Model construction method and apparatus

Abstract

A model configuration method and apparatus are provided. The method includes an operation, administration and maintenance OAM receiving a first model request message from a first access network device. The OAM determines a first model based on the first model request message and a first mapping relationship, where the first mapping relationship includes a mapping relationship between the model, a model application scenario and a model function, or the first mapping relationship includes a mapping relationship between the model, a model performance level and a model function. The OAM sends information about the first model to the first access network device. According to the method and apparatus of the present application, a first model corresponding to an application scenario or a performance indicator is configured for the first access network device, which can improve the performance and speed of model inference.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a model configuration method and apparatus.

Background Art

[0002] In a wireless communication network, for example, in a mobile communication network, as the services supported by the network are becoming increasingly diversified, the requirements that need to be satisfied are also becoming increasingly diversified. For example, the network must have the ability to support ultra-high speed, ultra-low latency, and / or ultra-large scale connection. This feature makes network planning, network configuration, and / or resource scheduling increasingly complex. These new requirements, scenarios, and functions have brought unprecedented challenges to network planning, operation, and maintenance, as well as efficient operation. To meet this challenge, artificial intelligence technology may be introduced into wireless communication technology to implement network intelligence. Based on this, how to configure an artificial intelligence model for access network devices in the network is a problem worthy of consideration.

Summary of the Invention

[0003] This application provides a model configuration method and apparatus for configuring a first model corresponding to an application scenario or a performance level for a first access network device, and improving the performance and speed of model prediction.

[0004] According to a first aspect, a model configuration method is provided. The method may be executed by operation, administration, and maintenance (OAM), or may be a component (such as a processor, a chip, etc.) configured in OAM, or may be a software module, etc. The method is Receiving a first model request message from a first access network device, and determining a first model based on the first model request message and a first mapping relationship, where the first mapping relationship includes a mapping relationship between a model, a model application scenario, and a model function, or the first mapping relationship includes a mapping relationship between a model, a model performance level, and a model function; and sending information about the first model to the first access network device. Optionally, the mapping relationship between a model, a model performance level, and a model function may be replaced by a mapping relationship between a model, a model performance indicator, and a model function.

[0005] According to the above method, the requirements for the model performance indicator of models implementing the same function are different in different application scenarios. In this application, OAM stores a first mapping relationship, and the first mapping relationship is a mapping relationship between a model, a model application scenario, and a model function. Based on the mapping relationship, for the same model function, different models corresponding to the scenarios can be assigned in different application scenarios, so that the model satisfies the requirements for the performance indicator in the scenario corresponding to the model. Compared with the case of using the same model for model inference in any scenario, the solution of this application can improve prediction performance, prediction speed, etc. Alternatively, in this application, OAM establishes a first mapping relationship, and the first mapping relationship is a mapping relationship between a model, a model performance level (or a model performance indicator), and a model function. The requirements for the model performance indicator of a model are different in different applications, and there is a correspondence between the model performance indicator and the performance level, so OAM can configure different models for different model performance indicators in different applications based on the first mapping relationship. Compared with the case of using the same model for model inference under the model performance indicator in any scenario, the solution of this application can improve prediction performance, prediction speed, etc.

[0006] In a possible design, the first model request message indicates the model application scenario and model functions of the first model.

[0007] In a possible design, the first model request message indicates the model functions and model performance level of the first model, or indicates the model functions and model performance indicators of the first model.

[0008] In a possible design, the information about the first model is the following of the first model: Model index, model structure information, model parameters, model input format, model output format, model performance indicators, model application scenario, model functions, or training parameters indicates at least one of them.

[0009] According to the above method, the OAM sends the information about the first model to the first access network device. The first access network device can determine or restore the first model based on the information about the first model. Optionally, the information about the first model may include training parameters, and the first access network device can subsequently continue to train the first model based on the training parameters to satisfy various requirements for the model.

[0010] In a possible design, the method further includes receiving first feedback information from the first access network device and updating the first model based on the first feedback information.

[0011] In a possible design, the first feedback information indicates the actual accuracy of the prediction information output by the first model, and updating the first model based on the feedback information includes retraining and updating the first model when the actual accuracy of the prediction information output by the first model is less than a threshold.

[0012] When determining the first model according to the above method, the first access network device may perform model inference based on the first model, and the inference result may be called prediction information. The first feedback information is determined based on the prediction information. The first feedback information includes the actual accuracy of the prediction information output by the first model, and the first feedback information is fed back to the OAM. The OAM updates the first model based on the first feedback information so as to improve the accuracy of the inference result of the first model.

[0013] In a possible design, the first feedback information indicates a scenario change instruction, a model performance level change instruction, or a model performance indicator change instruction. The fact that the first model is updated based on the first feedback information includes selecting a second model for the terminal device based on the scenario change instruction, the model performance level change instruction, or the model performance indicator change instruction, and sending information about the second model to the first access network device. The second model is for updating the first model.

[0014] Similar to the above method, the difference is that in this design, when the application scenario, the model performance level, or the model performance indicator changes, the first access network device sends instruction information indicating an application scenario change, a model performance level change, or a model performance indicator change to the OAM, and the OAM reassigns a model to the first access network device based on the changed application scenario, model performance level, or model performance indicator for model inference, and it can be guaranteed as much as possible that the model assigned to the first access network device meets the requirements for the application scenario, model performance level, and model performance indicator.

[0015] In a possible design, the method further includes receiving, from a second access network device, a second model request message indicating an index of a first model; determining the first model based on the index of the first model; and sending information about the first model to the second access network device.

[0016] According to a second aspect, a model configuration method is provided. For the advantageous effects of the method on the first access network device side corresponding to the first aspect, refer to the first aspect. The method may be executed by the first access network device, or may be a component (such as a processor, a chip, etc.) configured in the first access network device, or may be a software module, etc. The method is to send a first model request message to operation, administration, and maintenance OAM, where the first model request message indicates a model application scenario and model functions of the first model, or the first model request message indicates model functions and a model performance level of the first model, or the first model request message indicates model functions and a model performance indicator of the first model, including sending and receiving information about the first model from OAM.

[0017] In a possible design, before sending the first model request message to OAM, the method further includes determining whether an artificial intelligence AI-based enhanced service needs to be provided.

[0018] In a possible design, the information about the first model includes at least one of the following of the first model: model index, model structure information, model parameters, model input format, model output format, model performance indicator, model application scenario, model functions, or training parameters indicating at least one of them.

[0019] In a possible design, the method further includes performing model inference based on the first model to obtain prediction information.

[0020] In a possible design, the method further includes sending prediction information including the prediction result to a terminal device.

[0021] In a possible design, the prediction information further includes an accuracy for predicting the predicted outcome.

[0022] In a possible design, the method further includes receiving second feedback information from the terminal device, the second feedback information indicating an actual accuracy of the prediction information.

[0023] In a possible design, the method further includes sending first feedback information to the OAM indicating an actual accuracy of the forecast information, an application scenario change instruction, a model performance level change instruction, or a model performance indicator change instruction.

[0024] It should be noted that in the present application, the first model may provide an artificial intelligence (AI) or machine learning (ML) inference service for the terminal device, or may provide an AI or ML inference service for the first access network device. When the first model is configured to provide an AI or ML inference service for the terminal device, the method further includes the first RAN node sending inference information (or called prediction information) of the first model to the terminal device. The terminal device sends second feedback information to the first access network device based on the inference information, where the second feedback information can indicate the actual accuracy of the inference information, and the first access network device can directly forward the second feedback information to the OAM. Alternatively, the first access network device obtains the actual accuracy of the inference information based on the second feedback information, and sends first feedback information indicating the actual accuracy of the inference information, etc., to the OAM. The OAM can update the first model based on the actual accuracy of the inference information to improve the inference performance, inference speed, etc. of the first model.

[0025] In a possible design, the method further includes determining a target cell of the terminal device and sending an index of a first model to a second access network device corresponding to the target cell.

[0026] In a possible design, the method further includes sending first indication information to a second access network device corresponding to the target cell, where the first indication information indicates type information of input data of the first model.

[0027] In a possible design, the method further includes sending historical data information required for model inference to the second access network device.

[0028] In a possible design, the method is to receive second indication information from the second access network device, where the second indication information indicates one of the following: whether the terminal Device is handed over to the target cell, instructing the first access network device to delete the first model, or instructing the first access network device to release corresponding computing resources, and further includes deleting the first model to release corresponding computing resources.

[0029] According to a third aspect, a model configuration method is provided. For the advantageous effects of the method on the first access network device side corresponding to the first aspect, refer to the first aspect. The method may be executed by the second access network device, or may be a component (such as a processor, a chip, etc.) configured in the second access network device, or may be a software module, etc. The method includes receiving an index of a first model from the first access network device, sending a second model request message indicating the index of the first model to operation, administration, and maintenance (OAM), and receiving information about the first model from OAM.

[0030] In a possible design, information about the first model is the following of the first model: model index, model structure information, model parameters, model input format, model output format, model performance indicator, model application scenario, model function, or training parameters indicates at least one of the following.

[0031] In a possible design, the method further includes receiving, from a first access network device, first indication information indicating type information of input data of the first model.

[0032] In a possible design, the method further includes receiving, from the first access network device, historical data information required for model inference, and in model inference, the historical data information is for determining the input data of the first model.

[0033] In a possible design, the method further includes sending second indication information to the first access network device, where the second indication information indicates one of the following: the terminal Device is handed over to the target cell, instructing the first access network device to delete the first model, or instructing the first access network device to release the corresponding computing resources.

[0034] According to a fourth aspect, an apparatus is provided. For advantageous effects, see the description of the first aspect. The apparatus may be an apparatus configured in operation, administration, and maintenance OAM, or OAM, or an apparatus used in compatibility with OAM.

[0035] In a design, the apparatus may include corresponding units that are in one-to-one correspondence with the methods / operations / steps / actions described in the first aspect. The units may be implemented by using a hardware circuit, software, or a combination of a hardware circuit and software.

[0036] For example, the apparatus may include a processing unit and a communication unit, and the processing unit and the communication unit may perform corresponding functions in any design example of the first aspect.

[0037] The communication unit is configured to receive a first model request message from a first access network device. The processing unit is configured to determine a first model based on the first model request message and a first mapping relationship, where the first mapping relationship includes a mapping relationship between a model, a model application scenario, and a model function, or the first mapping relationship includes a mapping relationship between a model, a model performance level, and a model function. The communication unit is further configured to send information about the first model to the first access network device. For the specific execution process of the processing unit and the communication unit, refer to the first aspect. Details are not described here again.

