其特征在于，所述管理AI模型包括：对所述信息中指示的至少一个AI功能进行操作或同一AI功能的至少一个模型进行操作。
  3. 根据权利要求1或2所述的方法，其特征在于，所述信息包括参数信息，不同功能和/或不同模型对应不同的参数信息。
  4. 根据权利要求3所述的方法，其特征在于，所述不同的参数信息包括以下信息中的至少一项不同：
    网络设备覆盖参数信息；
    终端分布信息；
    波束信息；
    小区标识信息。
  5. 根据权利要求4所述的方法，其特征在于，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：
    市区宏小区、市区微小区、室内热点、密集城市以及乡村。
  6. 根据权利要求4所述的方法，其特征在于，所述网络设备覆盖参数信息包括网络设备之间的间距。
  7. 根据权利要求4所述的方法，其特征在于，所述终端分布信息包括以下信息中的至少一项：
    室外终端数量；
    室内终端数量；
    室外终端数量与室内终端数量之间的比值。
  8. 根据权利要求4所述的方法，其特征在于，所述波束信息包括第一集合与第二集合之间的波束信息；
    所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
  9. 根据权利要求8所述的方法，其特征在于，第一集合与第二集合之间的波束信息以下至少一项：
    第一集合对应的波束数量；
    第二集合对应的波束数量；
    第一集合对应的波束数量与第二集合对应的波束数量的比值。
  10. 根据权利要求8所述的方法，其特征在于，所述第一集合为所述第二集合的子集；
    第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
  11. 根据权利要求8所述的方法，其特征在于，所述第一集合不同于所述第二集合；
    第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
  12. 根据权利要求8至11中任意一项所述的方法，其特征在于，所述第一集合为AI模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
  13. 根据权利要求4所述的方法，其特征在于，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
  14. 根据权利要求4所述的方法，其特征在于，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
  15. 根据权利要求1所述的方法，其特征在于，所述信息通过以下至少一种方式承载：
    系统信息、无线资源控制RRC信令、以及RRC释放消息。
  16. 根据权利要求15所述的方法，其特征在于，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
  17. 根据权利要求16所述的方法，其特征在于，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
  18. 根据权利要求15所述的方法，其特征在于，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；
    所述终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
  19. 根据权利要求1-18中任意一项所述的方法，其特征在于，所述AI模型用于波束预测。
  20. 一种通信方法，其特征在于，由网络设备执行，所述方法包括：
    发送信息至终端，所述信息用于所述终端管理人工智能AI模型。
  21. 根据权利要求20所述的方法，其特征在于，所述管理AI模型包括：对所述信息中指示的至少一个AI功能进行操作或同一AI功能的至少一个模型进行操作。
  22. 根据权利要求20或21所述的方法，其特征在于，所述信息包括参数信息，不同功能和/或不同模型对应不同的参数信息。
  23. 根据权利要求22所述的方法，其特征在于，所述不同的参数信息包括以下信息中的至少一项不同：
    网络设备覆盖参数信息；
    终端分布信息；
    波束信息；
    小区标识信息。
  24. 根据权利要求23所述的方法，其特征在于，所述网络设备覆盖参数信息包括部署类型，所述部署类型以下至少一项：
    市区宏小区、市区微小区、室内热点、密集城市以及乡村。
  25. 根据权利要求23所述的方法，其特征在于，所述网络设备覆盖参数信息包括网络设备之间的间距。
  26. 根据权利要求23所述的方法，其特征在于，所述终端分布信息包括以下信息中的至少一项：
    室外终端数量；
    室内终端数量；
    室外终端数量与室内终端数量之间的比值。
  27. 根据权利要求23所述的方法，其特征在于，所述波束信息包括第一集合与第二集合之间的波束信息；
    所述第一集合和所述第二集合中包括网络设备发送波束和/或终端设备接收波束。
  28. 根据权利要求27所述的方法，其特征在于，第一集合与第二集合之间的波束信息以下至少一项：
    第一集合对应的波束数量；
    第二集合对应的波束数量；
    第一集合对应的波束数量与第二集合对应的波束数量的比值。
  29. 根据权利要求27所述的方法，其特征在于，所述第一集合为所述第二集合的子集；
    第一集合与第二集合之间的波束信息包括位置信息，所述位置信息为所述第一集合所包括波束在所述第二集合所包括波束中的位置。
  30. 根据权利要求27所述的方法，其特征在于，所述第一集合不同于所述第二集合；
    第一集合与第二集合之间的波束信息包括波束映射关系，所述波束映射关系为所述第一集合所包括波束与所述第二集合所包括波束之间的映射关系。
  31. 根据权利要求27至30中任意一项所述的方法，其特征在于，所述第一集合为AI 模型输入值对应的波束集合，所述第二集合为模型输出对应的波束集合。
  32. 根据权利要求23所述的方法，其特征在于，所述波束信息包括波束类型，所述波束类型包括离散傅里叶变换DFT波束，或非DFT波束。
  33. 根据权利要求23所述的方法，其特征在于，所述小区标识信息包括服务小区标识和邻小区标识中的至少一项。
  34. 根据权利要求20所述的方法，其特征在于，所述信息通过以下至少一种方式承载：
    系统信息、无线资源控制RRC信令、以及RRC释放消息。
  35. 根据权利要求34所述的方法，其特征在于，所述RRC信令包括RRC重配置信息，所述RRC重配置信息包括目标小区的所述信息，所述目标小区为所述终端将切换接入的目标小区。
  36. 根据权利要求35所述的方法，其特征在于，所述目标小区所属网络设备发送所述目标小区的所述信息给服务小区所属网络设备。
  37. 根据权利要求34所述的方法，其特征在于，所述RRC释放消息用于所述终端由RRC连接态转换为RRC非激活态或RRC空闲态时的参数配置；
    所述终端在RRC非激活态或RRC空闲态基于所述信息进行AI模型管理。
  38. 根据权利要求20-37中任意一项所述的方法，其特征在于，所述AI模型用于波束预测。
  39. 一种通信装置，其特征在于，由终端执行，包括：
    接收单元，用于接收网络设备发送的信息，所述信息用于所述终端管理人工智能AI模型。
  40. 一种通信装置，其特征在于，有网络设备执行，包括：
    发送单元，用于发送信息至终端，所述信息用于所述终端管理人工智能AI模型。
  41. 一种通信装置，其特征在于，包括：
    处理器；用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为：执行权利要求1至19中任意一项所述的通信方法，或执行权利要求20至38中任意一项所述的通信方法。
  42. 一种存储介质，其特征在于，所述存储介质中存储有指令，当所述存储介质中的指令由终端的处理器执行时，使得终端能够执行权利要求1至19中任意一项所述的通信方法，或
    当所述存储介质中的指令由网络设备的处理器执行时，使得网络设备能够执行权利要求20至38中任意一项所述的通信方法。
PCT/CN2023/086333 2023-04-04 2023-04-04 一种通信方法、装置及存储介质 Ceased WO2024207243A1 (zh)

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