[0038] For example, the apparatus includes a memory configured to implement the method described in the first aspect. The apparatus may further include a memory configured to store instructions and / or data. The memory is coupled to a processor. When executing the program instructions stored in the memory, the processor can implement the method described in the first aspect. The apparatus may further include a communication interface, which is used by the apparatus to communicate with other devices. For example, the communication interface may be a transceiver, a circuit, a bus, a module, a pin, or other types of communication interfaces, and the other devices may be network devices, etc. In a possible design, the apparatus a memory configured to store program instructions, a communication interface configured to receive a first model request message from a first access network device, and a processor configured to determine a first model based on the first model request message and a first mapping relationship. The first mapping relationship includes the mapping relationship between the model, the model application scenario, and the model function, or the first mapping relationship includes the mapping relationship between the model, the model performance level, and the model function. The communication unit is further configured to send information about the first model to the first access network device.

[0039] For the specific execution process of the communication interface and the processor, please refer to the first aspect. Details are not described here again.

[0040] According to the fifth aspect, a device is provided. For the advantageous effects, please refer to the description of the second aspect. The device may be the first access network device, or a device configured in the first access network device, or a device used in compatibility with the first access network device. In the design, the device may include a unit corresponding one-to-one to the method / operation / step / action described in the second aspect. The unit may be implemented by using a hardware circuit, software, or a combination of a hardware circuit and software.

[0041] For example, the device may include a processing unit and a communication unit, and the processing unit and the communication unit may execute the corresponding functions in any design example of the second aspect.

[0042] The communication unit is configured to send the first model request message to the operation, administration, and maintenance OAM, where the first model request message indicates the model application scenario and the model function of the first model, or the first model request message indicates the model function and the model performance level of the first model, and to receive information about the first model from the OAM. The processing unit is configured to determine the first model based on the information about the first model. For the specific execution process of the processing unit and the communication unit, please refer to the second aspect. Details are not described here again.

[0043] For example, the apparatus includes a memory configured to implement the methods described in the second aspect. The apparatus may further include a memory configured to store instructions and / or data. The memory is coupled to a processor. When executing the program instructions stored in the memory, the processor can implement the methods described in the second aspect. The apparatus may further include a communication interface, which is used by the apparatus to communicate with other devices. For example, the communication interface may be a transceiver, a circuit, a bus, a module, a pin, or other types of communication interfaces, and the other devices may be network devices, etc. In a possible design, the apparatus a memory configured to store program instructions, and a communication interface configured to send a first model request message to operation, administration, and maintenance OAM, the first model request message indicating a model application scenario and model functions of the first model, or the first model request message indicating model functions and a model performance level of the first model, and receiving information about the first model from the OAM, a processor configured to determine the first model based on the information about the first model.

[0044] For the specific execution processes of the communication interface and the processor, please refer to the second aspect. Details are not described here again.

[0045] According to the sixth aspect, an apparatus is provided. For the advantageous effects, please refer to the description of the third aspect. The apparatus may be a second access network device, or an apparatus configured in the second access network device, or an apparatus used in combination with the second access network device. In a design, the apparatus may include units corresponding one-to-one to the methods / operations / steps / actions described in the third aspect. The units may be implemented by using a hardware circuit, software, or a combination of a hardware circuit and software.

[0046] For example, the apparatus may include a processing unit and a communication unit, and the processing unit and the communication unit may perform corresponding functions in any design example of the second aspect.

[0047] The communication unit is configured to receive an index of a first model from a first access network device, send a second model request message indicating the index of the first model to operation, administration, and maintenance OAM, and receive information about the first model from OAM. The processing unit is configured to determine the first model based on the information about the first model. For the specific execution process of the processing unit and the communication unit, please refer to the third aspect. Details are not described here again.

[0048] For example, the apparatus includes a memory configured to implement the method described in the third aspect. The apparatus may further include a memory configured to store instructions and / or data. The memory is coupled to a processor. When executing the program instructions stored in the memory, the processor can implement the method described in the third aspect. The apparatus may further include a communication interface, and the communication interface is used by the apparatus to communicate with other devices. For example, the communication interface may be a transceiver, a circuit, a bus, a module, a pin, or other types of communication interfaces, and the other devices may be network devices, etc. In a possible design, the apparatus a memory configured to store program instructions, a communication interface configured to receive an index of a first model from a first access network device, send a second model request message indicating the index of the first model to operation, administration, and maintenance OAM, and receive information about the first model from OAM, a processor configured to determine the first model based on the information about the first model, and includes.

[0049] For the specific execution processes of the communication interface and the processor, please refer to the third aspect. Details will not be described again here.

[0050] According to the seventh aspect, the embodiments of the present application further provide a computer-readable storage medium including instructions. When the instructions are executed by a computer, the computer can execute the method in any one of the first aspect, the second aspect, or the third aspect.

[0051] According to the eighth aspect, the present application further provides a chip system. The chip system includes a processor and may further include a memory configured to implement the method in any one of the first aspect, the second aspect, or the third aspect. The chip system may include a chip, or may include a chip and other discrete components.

[0052] According to the ninth aspect, the present application further provides a computer program product including instructions. When the instructions are executed by a computer, the computer can execute the method in any one of the first aspect, the second aspect, or the third aspect.

[0053] According to the tenth aspect, the present application further provides a system. The system includes the device in the fourth aspect and the device in the fifth aspect, or the device in the fourth aspect, the device in the fifth aspect, and the device in the sixth aspect.

Brief Description of Drawings

[0054]

Figure 1

Figure 2

Figure 3

Figure 4a

Figure 4b

Figure 4c

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Embodiments for Carrying Out the Invention

[0055] FIG. 1 is a diagram of the architecture of a communication system 1000 to which the present application is applied. As shown in FIG. 1, the communication system includes a radio access network 100 and a core network 200. Optionally, the communication system 1000 may further include the Internet 300. The radio access network 100 may include at least one access network device (e.g., 110a and 110b in FIG. 1), and may further include at least one terminal device (e.g., 120a to 120j in FIG. 1). The terminal device is connected to the access network device in a wireless manner, and the access network device is connected to the core network in a wireless or wired manner. The core network device and the access network device may be different physical devices independent of each other, or the functions of the core network device and the logical functions of the access network device may be incorporated into the same physical device, or a part of the functions of the core network device and a part of the functions of the access network device may be incorporated into one physical device. The terminal devices may be connected to each other in a wired or wireless manner, and the access network devices may be connected to each other in a wired or wireless manner. FIG. 1 is merely a diagram. The communication system may further include other network devices, for example, a wireless relay device, a wireless backhaul device, etc., but these are not shown in FIG. 1.

[0056] The access network device may be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next generation NodeB (gNB) in a 5th generation (5G) mobile communication system, an access network device in an open radio access network (O-RAN), a next generation base station in a 6th generation (6G) mobile communication system, a base station in a future mobile communication system, an access node in a wireless fidelity (Wi-Fi) system, etc., or may be a module or a unit, for example, a central unit (CU) that realizes a part of the functions of a base station, a distributed unit (DU), a central unit control plane (CU-CP) module, or a central unit user plane (CU-UP) module. The access network device may be a macro base station (e.g., 110a in FIG. 1), or may be a micro base station or an indoor base station (e.g., 110b in FIG. 1), or may be a relay node, a donor node, etc. The specific technologies and specific device forms used by the access network device are not limited in this application.

[0057] In the present application, a device configured to implement the functions of an access network device may be an access network device, or a device capable of supporting the access network device in implementing its functions, such as a chip system, a hardware circuit, a software module, or a combination of a hardware circuit and a software module. The device may be installed in the access network device or may be used in compatibility with the access network device. In the present application, the chip system may include a chip or may include a chip and other discrete components. For the sake of simplicity in description, the following uses an example where the device configured to implement the functions of an access network device is an access network device and the access network device is a RAN node to describe the technical solution provided in the present application.

[0058] (1) Protocol layer structure

[0059] Communication between an access network device and a terminal device follows a specific protocol layer structure. The protocol layer structure may include a control plane protocol layer structure and a user plane protocol layer structure. For example, the control plane protocol layer structure may include the functions of protocol layers such as a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a media access control (MAC) layer, and a physical layer. For example, the user plane protocol layer structure may include the functions of protocol layers such as a PDCP layer, an RLC layer, a MAC layer, and a physical layer. In a possible implementation, a service data adaptation protocol (SDAP) layer may be further included above the PDCP layer.

[0060] Optionally, the protocol layer structure between the access network device and the terminal device may further include an artificial intelligence (AI) layer configured to transmit data related to the AI function.

[0061] (2) Central unit (CU) and distributed unit (DU)

[0062] The access device may include a CU and a DU. A plurality of DUs may be controlled by one CU in a centralized manner. For example, the interface between the CU and the DU may be called the F1 interface. The control plane (CP) interface may be F1-C, and the user plane (UP) interface may be F1-U. The specific name of each interface is not limited in this application. The CU and DU may be divided based on the protocol layer of the wireless network. For example, the functions of the PDCP layer and the protocol layers above the PDCP layer are set in the CU, and the functions of the protocol layers below the PDCP layer (for example, the RLC layer and the MAC layer) are set in the DU. As another example, the functions of the protocol layers above the PDCP layer are set in the CU, and the functions of the PDCP layer and the protocol layers below the PDCP layer are set in the DU. This is not limited.

[0063] The above division of the processing functions of the CU and DU based on protocol layers is only an example, and alternatively, other divisions may be possible. For example, the CU or DU may be defined to have more protocol layer functions. As another example, the CU or DU may alternatively be defined to have a part of the processing functions of the protocol layer. In the design, a part of the functions of the RLC layer and the functions of the protocol layers above the RLC layer are set in the CU, and the remaining functions of the RLC layer and the functions of the protocol layers below the RLC layer are set in the DU. In other designs, the division of the functions of the CU or DU may alternatively be performed based on service type or other system requirements. For example, the division may be performed based on latency. Functions whose processing time needs to meet the latency requirements are set in the DU, and functions whose processing time does not need to meet the latency requirements are set in the CU. In other designs, the CU may alternatively have one or more functions of the core network. For example, the CU may be deployed on the network side to facilitate centralized management. In other designs, the radio unit (RU) of the DU is remotely deployed. Optionally, the RU may have radio frequency functions.

[0064] Optionally, DU and RU may be distinguished at the physical layer (PHY). For example, DU may implement the upper layer functions of the PHY layer, and RU may implement the lower layer functions of the PHY layer. When the PHY layer is for transmission, the functions of the PHY layer may include at least one of the following functions: addition of cyclic redundancy check (CRC) code, channel coding, rate matching, scrambling, modulation, layer mapping, precoding, resource mapping, physical antenna mapping, or radio frequency transmission. When the PHY layer is for reception, the functions of the PHY layer may include at least one of the following functions: CRC check, channel decoding, derate matching, descrambling, demodulation, layer demapping, channel detection, resource demapping, physical antenna demapping, or radio frequency reception. The upper layer functions of the PHY layer may include a part of the functions of the PHY layer. For example, a part of the functions is closer to the MAC layer. The lower layer functions of the PHY layer may include other parts of the functions of the PHY layer. For example, the said part of the functions is closer to the radio frequency function. For example, the upper layer functions of the PHY layer may include addition of CRC code, channel coding, rate matching, scrambling, modulation, and layer mapping, and the lower layer functions of the PHY layer may include precoding, resource mapping, physical antenna mapping, and radio frequency transmission functions. Alternatively, the upper layer functions of the PHY layer may include addition of CRC code, channel coding, rate matching, scrambling, modulation, layer mapping, and precoding. The lower layer functions of the PHY layer may include resource mapping, physical antenna mapping, and radio frequency transmission functions. For example, the upper layer functions of the PHY layer may include CRC check, channel decoding, derate matching, decoding, demodulation, and layer demapping, and the lower layer functions of the PHY layer may include channel detection, resource demapping, physical antenna demapping, and radio frequency reception functions.Alternatively, the upper layer functions of the PHY layer may include CRC check, channel decoding, rate matching, decoding, demodulation, layer demapping, and channel detection, and the lower layer functions of the PHY layer may include resource demapping, physical antenna demapping, and radio frequency reception functions.

[0065] For example, the functions of the CU may be implemented by one entity, or may be implemented by different entities. For example, the functions of the CU may be further divided. Specifically, the control plane and user plane of the CU are separated and implemented by different entities, namely the control plane CU entity (i.e., the CU-CP entity) and the user plane CU entity (i.e., the CU-UP entity). The CU-CP entity and the CU-UP entity may be coupled to the DU so as to jointly realize the functions of the access network device.

[0066] Optionally, any one of the DU, CU, CU-CP, CU-UP, and RU may be a software module, a hardware structure, or a combination of a software module and a hardware structure. This is not limited. Different entities may exist in different forms, which is not limited. For example, the DU, CU, CU-CP, and CU-UP are software modules, and the RU is a hardware structure. These modules and the methods executed by these modules also fall within the scope of protection of the present disclosure.

[0067] In a possible implementation, the access network device includes a CU-CP, a CU-UP, a DU, and an RU. For example, the present application is executed by the DU, or the DU and the RU, or the CU-CP and the DU and the RU, or the CU-UP and the DU and the RU. This is not limited. The methods executed by the modules also fall within the scope of protection of the present application.

[0068] A terminal device may also be referred to as a terminal, a user equipment (UE), a mobile station, a mobile terminal device, etc. The terminal device is widely used for communication in various scenarios including, but not limited to, for example, the following scenarios: device-to-device (D2D), vehicle-to-everything (V2X), machine-type communication (MTC), internet of things (IOT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, etc. The terminal device may be a mobile phone, a tablet computer, a computer with a wireless transceiver function, a wearable device, a vehicle, a drone, a helicopter, an airplane, a ship, a robot, a robotic arm, a smart home device, etc. The specific technologies and specific device forms used by the terminal device are not limited in this application.

[0069] In this application, the device configured to implement the functions of the terminal device may be the terminal device, or a device that can support the terminal device in implementing the functions, for example, a chip system, a hardware circuit, a software module, or a hardware circuit and a software module. The device may be installed in the terminal device or may be used in compatibility with the terminal device. For the sake of easy description, the following uses an example where the device configured to implement the functions of the terminal device is the terminal device and the terminal device is a UE to describe the technical solution provided in this application.

[0070] The base station and the terminal device may be fixed or movable. The base station and / or the terminal device may be deployed on the ground, including indoor or outdoor scenarios, and handheld or vehicle-mounted scenarios, or may be deployed on water, or may be deployed on aircraft, balloons, and artificial satellites in the air. The application scenarios of the base station and the terminal device are not limited in this application. The base station and the terminal device may be deployed in the same scenario or different scenarios. For example, both the base station and the terminal device are deployed on the ground. Alternatively, the base station is deployed on the ground and the terminal is deployed on water. Examples are not listed one by one.

[0071] The roles of the base station and the terminal device may be relative. For example, the helicopter or unmanned aircraft 120i in FIG. 1 may be configured as a mobile base station. In the case of the terminal device 120j accessing the wireless access network 100 via 120i, the terminal device 120i is a base station, while in the case of the base station 110a, 120i is a terminal device. In other words, 110a and 120i communicate with each other based on the wireless air interface protocol. 110a and 120i may alternatively communicate with each other based on the interface protocol between base stations. In this case, for 110a, 120i is also a base station. Therefore, the base station and the terminal device may both be collectively referred to as communication devices. 110a and 110b in FIG. 1 may be called communication devices having the functions of a base station, and 120a to 120j in FIG. 1 may be called communication devices having the functions of a terminal device.

[0072] In this application, an independent network element (for example, referred to as an AI network element or an AI node) can be introduced into the communication system shown in FIG. 1 to implement AI-related operations. The AI network element may be directly connected to an access network device within the communication system, or may be indirectly connected to the access network device via a third-party network element. The third-party network element may be a core network element such as an authentication management function (AMF) or a user plane function (UPF). Alternatively, the AI function, AI module, or AI entity may be configured in other network elements within the communication system to implement AI-related operations. For example, the other network element may be an access network device (for example, gNB), a core network device, or operation, administration, and maintenance (OAM). In this case, the network element that executes the AI-related operation is a network element equipped with a built-in AI function. OAM is configured to perform operations such as operation, management, and maintenance on the access network device and / or the core network device.

[0073] In the present application, as shown in FIG. 2 or FIG. 3, the AI model may be deployed on at least one of a core network device, an access network device, a terminal device, OAM, etc., and the corresponding functions are implemented by using the AI model. In the present application, the AI models deployed on different nodes may be the same or different. The models are different with respect to at least one of the following: different structural parameters of the models, such as different numbers of layers and / or weights of the models, different input parameters of the models, or different output parameters of the models. The different input parameters and / or different output parameters of the models may be described as different functions of the models. Different from FIG. 2, in FIG. 3, the access network device is divided into a CU and a DU. Optionally, the CU and the DU may be the CU and the DU in the O-RAN architecture. One or more AI models may be deployed in the CU, and / or one or more AI models may be deployed in the DU. Optionally, the CU in FIG. 3 may be further divided into a CU-CP and a CU-UP. Optionally, one or more AI models may be deployed in the CU-CP, and / or one or more AI models may be deployed in the CU-UP. Optionally, in FIG. 2 or FIG. 3, the OAM of the access network device and the OAM of the core network device may be deployed separately.

[0074] Optionally, FIG. 4a shows the architecture of the communication system according to the present application. As shown in FIG. 4a, in the first design, the access network device includes a quasi-real-time access network intelligent controller (RIC) module configured to perform model training and inference. For example, the quasi-real-time RIC can be configured to train an AI model and perform inference by using the AI model. For example, the quasi-real-time RIC can obtain network-side and / or terminal-side information from at least one of the CU, DU, or RU, and the information can be used as training data or inference data. Optionally, the quasi-real-time RIC can present the inference result to at least one of the CU, DU, RU, or terminal device. Optionally, the CU and DU can exchange inference effects. Optionally, the DU and RU can exchange inference results. For example, the quasi-real-time RIC presents the inference result to the DU, and the DU transfers the inference result to the RU.

[0075] Alternatively, in the second design, as shown in FIG. 4a, the access network device can include a non-real-time RIC (optionally, the non-real-time RIC can be located in the OAM or the core network device) configured to perform model training and inference. For example, the non-real-time RIC can be configured to train an AI model and perform inference by using the model. For example, the non-real-time RIC can obtain network-side and / or terminal-side information from at least one of the CU, DU, or RU, and the information can be used as training data or inference data. The inference result can be presented to at least one of the CU, DU, RU, or terminal device. Optionally, the CU and DU can exchange inference results. Optionally, the DU and RU can exchange inference results. For example, the non-real-time RIC presents the inference result to the DU, and the DU transfers the inference result to the RU.

[0076] Alternatively, in the third design, as shown in Figure 4a, the access network device includes a quasi-real-time RIC, and the non-real-time RIC is located outside the access network device (optionally, the non-real-time RIC may be located in the OAM or the core network device). Similar to the second design above, the non-real-time RIC may be configured to perform model training and inference, and / or, similar to the first design, the quasi-real-time RIC may be configured to perform model training and inference, and / or, the non-real-time RIC performs model training, the quasi-real-time RIC obtains AI model information from the non-real-time RIC, obtains network-side and / or terminal-side information from at least one of the CU, DU, or RU, and may obtain an inference result by using the information and the AI model information. Optionally, the quasi-real-time RIC may present the inference result to at least one of the CU, DU, RU, or the terminal device. Optionally, the CU and the DU may exchange the inference result. Optionally, the DU and the RU may exchange the inference result. For example, the quasi-real-time RIC presents the inference result to the DU, and the DU transfers the inference result to the RU. For example, the quasi-real-time RIC is configured to train Model A and perform inference by using Model A. For example, the non-real-time RIC is configured to train Model B and perform inference by using Model B. For example, the non-real-time RIC is configured to train Model C and send information about Model C to the quasi-real-time RIC, and the quasi-real-time RIC uses Model C for inference.

[0077] Figure 4b is the architecture of another communication system according to the present application. Compared with Figure 4a, in Figure 4b, the CU is separated into a CU-CP and a CU-UP.

[0078] An AI model is a specific implementation of an AI function. An AI model represents the mapping relationship between the input and output of the model. The AI model may be a neural network, a linear regression model, a decision tree model, a support vector machine (SVM), a Bayesian network, a Q-learning model, or other machine learning models, etc. In this application, the AI function may include at least one of the following: data collection (collection of training data and / or inference data), data preprocessing, model training (or called model learning), model information release (model information setting), model verification, model inference, or release of inference results. Inference may sometimes be called prediction. In this application, the AI model may sometimes be abbreviated as the model.

[0079] Figure 4c is a diagram of the application architecture of the AI model. The data source is configured to store training data and inference data. The model training host analyzes or trains the training data supplied by the data source to obtain the AI model and deploys the AI model to the model inference node (model inference host). Optionally, the model training node may further update the AI model deployed to the model inference node. The model inference node may further feedback the relevant information of the deployed model to the model training node so that the model training node can optimize or update the deployed AI model, etc.

[0080] Obtaining an AI model through learning by a model training node is equivalent to the model training node obtaining the mapping relationship between the input and output of the model through learning by using training data. The model inference node uses the AI model to perform inference based on the inference data supplied by the data source and obtains an inference result. The method can also be described as follows: The model inference node inputs the inference data into the AI model and obtains an output by using the AI model. The output is the inference result. The inference result may indicate the set parameters used (executed) by the subject of the operation and / or the operations executed by the subject of the operation. The inference result is centrally planned by an actor entity and can be sent to one or more subjects of the operation (e.g., network entities) for the operation. Optionally, the actor entity or the subject of the operation may further feedback the parameters or measurements collected by the actor entity or the actor object to the data source. This process is sometimes called performance feedback, and the feedback parameters can be used as training data or inference data. Optionally, the actor entity or the actor object may further determine feedback information regarding the model performance based on the inference result output by the model inference node and feedback the feedback information to the model inference node. The model inference node may feedback the performance information of the model to the model training node based on the feedback information, whereby the model training node optimizes or updates the deployed AI model. This process may be called model feedback.

[0081] The AI model may be a neural network or other machine learning model. A neural network is used as an example. A neural network is a specific embodiment of machine learning technology. According to the universal approximation theorem, a neural network can theoretically approximate any continuous function, whereby the neural network has the function of learning any mapping. Therefore, a neural network can accurately perform abstract modeling for complex high-order problems.

[0082] The idea of a neural network is derived from the neuron structure of the brain tissue. Each neuron performs a weighted summation operation on the input values of the neuron and outputs the result of the weighted summation through an activation function. Figure 5 is a diagram of the structure of a neuron. The input values of the neuron are x = [x0, x1, ···, x n , and the weights corresponding to the input values are w = [w, w1, ···, w n , and it is assumed that the bias of the weighted summation is b. The form of the activation function can be diverse. It is assumed that the activation function of one neuron is y = f(z) = max(0, z). In this case, the output of the neuron is,

Equation

Equation

[0083] A neural network typically includes a multi-layer structure, and each layer may contain one or more neurons. As the depth and / or width of the neural network increases, the representation ability of the neural network is improved, and it can provide a more powerful information extraction ability and abstract modeling ability for complex systems. The depth of the neural network can refer to the number of layers included in the neural network, and the number of neurons included in each layer can be called the width of the layer. FIG. 6 is a diagram of the layer relationship of the neural network. In implementation, the neural network includes an input layer and an output layer. After performing neuron processing on the received input, the input layer of the neural network transfers the result to the output layer, and the output layer obtains the output result of the neural network. In other implementations, the neural network includes an input layer, a hidden layer, and an output layer. After performing neuron processing on the received input, the input layer of the neural network transfers the result to the intermediate hidden layer, and then the hidden layer transfers the calculation result to the output layer or an adjacent hidden layer, and finally, the output layer obtains the output result of the neural network. The neural network may include one hidden layer or a plurality of sequentially connected hidden layers. This is not limited. In the training process of the neural network, a loss function can be defined. The loss function represents the gap or difference between the output value of the neural network and the ideal target value of the neural network. The specific form of the loss function is not limited in this application. The training process of the neural network is a process of adjusting neural network parameters such as the number and width of the layers of the neural network, the weights of the neurons, and the parameters in the activation function of the neurons, so that the value of the loss function is smaller than the threshold or meets the target requirements.

[0084] In one solution, the RAN node supports multiple AI assistance cases. For example, mobility enhancement, load balancing, and network energy saving. The OAM is configured to train or maintain the AI model. The RAN node requests the AI model from the OAM and performs inference by using the AI model. How the OAM configures the corresponding model for the RAN node is the problem to be solved in this application. In the specification of this application, the OAM may be replaced by other nodes that can train or maintain the AI model, such as a cloud server, a core network device, or other possible devices.

[0085] As shown in FIG. 7, a procedure for a model configuration method is provided, including at least the following steps.

[0086] Step 701: The first RAN node sends a first model request message to the OAM, and correspondingly, the OAM receives the first model request message from the first RAN node.

[0087] For ease of distinction, the model request message sent by the first RAN node to the OAM is called the first model request message. The model request message sent by the second RAN node to the OAM is called the second model request message. The model request message is used by the RAN node to request the model from the OAM, and the name of the model request message is not limited. For example, the first model request message may be called the first message, and the second model request message may be called the second message. To distinguish different RAN nodes, these RAN nodes are called the first RAN node, the second RAN node, etc. The first RAN node and the second RAN node are different RAN nodes. The relationship between the first RAN node and the second RAN node is not limited.

[0088] In the design, the first RAN node may determine a service scenario and functional requirements, and determine a first model requirement message based on the service scenario and functional requirements. The service scenario and functional requirements are the service scenario and functional requirements of the RAN node. For example, the first model required by using the first model requirement message is for model inference related to the RAN node, for example, for cell energy saving. The first RAN node may determine a first model requirement message, etc., based on the service scenario and functional requirements of the RAN node. Alternatively, the service scenario and functional requirements are the service scenario and functional requirements of the UE. For example, the first model required by using the first model requirement message is for model inference related to the UE, for example, for predicting the movement trajectory of the UE. The first RAN node may determine a first model requirement message, etc., based on the service scenario and functional requirements of the UE. Alternatively, the first RAN node may determine a first model requirement message, etc., based on the service scenario and functional requirements of other nodes other than the RAN node and the UE. This is not limited. The first RAN node may obtain the service scenario and functional requirements of the UE based on the information reported by the UE. For example, the UE is in an autonomous driving scenario and sends a quality of service (QoS) prediction service request to the RAN node. The RAN node may obtain the service scenario and functional requirements of the UE as autonomous driving and QoS prediction. Alternatively, the first RAN node may obtain the service scenario and functional requirements of the UE through analysis. For example, the RAN node discovers that the UE is moving to the cell edge and requires a mobility enhancement service, and since the mobility enhancement service requires prediction of the UE's trajectory, the service scenario and functional requirements of the UE are mobility enhancement and prediction of the UE's trajectory information. Alternatively, the first RAN node may obtain the service scenario and functional requirements of the UE from other devices, and the other devices include, but are not limited to, devices such as minimization of drive tests (MDT) devices. Alternatively, the first RAN node stores the service scenario and functional requirements of the UE.For example, when accessing the first RAN node, the UE actively reports the service scenario and functional requirements of the UE to the first RAN node.

[0089] In this application, the service scenario includes, but is not limited to, at least one of the following: mobility enhancement, load balancing, virtual reality (VR), augmented reality (AR), vehicle-to-everything (autonomous driving), wireless robot cloud control, remote diagnosis with force feedback, high-definition video (e.g., 8K video), etc. For example, mobility enhancement refers to enhancing services to provide cell handover for a moving UE. For example, the RAN node predicts the target cell of the UE, handover time, etc. based on at least one of the UE movement trajectory, measurement report, cell load status, etc. The target cell of the UE can refer to the serving cell to which the UE is about to be handed over. The movement mode of the UE includes movement on a fixed route, movement on a non-fixed route, etc. Mobility enhancement can include mobility enhancement for vehicle movement on a fixed route, mobility enhancement for vehicle movement on a non-fixed route, mobility enhancement for a pedestrian scenario, etc. In remote diagnosis, also called remote consultation, a doctor diagnoses or performs surgery on a patient in another location through a network. The above force feedback is tactile feedback of force, which is a service with very high requirements for latency, and a latency prediction service needs to be provided. For example, if the predicted latency exceeds a threshold, the information transmission solution needs to be adjusted, or an alarm needs to be provided in advance to respond to an emergency.

[0090] In this application, the functional requirements include, but are not limited to, at least one of the following: UE trajectory prediction, load prediction, signal to interference plus noise ratio (SINR) prediction, reference signal received power (RSRP) prediction, reference signal received quality (RSRQ) prediction, latency prediction, QoS prediction, throughput prediction, cell energy saving, etc.

[0091] In the design, after the first RAN node determines the first model request message based on the service scenario and functional requirements, the first model request message indicates the service scenario and functional requirements. In other words, the first model request message indicates the model application scenario, model function, etc. of the required first model. The model application scenario corresponds to the above service scenario, and the model function corresponds to the above functional requirements. The indication in this application may include implicit indication, explicit indication, etc., which is not limited. For example, the first model request message may include the service scenario, functional requirements, etc. Alternatively, the first RAN node may directly send the service scenario and functional requirements to OAM, etc. For example, the combination of service scenario and functional requirements sent by the first RAN node to OAM may include mobility enhancement scenario and trajectory prediction, load balancing and trajectory prediction, autonomous driving and QoS prediction, VR / AR and QoS prediction, wireless robot cloud control and latency prediction, remote diagnosis with force feedback and latency prediction, etc.

[0092] In other designs, the process by which the first RAN node determines the first model requirement message based on the service scenario and functional requirements is as follows: the first RAN node determines the model functions and model performance indicators of the required first model based on the service scenario and functional requirements. The model functions correspond to the above functional requirements. The model performance indicators include at least one of the following: accuracy, computational complexity, memory occupancy, inference period, etc. Accuracy is the percentage of the currently predicted number of samples with respect to the total number of samples. Computational complexity is the number of calculations required by the model. The overall computational complexity of the model is equal to the sum of the computational complexities of the operators within the model, and the computational complexity feeds back the requirements of the model for the hardware computing unit. Alternatively, the computational complexity may be the size of the computing resources occupied when the model is running. Memory occupancy refers to the size of the memory and video RAM occupied when the model is running, or alternatively, memory occupancy may be referred to as the size of the model, etc. Inference time refers to the duration required by the model to infer the prediction result. The first model requirement message sent by the first RAN node to the OAM indicates the model functions and model performance indicators of the first model. For example, the first model requirement message includes the model functions and model performance indicators. Alternatively, the first RAN node may directly send the model functions and model performance indicators to the OAM. The model functions may also be referred to as model categories. For example, the first RAN node can learn through analysis that the QoS prediction service in the autonomous driving scenario needs to be provided to the UE, and the first RAN node can continue to analyze the performance indicators required for the QoS prediction service in the autonomous driving scenario, and the first RAN node sends the QoS prediction service and the corresponding performance indicators to the OAM. Alternatively, the first RAN node determines the model performance level based on the model performance indicators. There is a correspondence between the model performance level and the model performance indicators. For details, please refer to the following description. The first model requirement message sent by the first RAN node to the OA indicates the model functions and model performance levels of the required first model.

[0093] Optionally, before step 701, the method may further include: the first RAN node determining whether an AI-based enhanced service needs to be provided. If an AI-based enhanced service needs to be provided, step 701 is required to send a first model request message to the OAM. If an AI-based enhanced service does not need to be provided, the procedure shown in FIG. 7 is not executed. For example, in an AI-based mobility enhancement solution, the source RAN node may predict the target cell of the UE in an AI manner. However, if the UE is not located at the cell edge, the source RAN node does not need to predict the target cell of the UE. In other words, only when the UE is at the cell edge, the source RAN node needs to send a first model request message to the OAM to request a model for predicting the target cell. Alternatively, when the source RAN node predicts the target cell of the UE in an AI manner, the input of the model includes the trajectory information of the UE. However, if the trajectory of the UE is very random, the accuracy of the target cell predicted in an AI manner may be significantly reduced. In this case, the source RAN node can consider not using the AI method to predict the target cell of the UE. In this case, the source RAN node no longer sends a first model request message to the OAM and predicts the target cell of the UE in a conventional way.

[0094] Step 702: The OAM determines a first model based on the first model request message and the first mapping relationship.

[0095] In this application, to distinguish different models, different models are respectively called the first model, the second model, etc. A model represents the mapping relationship between the input and output of the model and is for implementing a specific function. The name of the model is not restricted. For example, the model may also be called an AI model. The AI model may be a neural network, a linear regression model, a decision tree model, an SVM, a Bayesian network, a Q-learning model, or other machine learning models. The first mapping relationship represents mapping, correspondence, etc. The first mapping relationship may also be called correspondence, etc. Similar cases will not be described one by one hereafter.

[0096] In this application, OAM can obtain the first mapping relationship. The first mapping relationship can be established by OAM or other nodes. OAM obtains the first mapping relationship through other nodes.

[0097] In an example, the first mapping relationship includes the mapping relationship between the model, the model performance level, and the model function, and the model performance level may sometimes be abbreviated as the performance level.

[0098] For example, there is a one-to-many correspondence between the model performance level and the model performance indicator. For example, the model performance indicator for model level classification includes accuracy, and the level can be classified based on the accuracy. The higher the accuracy, the higher the model performance level. Alternatively, the model performance indicator for model level classification includes the inference time, and the level can be classified based on the inference time. The shorter the inference time, the higher the model performance level. Alternatively, the model performance indicator for model level classification includes at least two indicators among accuracy, computational amount, inference time, and memory occupancy. For example, multiple indicators are weighted by a specific weighting method to determine a weighted value, and the level is classified based on the weighted value. For models with different functions, the method for classifying the model performance level can be the same or different. For example, the model performance level is classified based on the model accuracy of the model for trajectory prediction and the model for SINR prediction. Assume that both the model for trajectory prediction and the model for SINR prediction include three model performance levels. As shown in Table 1, for the model for trajectory prediction, the accuracy corresponding to model performance level 1 is [n1, n2], the accuracy corresponding to model performance level 2 is (n2, n3], and the accuracy corresponding to model performance level 3 is (n3, n4], where n4 is greater than or equal to n3, n3 is greater than or equal to n2, and n2 is greater than or equal to n1. As shown in Table 2, for the model for SINR prediction, the accuracy corresponding to model performance level 1 is [m1, m2], and the accuracy corresponding to model performance level 2 is (m2, m3], where m3 is greater than or equal to m2 and m2 is greater than or equal to m1. As another example, as shown in Table 3, for the model for SINR prediction, the accuracy corresponding to model performance level 1 is [t1, t2], the accuracy corresponding to model performance level 2 is (t2, t3], and the accuracy corresponding to model performance level 3 is (t3, t4], where t4 is greater than or equal to t3, t3 is greater than or equal to t2, and t2 is greater than or equal to t1.

Table 1

Table 2

Table 3

[0099] In the design, the first RAN node may obtain a model application scenario, determine a corresponding model performance indicator based on the model application scenario, and determine a model performance level based on the model performance indicator. The first model request message sent to the OAM indicates the model performance level and the model function. The OAM determines a model called the first model from the first mapping relationship based on the model performance level and the model function indicated by the first model request message. The first mapping relationship includes the mapping relationship between the model, the model performance level, and the model function. Reference can be made to the following description in Table 6.

[0100] In an example, the first mapping relationship includes the mapping relationship between the model, the model application scenario, and the model function. For example, the first mapping relationship includes an AI model, an autonomous driving scenario, and QoS prediction.

[0101] For example, the first RAN node may obtain the model application scenario and model function of the first model. Reference can be made to the description of step 701. The first model request message sent by the first RAN node to the OAM indicates the model application scenario and model function of the first model. The OAM may determine the AI model from the first mapping relationship based on the application scenario and model function indicated by the first model request message. The AI model is called the first model. For example, as shown in Table 4, the first mapping relationship includes four application scenarios (having indexes 1 to 4) and three model functions (having indexes 1 to 3). The four application scenarios and three model functions can form 12 mapping relationships, and the indexes of the 12 mapping relationships are 1 to 12. For example, mapping relationship 1 includes application scenario 1, model function 1, and model 1, and mapping relationship 2 includes application scenario 1, model function 2, and model 2.

Table 4

[0102] In the first mapping relationship shown in Table 4, for the same model function, the corresponding models are different in different scenarios. For example, model function 1 corresponds to model 1, model 4, model 7, and model 10 in application scenarios 1 to 4 respectively. The establishment of the first mapping relationship shown in Table 4 by the OAM is used as an example. For the same model function, the OAM may separately collect the corresponding training data in different scenarios and train the corresponding AI model. For example, in application scenario 1, the OAM may collect the training data corresponding to model function 1 and use the training data to train model 1, so that model 1 can satisfy the performance indicator of application scenario 1. The training process may be machine learning, non-machine learning, reinforcement learning, etc. This is not limited.

[0103] Instead, in this example, there is a one-to-one correspondence between the model performance level and the model performance indicator, where each model performance level corresponds to a unique model performance indicator, and each model performance indicator corresponds to a unique model performance level. For example, as shown in Table 5, model performance levels 1 to 3 correspond to model performance indicators 1 to 3 respectively. It is used as an example that the model performance indicator includes accuracy, computational complexity, inference time, and memory occupancy. Model performance level 1 corresponds to model performance indicator 1. Correspondingly, the accuracy is at least 99%, the computational complexity is up to 3% at most, the inference time is up to 1 second at most, and the memory occupancy is up to 500 KB at most.

Table 5

[0104] In this example, the first RAN node may obtain the model performance indicator of the required model, and the first model request message sent to the OAM indicates the model function and the model performance indicator of the required model. Upon receiving the first model request message, the OAM may determine the model performance level based on the model performance indicator indicated by the first model request message. Alternatively, the first RAN node can determine the model performance level based on the model performance indicator, and the first model request message sent to the OAM indicates the model function and the model performance level. The OAM determines the model from the first mapping relationship based on the model function and the model performance level, and the model is called the first model, etc. For example, as shown in Table 6, the first mapping relationship includes 12 mapping relationships with indexes from 1 to 12. For example, mapping relationship 1 includes model 1, model performance level 1, and model function 1, and mapping relationship 2 includes model 2, model performance level 1, and model function 2.

Table 6

[0105] Note that in the above example where there is a one-to-one correspondence between the model performance level and the model performance indicator, it should be noted that the model performance level in the above first mapping relationship may be replaced by the model performance indicator. Alternatively, in the present application, the first mapping relationship may be established based on the model performance indicator. For example, the first mapping relationship includes the mapping relationship among the model, the model performance indicator, and the model function. In this design, the first model request message includes the model function and the model performance indicator of the first model. OAM determines the model from the first mapping relationship based on the model function and the model performance indicator indicated by the first model request message. The model may be referred to as the first model. For example, as shown in Table 7, the first mapping relationship includes 12 mapping relationships having indexes from 1 to 12. For example, mapping relationship 1 includes model 1, model performance indicator 1, and model function 1, and mapping relationship 2 includes model 2, model performance indicator 1, and model function 2.

Table 7

[0106] In the first mapping relationship shown in Table 6 or Table 7, the models corresponding to the same model function are different under different model performance levels and model performance indicators. For example, Table 6 or Table 7 can be referred to. In the case of model function 1, the models corresponding to model performance indicators 1 to 4 or model performance indicators 1 to 4 are model 1, model 4, model 7, and model 10, respectively. The establishment of the first mapping relationship shown in Table 6 or Table 7 by OAM is used as an example. OAM may separately collect the corresponding training data under different model performance levels or model performance indicators and train the corresponding models. For example, OAM may collect the training data corresponding to model function 1 under model performance level 1 or model performance indicator 1 and train model 1.

[0107] As described above, the model performance indicator includes at least one of the following: accuracy, computational complexity, memory occupancy, inference time, etc. In different scenarios, the same model function may have different requirements for the model performance indicator. Optionally, the computational complexity in the model performance indicator is also called computational resource occupancy, and the memory occupancy is also called the model size. In the possible case, as shown in Table 8, in the VR / AR scenario and the V2X scenario, the requirements for the model performance indicator in QoS prediction are different. In another possible case, as shown in Table 9, in scenarios such as vehicles on fixed routes, vehicles on non-fixed routes, and pedestrian scenarios, the requirements for the model performance indicator in trajectory prediction are different.

Table 8

Table 9

[0108] In different scenarios, the requirements for performance indicators of models with the same function may vary. If the same model is used for inference for a certain function in any scenario, it is difficult to meet the requirements for the performance indicators of the corresponding scenario. For example, in Table 8, in the case of AR / VR scenarios and V2X scenarios, if the same model in QoS prediction is used for model inference, it is difficult to meet the requirements for the performance indicators of each scenario. In the solution of the present application, the first mapping relationship shown in Table 4 above can be referred to. For the same model function, different models can be composed in different scenarios. For example, in the VR / AR scenario, the model is composed for QoS prediction. In the V2X scenario, other models are composed for QoS prediction. Alternatively, the mapping relationship shown in Table 6 above can be referred to. A model with the corresponding performance level can be composed for the requirements for performance indicators in different scenarios. Alternatively, the mapping relationship shown in Table 7 above can be referred to, and different models can be composed for each different performance indicator, so that each model meets the requirements for the performance indicators of the corresponding application scenario.

[0109] Step 703: OAM sends information about the first model to the first RAN node.

[0110] In the present application, the information about the first model indicates at least one of the following of the first model: model index, model structure information, model parameters, model input format, model output format, input data processing method, output data processing method, model performance indicator, model application scenario, model function, training parameters, etc.

[0111] The model index is for distinguishing different models. Model structure information, model parameters, model input format, model output format, etc. are used by the first RAN node to determine the first model. In other words, the first RAN node determines the first model based on model structure information, model parameters, model input format, model output format, etc. The model structure information includes at least one of the following: the number of layers included in the model (also called the depth of the model), the network type of each layer (e.g., fully connected layer network, convolutional layer network, long short-term memory (LSTM) layer), the number of neurons included in each layer (also called the width of the model), the connection relationship between layers, etc. The model parameters may include at least one of the following: the weights of neurons, the activation functions of neurons, the offsets within the activation functions, etc. The model input format and model output format may be the formats that need to be satisfied by the input data and output data when training or inferring the model. The above input data processing method includes the method of preprocessing the raw data before the collected raw data is input into the model. For example, the imaginary part is separated from the real part of the raw data, normalization processing is performed on the raw data, or the phase and amplitude of the raw data are separated. The above output data processing method includes the method of processing the output data when the model outputs data, for example, intercepting the output data. For the model performance indicator, model application scenario, and model function, please refer to the above description. In this application, when determining the first model based on information such as model structure, model parameters, model input format, and model output format, the first RAN node may further continue to train the first model based on the above training parameters. The training parameters may include the instruction information of the loss function, etc. For example, the first RAN node may obtain training data. The training data may be from the first RAN node, or from the UE, or from both the first RAN node and the UE, or from other nodes.The first RAN node trains a first model based on training data. For example, the training data is input into the first model. The value of the loss function is determined based on the output of the first model. When the value of the loss function is less than the threshold or meets the target requirement, the training of the first model ends. When the value of the loss function is greater than or equal to the threshold or does not meet the target requirement, the parameters of the first model are updated, and the training of the first model continues. The updating of the parameters of the first model includes updating at least one of the following information of the first model: the number of layers of the neural network, the width of the neural network, the connection relationship between layers, the weights of neurons, the activation function of neurons, the offset within the activation function, etc. The training process may be label learning, unlabeled learning, reinforcement learning, etc. This is not limited. When the first model is trained, the initial model of the first model can be instructed by the information about the first model, or can be randomly generated, or can be agreed upon by protocol. This is not limited.

[0112] Alternatively, the information about the first model indicates at least one of the following of the first model: the model, the model usage description, or the model index. The model includes a network architecture, parameter weights, training hyperparameters, an optimizer, a batch size, a learning rate, momentum, etc. Optionally, the first RAN node may perform online learning, etc. based on the training hyperparameters. The model usage description includes at least one of a model input / output format, a model performance indicator, a model application scenario, and a model function. The model index is a number of the model used to distinguish different models, etc.

[0113] During actual application, the requirements for the performance indicators of models implementing the same function vary in different application scenarios. For example, as shown in Table 8 or Table 9 above, in the cases of QoS prediction and trajectory prediction, the requirements for the performance indicators of the model are different in different scenarios. In the present application, the OAM establishes a first mapping relationship, and the first mapping relationship is a mapping relationship among the model, the model application scenario, and the model function. Based on the mapping relationship, for the same model function, in different application scenarios, models corresponding to the scenarios can be configured, so that the model satisfies the requirements for the performance indicators in the scenario corresponding to the model. Compared with the case where the same model is used for model inference in any scenario, the solution of the present application can improve prediction performance, prediction accuracy, etc. Alternatively, in the present application, the OAM establishes a first mapping relationship, and the first mapping relationship is a mapping relationship among the model, the model performance level, and the model function, or the first mapping relationship is a mapping relationship among the model, the model performance indicator, and the model function. Since the requirements for the performance indicators of the model vary in different application scenarios and there is a correspondence between the model performance indicator and the model performance level, the OAM can configure different models according to different requirements for the model performance indicators in different application scenarios based on the first mapping relationship. Compared with the case where the same model is used for model inference under the model performance indicator in any scenario, the solution of the present application can improve prediction performance, prediction speed, etc.

[0114] Optionally, in addition to steps 701 to 703, the procedure shown in FIG. 7 may further include the following steps.

[0115] Step 704: The first RAN node performs model inference based on the first model to obtain prediction information.

[0116] For example, the first RAN node can collect input data and allocate computing resources, memory resources, etc. for model inference. The input data can be from the first RAN node and / or the UE, or from other nodes, etc. This is not limited. Optionally, the first RAN node can collect input data based on the model usage description. The input data is input into the first model and output as prediction information. Alternatively, the input data is processed, the processed input data is input into the first model, and / or the output data of the first model is processed, and the processed output data is the prediction information. It can be understood that the input data and the output data satisfy the requirements for the input format and output format of the first model. The prediction information includes prediction results. For example, when a model training node such as OAM trains a model, the model training node executes model training by using a training set to obtain the model, and this process can be called model training. The model is verified by using a validation set, and this process can be called model validation. Model validation is usually performed during model training. For example, every time the model is trained for one or more epochs, the validation set can be used to verify the current model to monitor the model training status, for example, to verify whether the current model is underfitting, overfitting, or converging, and to determine whether to end the training. Optionally, in the model validation process, the hyperparameters of the model may be further adjusted. Hyperparameters can refer to at least one of the following parameters of the model: the number of layers of the neural network, the number of neurons, the activation function, the loss function, etc. The model is tested by using a test set, and this process can be called model testing. For example, it is determined whether the generalization ability of the model is evaluated, whether the model meets the requirements, whether the modem is available, or the prediction accuracy of the model is obtained. In the present application, the information about the first model transmitted to the first RAN node by OAM may further include prediction accuracy, etc.Alternatively, when determining the first model, the first RAN node may test the first model by using the test data collected locally to obtain, for example, the prediction accuracy. In addition to the prediction result inferred by the first model, optionally, the prediction information transmitted by the first RAN node to the UE may further include the prediction accuracy. For example, in trajectory prediction, the prediction information transmitted by the first RAN node to the UE includes the predicted trajectory information of the UE and the accuracy of the predicted trajectory information (e.g., 98%).

[0117] In the present application, when the prediction information is the prediction information related to the first RAN node, the first RAN node determines the first feedback information based on the prediction information. For example, the first RAN Nodeperforms corresponding operations based on the prediction information. For example, in a network energy saving scenario, the prediction information is an energy saving policy, such as carrier shutdown, symbol shutdown, cell shutdown, or deep sleep. After the first RAN node infers the energy saving policy by using the first model, the first RAN node may execute the energy saving policy. The first RAN node determines the first feedback information based on the performance changes (e.g., changes in UE cell throughput or QoS) before and after the prediction information is executed. Alternatively, the prediction information is prediction information related to the UE, and the first RAN node needs to send the prediction information to the UE. The UE determines the second feedback information based on the prediction information. For example, the UE may determine the second feedback information based on the performance changes after the prediction information is executed. For example, in a mobility enhancement scenario, the prediction information is the target cell to which the UE should be handed over, the handover time, etc. The UE performs corresponding operations based on the prediction information and may determine the second feedback information based on the performance changes after the prediction information is executed. Alternatively, the UE compares the prediction information with the actual information to determine the second feedback information. For example, in a trajectory prediction scenario, the UE may compare the predicted movement trajectory of the UE with the actual movement trajectory of the UE to determine the second feedback information. The UE sends the second feedback information to the first RAN node. The first RAN node sends the first feedback information to the OAM based on the received second feedback information, and the OAM updates the first model based on the first feedback information, etc. For details, please refer to the description of the first design in the subsequent steps 705 and 706. Alternatively, when it is determined that the application scenario changes or the required performance level (or performance indicator) of the model changes, the first RAN node may alternatively send the first feedback information indicating that the scenario changes or the performance level changes to the OAM. The OAM may reconfigure the model for the first RAN node based on the scenario change instruction or the performance level change instruction. The model may be called the second model, etc.For example, when the UE changes from a walking scenario to a scenario where a vehicle travels on a fixed route, the first RAN node may predict the movement trajectory in the scenario where the vehicle travels on the fixed route by using the model in the trajectory prediction required in the walking scenario, and as a result, there may be a decrease in prediction accuracy. In the present application, when it is discovered that the scenario of the UE has changed, the first RAN node can indicate the scenario change to the OAM or the like, and the OAM can configure a model that conforms to the current scenario or the like for the first RAN node. The above scenario change instruction or performance level change instruction can be discovered by the first RAN node by analyzing the UE, or can be reported to the first RAN node by the UE. For details, refer to the description of the second design in step 706. Alternatively, the first model is a model related to the first RAN node, the first RAN. Node is the first RAN Node By analyzing, it can be determined that the application scenario has changed, the performance level has changed, etc.

[0118] Step 705: The first RAN node transmits prediction information to the UE. Correspondingly, the UE receives the prediction information from the first RAN node.

[0119] Step 706: The UE transmits the second feedback information to the first RAN node. Correspondingly, the first RAN node receives the second feedback information from the UE.

[0120] In the first design, upon receiving prediction information, the UE may compare the prediction information transmitted by the first RAN node with the actual information to determine the accuracy of the prediction information. The accuracy is referred to as the actual accuracy of the prediction information. For example, the prediction information is trajectory information. The UE may compare the predicted trajectory information with the actual trajectory information of the UE to determine the actual accuracy of the prediction information, and transmit second feedback information to the first RAN node. At this time, the second feedback information indicates the actual accuracy of the prediction information. The indication may be an implicit indication, an explicit indication, etc. In other words, the UE transmits the actual accuracy of the prediction information to the first RAN node. For example, the UE may feedback to the first RAN node the actual accuracy of the prediction information within a certain period. At this time, the feedback may be periodic feedback, feedback based on an event trigger, etc. For example, the UE may use 10 seconds as a period, calculate the actual accuracy of the prediction information every 10 seconds, and feedback the actual accuracy to the first RAN node. Alternatively, the UE may also calculate the accuracy of the prediction information every 10 seconds. When the accuracy is less than a threshold (e.g., 80%), the UE transmits the actual accuracy of the prediction information to the first RAN node.

[0121] In the second design, when finding that the application scenario of the UE changes or the performance level of the UE changes, the UE may alternatively transmit second feedback information to the first RAN node. The second feedback information indicates that the application scenario has changed or the performance level has changed. In other words, the UE transmits an application scenario change indication, a performance level change indication, etc. to the first RAN node.

[0122] Step 707: The first RAN node transmits the first feedback information to the OAM. Correspondingly, the OAM receives the first feedback information from the first RAN node.

[0123] In the design, the first RAN node may send the first feedback information to the OAM periodically or based on an event trigger or the like. The first feedback information may implicitly or explicitly indicate the actual accuracy of the prediction information. In other words, the first RAN node sends the actual accuracy of the prediction information to the OAM. Similar to step 706 above, the first RAN node may feedback the actual accuracy of the prediction information within a certain period to the OAM. For example, the first RAN node may feedback the actual accuracy of the prediction information within a certain period to the OAM periodically or based on an event trigger or the like.

[0124] In other designs, when the application scenario or performance level related to the first model changes, the first RAN node sends the first feedback information to the OAM. The first feedback information indicates that the application scenario has changed or the performance level has changed. In other words, the first RAN node sends an application scenario change instruction, a performance level change instruction, etc. to the OAM.

[0125] Step 708: The OAM updates the first model based on the first feedback information.

[0126] In the design, when the first feedback information indicates the actual accuracy of the prediction information output by the first model, the OAM updates the first model based on the first feedback information: when the actual accuracy of the prediction information output by the first model is less than the threshold, the first model is retrained and the first model is updated. For example, the OAM can collect training data, and the training data is from the first RAN node and / or the UE, other nodes, etc. The OAM may retrain the first model by using the collected training data. For the model training process, please refer to the above description.

[0127] In other designs, the first feedback information indicates a scenario change instruction, a model performance level change instruction, or a model performance indicator change instruction. For OAM to update the first model based on the first feedback information includes: the second model is determined based on a scenario change instruction, a model performance level change instruction, or a model performance indicator change instruction. For example, the first mapping relationship includes the mapping relationship between the model, the model application scenario, and the model function, and OAM can determine the changed scenario based on the indicated scenario change instruction. The model corresponding to the changed scenario is determined based on the above first mapping relationship and is called the second model. Alternatively, the first mapping relationship includes the mapping relationship between the model, the model performance level, and the model function, and OAM can determine the changed model performance level based on the indicated model performance level change instruction. Alternatively, OAM can determine the changed model performance level based on the indicated model performance indicator change instruction. The model corresponding to the changed model performance level is determined based on the above mapping relationship and is called the second model. Alternatively, the first mapping relationship includes the mapping relationship between the model, the model performance indicator, and the model function. OAM determines the model corresponding to the indicated changed model performance indicator based on the first mapping relationship, and the model is called the second model.

[0128] Step 709: OAM sends the information about the updated model to the first RAN node. Correspondingly, the first RAN node receives the information about the updated model from OAM.

[0129] Regarding the content included in the information about the updated model, please refer to the content included in the information about the first model in step 703 above. Details are not described here again.

[0130] In the present application, the first RAN node may periodically transmit the first feedback information to the OAM based on an event trigger or the like. The OAM evaluates whether the first model needs to be updated based on the first feedback information, updates the first model as appropriate, and improves the prediction performance and prediction speed of the first model.

[0131] After the first model is configured for the first RAN node by using the method of the procedure in FIG. 7, as shown in FIG. 8, the present application provides a cell handover flowchart. In this procedure, the first RAN node is called the source RAN node, the second RAN node is called the target RAN node, and the procedure includes at least the following steps.

[0132] Step 801: The source RAN node determines the target cell.

[0133] In the design, the source RAN node may receive the measurement report reported by the UE, and the measurement report includes the identification information of the cell, the quality information of the cell, etc. The cell may be the current serving cell, an adjacent cell, etc. This is not limited. In the design, the UE may periodically report the measurement report to the source RAN node. For example, the UE may periodically measure the signal quality of the serving cell and / or the signal quality of the adjacent cell, and then periodically report the measured information to the source RAN node. Alternatively, the UE may periodically measure the signal quality of the serving cell and / or the signal quality of the adjacent cell, and report the measured information, etc. to the source RAN node when specific conditions are met.

[0134] In this application, the measurement report includes the identification information of the cell. The identification information of the cell may include at least one of a cell global identifier (CGI) of the cell, a physical cell identifier (PCI) and frequency, a cell identity (cell ID), a non-public network identifier (NPN ID), a non-terrestrial network identifier (NTN ID), and other cell identifications. The CGI may include a public land mobile network (PLMN ID), a cell ID, and the like. Optionally, the identification information of the cell may further include a tracking area code (TAC) and / or the identification information of the network device to which the cell belongs, for example, a global network device identifier.

[0135] In this application, the measurement report includes the signal quality information of the cell. For example, the UE may obtain the quality information of the cell by measuring at least one of the downlink synchronization channel, channel state information reference signal, demodulation reference signal (DMRS), cell-specific reference signal (CRS), synchronization signal block (SSB), synchronization signal / physical broadcast channel block, or other downlink signals. For example, the quality information of the cell may include at least one of received signal code power (RSCP), reference signal received power (RSRP), reference signal received quality (RSRQ), signal noise ratio (SNR), signal to interference plus noise ratio (SINR), reference signal strength indication (RSSI), or other signal quality.

[0136] Optionally, the cell quality information may be at least one of a cell level, a beam level, a synchronization signal / physical broadcast channel block (SS / PBCH block) level, a channel state information reference signal (CSI-RS) level, a numerology level, a slicing level, or a bandwidth part (BWP) level. The level of the cell quality information is the granularity at which the cell quality information is measured. For example, the cell quality information being at the cell level means that the UE measures each cell among a plurality of cells to be measured in order to obtain the quality of each cell. Alternatively, the cell quality information being at the beam level means that the cell includes at least one beam, and the UE obtains the cell quality information by measuring the beams in the cell. For example, the cell includes three cells, and the UE may separately measure beam 1, beam 2, and beam 3 among the three beams to obtain the quality information of beam 1 and the quality information of beam 2 that satisfy the beam quality condition. Optionally, the UE may then obtain the cell quality information based on the quality information of beam 1 and the quality information of beam 2, for example, by obtaining a larger value, an average, or a weighted sum. Alternatively, the UE may report both the quality information of beam 1 and the quality information of beam 2. The cell quality measured by the UE at other granularities is the same as above and will not be described again.

[0137] Note that when the cell quality information is at the beam level, the SS / PBCH block level, the CSI-RS level, the air interface technology level, the slicing level, the BWP level, etc., it should be noted that the identification information of the cell further includes at least one of the identification information of the corresponding beam, the identification information of the SS / PBCH block, the identification information of the CSI-RS, the identification information of the air interface technology, the identification information of the slice, the identification information of the BWP, etc.

[0138] In the design, when receiving a measurement report, the source RAN node may determine whether the serving cell needs to be handed over for the UE based on the measurement report. For example, if the quality of service of the source cell is lower than a specific threshold, it may be determined that the serving cell needs to be handed over for the UE. The source RAN node may select a target cell for the UE based on the received measurement report. For example, a cell with a signal quality higher than the threshold and the best signal quality may be selected as the target cell from the adjacent cells of the UE.

[0139] In other designs, the source RAN node may determine the target cell in an AI manner. The source RAN node may predict the target cell by using an AI model or a machine learning (ML) model. The input data of the AI model or ML model includes at least one of the following: the measurement report of the UE, the past and / or real-time load of the adjacent cells, the past trajectory information of the UE, the geographical coordinates of the UE at the current time, the moving direction of the UE at the current time, the moving speed of the UE, etc. The output data of the AI model or ML model includes at least one of the following: the target cell of the UE, the handover time stamp, etc.

[0140] Step 802: The source RAN node transmits the model information to the target RAN node corresponding to the target cell.

[0141] In the design, the source RAN node may send model information to the target RAN node, and the model information includes the index of the first model. Optionally, the model information may further include at least one of the following: the application scenario of the first model, the function of the first model, the data that needs to be collected by the first model for model inference, etc. For example, in an AI-based mobility enhancement solution, the RAN node that performs model inference needs to request the target RAN node that the UE has previously accessed for the cases of success or failure of the UE's past handovers. In the present application, the process in which the source RAN node needs to notify the target RAN node of the data that needs to be collected for model inference may be further described as follows: The RAN node sends first indication information to the target RAN node, and the first indication information indicates the type information of the input data of the first model. In this process, the source RAN node can notify the target RAN node in advance of the data that needs to be collected for model inference in the model inference process, so that the target RAN node can prepare for model inference to improve the model inference efficiency.

[0142] Step 803: The source RAN node sends the UE's history information to the target RAN node, and the history information includes at least past data, past information, etc. collected from the UE by the source RAN node.

[0143] In the design, the UE's historical information may be the information necessary to perform model inference by using a first model or the like. In other words, when the target RAN node performs model inference by using the first model, the historical information may be used as part of the input data of the first model. The above process may be further described as follows: The source RAN node sends past data information necessary for model inference to the target RAN node, and the past data information is for determining the input data of the first model. For example, the target RAN node may directly use past data as the input of the first model to perform model inference, or the target RAN node may process the past data and use the processed past data as the input of the first model. This is not limited.

[0144] It should be noted that step 803 may be executed after step 802, or may be executed after step 804. This is not limited. In the procedure of FIG. 8, an example where the procedure is executed after step 802 is used for explanation. For example, when the target cell is determined by an AI method, step 803 may be executed after step 802. Alternatively, when the target cell is determined by a non-AI method, step 803 may be executed after step 804. In the present application, the order of steps in all procedures is not limited.

[0145] Step 804: The UE is handed over from the source cell of the source RAN node to the target cell corresponding to the target RAN node. After the handover, the target cell becomes the serving cell of the UE.

[0146] Step 805: The target RAN node sends second indication information to the source RAN node, and the second indication information indicates at least one of the following, that is, indicates that the UE is handed over to the target cell, instructs the source RAN node to delete the first model, or instructs the source RAN node to release the corresponding computing resources.

[0147] For example, the source RAN node allocates 1% of the resources to the UE for model inference. After the UE is handed over to the target cell, the source RAN node may release the 1% of computing resources allocated to the UE and delete the first model, etc.

[0148] Step 806: The source RAN node deletes the first model and releases the corresponding computing resources.

[0149] Step 807: The target RAN node sends a second model request message to the OAM, and the second model request message indicates the index of the first model.

[0150] Step 808: The OAM determines the first model based on the index of the first model.

[0151] In the present application, there is a mapping relationship between the index of the first model and the first model. The OAM can determine the first model based on the index of the first model.

[0152] Step 809: The OAM sends information about the first model to the target RAN node.

[0153] Step 8010: The target RAN node performs model inference based on the first model and obtains prediction information.

[0154] Step 8011: The target RAN node sends the prediction information to the UE.

[0155] Step 8012: The UE sends the second feedback information to the target RAN node.

[0156] Step 8013: The target RAN node sends the first feedback information to the OAM.

[0157] Step 8014: The OAM updates the first model.

[0158] Step 8015: OAM sends information about the updated first model to the target RAN node.

[0159] For the specific execution process of Steps 809 to 8015, please refer to the description in FIG. 7.

[0160] In the present application, when the UE performs cell handover, the source RAN node sends the index of the first model to the target RAN node to ensure the continuity of the AI service. Further, after the UE successfully completes the handover, the target RAN node notifies the source RAN node to delete the model-related information to release the computing resources, so that the memory resource occupation and computing resource occupation of the RAN node can be reduced.

[0161] To implement the functions in the above method, it can be understood that the OAM and the RAN node include corresponding hardware structures and / or software modules for executing the functions. A person skilled in the art should easily notice that the present application can be implemented by hardware or a combination of hardware and computer software with reference to the units and method steps in the examples described in the present application. Whether the function is executed by hardware driven by hardware or computer software depends on the specific application scenario and design constraints of the technical solution.

[0162] FIG. 9 and FIG. 10 are diagrams of the structures of possible apparatuses according to the present application, respectively. These communication apparatuses may be configured to implement the functions of the OAM or the RAN node in the above method, and thus can also achieve the advantageous effects of the above method.

[0163] As shown in FIG. 9, the communication device 900 includes a processing unit 910 and a transceiver unit 920. The communication device 900 is configured to implement the functions of the OAM, the source RAN node, or the target RAN node in the method shown in FIG. 7 or FIG. 8.

[0164] When the communication device 900 is configured to implement the function of the OAM in the method shown in FIG. 7 or FIG. 8, the transceiver unit 920 is configured to receive a first model request message from a first access network device. The processing unit 910 is configured to determine a first model based on the first model request message and a first mapping relationship, where the first mapping relationship includes the mapping relationship between the model, the model application scenario, and the model function, or the first mapping relationship includes the mapping relationship between the model, the model performance level, and the model function. The transceiver unit 920 is further configured to transmit information about the first model to the first access network device.

[0165] When the communication device 900 is configured to implement the function of the first RAN node or the source RAN node in the method shown in FIG. 7 or FIG. 8, the transceiver unit 920 is configured to transmit a first model request message to the operation, administration, and maintenance OAM, where the first model request message indicates the model application scenario and the model function of the first model, or the first model request message indicates the model function and the model performance level of the first model. The transceiver unit 920 is further configured to receive information about the first model from the OAM.

[0166] When the communication device 900 is configured to implement the function of the target RAN node in the method shown in FIG. 8, the transceiver unit 920 is configured to receive an index of a first model from a first access network device and transmit a second model request message to the operation, administration, and maintenance OAM. The second model request message indicates the index of the first model,The transceiver unit 920 is Receive information about the first model from OAM further configured as .

[0167] For a more detailed description of the processing unit 910 and the transceiver unit 920, please directly refer to the relevant description of the method shown in FIG. 5, FIG. 7, or FIG. 8. Details will not be described again here.

[0168] As shown in FIG. 10, the communication device 1000 includes a processor 1010 and an interface circuit 1020. The processor 1010 and the interface circuit 1020 are coupled to each other. It can be understood that the interface circuit 1020 may be a transceiver, an input / output interface, a pin, etc. Optionally, the communication device 1000 may further include a memory 1030 configured to store instructions executed by the processor 1010, or input data required for the execution of instructions by the processor 1010, or data generated after the processor 1010 executes the instructions.

[0169] When the communication device 1000 is configured to implement the above method, the processor 1010 is configured to implement the functions of the processing unit 910, and the interface circuit 1020 is configured to implement the functions of the transceiver unit 920.

[0170] When the communication device is a module used in OAM, the module in OAM implements the functions of OAM in the above method. The module in OAM receives information from other modules in OAM (for example, a radio frequency module or an antenna), where the information is sent to OAM by the RAN node, or the module in OAM sends the information to other modules in OAM (for example, a radio frequency module or an antenna), where the information is sent to the RAN node by OAM. The module in OAM here may be the baseband chip of OAM, or other modules, etc.

[0171] When the communication device is a module used in a RAN node, the module in the RAN node implements the functions of the RAN node in the above method. The module in the RAN node receives information from other modules in the RAN node (for example, a radio frequency module or an antenna), and at this time, the information is sent by the terminal to the RAN node, or the module in the RAN node sends the information to other modules in the RAN node (for example, a radio frequency module or an antenna), and at this time, the information is sent by the RAN node to the terminal. The module in the RAN node here may be a baseband chip of the RAN node, or may be a DU or other modules. The DU here may be a DU in an open radio access network (O-RAN) architecture.

[0172] The processor of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSPs), or application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor, or may be any ordinary processor.

[0173] The memory of the present application may be a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, a register, a hard disk drive, a removable hard disk drive, a CD-ROM, or any other form of storage medium well known in the art.

[0174] For example, the storage medium may be coupled to the processor, whereby the processor can read information from or write information to the storage medium. Alternatively, the storage medium may be a component of the processor. The processor and the storage medium may be disposed in an ASIC. Further, the ASIC may be located in a base station or a terminal. Indeed, the processor and the storage medium may exist as separate components in a base station or a terminal.

[0175] Some or all of the methods of the present application may be implemented by software, hardware, firmware, or any combination thereof. When software is used to implement the method, some or all of the method may be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, all or part of the procedures or functions according to the present application are executed. The computer may be a general-purpose computer, a dedicated computer, a computer network, a network device, a user device, a core network device, an OAM, or other programmable device. The computer program or instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions may be transmitted in a wired or wireless manner from one website, computer, server, or data center to another website, computer, server, or data center. The computer-readable storage medium may be any useful medium accessible by the computer, or a data storage device, such as a server or data center incorporating one or more useful media. The useful medium may be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape, or an optical medium, such as a digital video disk, or a semiconductor medium, such as a solid state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include two types of storage media, namely, a volatile storage medium and a non-volatile storage medium.

[0176] In the present application, unless stated otherwise or there is no logical contradiction, the terms and / or descriptions in different embodiments are consistent and may be cross-referenced, and the technical features in different embodiments may be combined based on their internal logical relationships to form new embodiments.

[0177] In this application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship between related objects and indicates that there may be three relationships. For example, A and / or B can represent the following cases: A exists alone, both A and B exist, and B exists alone, and A and B may be singular or plural. In the text description of this application, the character " / " usually indicates the "or" relationship between related objects. In the formulas in this application, the character " / " indicates the "division" relationship between related objects. "Including at least one of A, B, or C" can mean including A, including B, including C, including A and B, including A and C, including B and C, and including A, B, and C.

[0178] It can be understood that the various numbers used in this application are only distinguished for the convenience of description and are not intended to limit the scope of this application. The sequential numbers of the above processes do not mean the execution order, and the execution order of the processes should be determined based on the functions and internal logics of the processes.

[0179] [Cross-reference to related applications] This application claims the priority of Chinese Patent Application No. 202210096175.1, titled "MODEL CONFIGURATION METHOD AND APPARATUS", filed with the China National Intellectual Property Administration on January 26, 2022, and the entire text of the previous Chinese patent application is incorporated herein by reference.

Claims

1. A model configuration method, comprising: Receiving a first model request message from a first access network device; Determining a first model based on the first model request message and a first mapping relationship, where the first mapping relationship includes a mapping relationship between a model, a model application scenario, and a model function, or the first mapping relationship includes a mapping relationship between a model, a model performance level, and a model function; Sending information about the first model to the first access network device; Receiving a second model request message indicating an index of the first model from a second access network device; Determining the first model based on the index of the first model; Sending the information about the first model to the second access network device. A method having the above steps.

2. The first model request message indicates a model application scenario and a model function of the first model. The method according to claim 1.

3. The first model request message indicates a model function and a model performance level of the first model, or indicates a model function and a model performance indicator of the first model. The method according to claim 1.

4. The information about the first model includes at least one of the following of the first model: Model index, model structure information, model parameters, model input format, model output format, model performance indicator, model application scenario, model function, or training parameters The method according to claim 1.

5. The method according to claim 1, further comprising: Receiving first feedback information from the first access network device; Updating the first model based on the first feedback information.

6. The first feedback information indicates an actual accuracy of prediction information output by the first model, and updating the first model based on the first feedback information includes: When the actual accuracy of the prediction information output by the first model is less than a threshold, retraining the first model and updating the first model. The method according to claim 5.

7. ​ The first feedback information indicates a scenario change instruction, a model performance level change instruction, or a model performance indicator change instruction, and updating the first model based on the first feedback information includes: selecting a second model for a terminal device based on the scenario change instruction, the model performance level change instruction, or the model performance indicator change instruction; sending information about the second model to the first access network device; wherein the second model is for updating the first model. The method according to claim 5.

8. A model configuration method, comprising: sending a first model request message to operation, administration, and maintenance (OAM), wherein the first model request message indicates a model application scenario and model functions of a first model, or the first model request message indicates model functions and a model performance level of the first model, or the first model request message indicates model functions and a model performance indicator of the first model; receiving information about the first model from the OAM; determining a target cell of a terminal device that is a transmission destination of prediction information output by the first model; sending an index of the first model to a second access network device corresponding to the target cell; A method comprising the above steps.

9. Before sending the first model request message to the OAM, the method further comprises: determining whether an artificial intelligence (AI)-based enhanced service needs to be provided. The method according to claim 8.

10. The information about the first model includes at least one of the following of the first model: model index, model structure information, model parameters, model input format, model output format, model performance indicator, model application scenario, model functions, or training parameters The method according to claim 8.

11. The method further comprises performing model inference based on the first model to obtain the prediction information. The method according to claim 8.

12. The method further comprises sending the prediction information including the prediction result to the terminal device. The method according to claim 11.

13. The prediction information further includes the accuracy of predicting the prediction result. The method according to claim 12.

14. ​ Further comprising receiving, from the terminal device, second feedback information indicating the actual accuracy of the prediction information The method according to claim 12 **Claim 15** Further comprising sending, to the OAM, first feedback information indicating the actual accuracy of the prediction information, an application scenario change instruction, a model performance level change instruction, or a model performance indicator change instruction The method according to claim 14 **Claim 16** Further comprising sending first instruction information to the second access network device corresponding to the target cell The first instruction information indicates type information of input data of the first model The method according to claim 8 **Claim 17** Further comprising sending, to the second access network device, historical data information required for model inference executed based on the first model to obtain the prediction information The method according to claim 8 **Claim 18** Receiving second instruction information from the second access network device, the second instruction information indicating one of the following: indicating that the terminal device is handed over to the target cell, instructing the first access network device to delete the first model, or instructing the first access network device to release corresponding computing resources Deleting the first model to release the corresponding computing resources The method according to claim 8, further comprising **Claim 19** A model configuration method, comprising Receiving an index of a first model from a first access network device Sending a second model request message indicating the index of the first model to an operation, administration, and maintenance (OAM) Receiving information about the first model from the OAM A method having **Claim 20** The information about the first model is one of the following of the first model Model index, model structure information, model parameters, model input format, model output format, model performance indicator, model application scenario, model function, or training parameters Indicating at least one of The method according to claim 19 **Claim 21** Further comprising receiving, from the first access network device, first instruction information indicating type information of input data of the first model The method according to claim 19 **Claim 22** further comprising receiving, from the first access network device, historical data information necessary for model inference, in the model inference, the historical data information is for determining the input data of the first model, The method according to claim 21.

23. further comprising sending second instruction information to the first access network device, the second instruction information indicates one of the following: indicating that the terminal device is handed over to the target cell, instructing the first access network device to delete the first model, or instructing the first access network device to release the corresponding computing resources, The method according to claim 19.

24. A communication device comprising a unit configured to implement the method according to any one of claims 1 to 7.

25. comprising a processor and a memory, the processor is coupled to the memory, the processor is configured to implement the method according to any one of claims 1 to 7, A communication device.

26. A communication device comprising a unit configured to implement the method according to any one of claims 8 to 18.

27. comprising a processor and a memory, the processor is coupled to the memory, the processor is configured to implement the method according to any one of claims 8 to 18, A communication device.

28. A communication device comprising a unit configured to implement the method according to any one of claims 19 to 23.

29. comprising a processor and a memory, the processor is coupled to the memory, the processor is configured to implement the method according to any one of claims 19 to 23, A communication device.

30. storing instructions, when the instructions are executed by a computer, the computer can execute the method according to any one of claims 1 to 7, A computer-readable storage medium.

31. having instructions, when the instructions are executed by a computer, the computer can execute the method according to any one of claims 1 to 7, A computer program.

32. storing instructions, when the instructions are executed by a computer, the computer can execute the method according to any one of claims 8 to 18, Computer-readable storage medium.

33. Having instructions, When the instructions are executed by a computer, the computer can execute the method according to any one of claims 8 to 18. Computer program.

34. Storing instructions, When the instructions are executed by a computer, the computer can execute the method according to any one of claims 19 to 23. Computer-readable storage medium.

35. Having instructions, When the instructions are executed by a computer, the computer can execute the method according to any one of claims 19 to 23. Computer program.

Images