Communication method and device

Abstract

The invention discloses a communication method and device, relates to the technical field of communication, and is used for reducing waste of transmission resources. On one hand, after a terminal device side sends a transmission demand of the terminal device side for an AI model to an access network device side, the access network device side determines which transmission mode is adopted to transmit the first AI model to the terminal device for the terminal device according to the demand. On the other hand, after the access network equipment side sends the own transmission demand to the terminal equipment side, the terminal equipment side reports the own capability parameter for receiving the AI model to the access network equipment side; therefore, the access network equipment side can determine a better transmission mode of the AI model capable of being matched with the capability of the terminal equipment for the terminal equipment according to the capability of the current terminal equipment and own requirements. The transmission mode determined by the scheme provided by the invention can adapt to the requirements of the terminal equipment (or the access network equipment) in different mobile scenes, and the waste of transmission resources is reduced.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a communication method and device. Background Art

[0002] Artificial intelligence (AI) technology was proposed in the 1950s to simulate the human brain to perform complex calculations. With the improvement of data storage and computing power, AI has been increasingly used. Currently, the 3rd Generation Partnership Project (3GPP) proposes that AI can be applied to the new radio (NR) system to improve network performance and user experience through intelligent data collection and analysis.

[0003] Currently, terminal devices are required to have AI functions, so they need to obtain AI models from the network side to implement AI functions. However, the terminal devices have different requirements for models in different mobile scenarios, and the way the network side transmits AI models to terminal devices is not well adapted to different mobile scenarios, resulting in a waste of transmission resources. Summary of the invention

[0004] The embodiments of the present application provide a communication method and device for reducing the waste of transmission resources.

[0005] In a first aspect, an embodiment of the present application provides a communication method, which can be performed by a second communication device, or by other devices including the functions of the second communication device, or by a chip system (or, chip) or other functional modules, which can realize the functions of the second communication device, and the chip system or functional module is, for example, set in the second communication device. The second communication device may be an access network device or a module (such as a chip or circuit) in the access network device, or a CU, or a CU-CP, or a CU-CP2, etc. The communication method includes: the second communication device receives the first information. The first information may come from the first communication device. The first information indicates the transmission requirements of the first communication device for the first artificial intelligence AI model to be applied; the first information is used to determine the transmission mode of the first AI model; and the second information is sent to the first communication device, and the second information is used to indicate the transmission mode of the first AI model. Exemplarily, the first communication device may be a terminal device or a chip or functional module in a terminal device.

[0006] In the embodiment of the present application, after the access network device side receives the transmission demand from the terminal device side, it determines which transmission mode to use for the terminal device to transmit the first AI model to the terminal device according to the demand. The method adopted in the present application can match the transmission demand of the terminal device, and the determined transmission mode can adapt to the needs of the terminal device in different mobile scenarios, reducing the waste of transmission resources.

[0007] In one possible design, the transmission method of the first AI model includes one or more of the following: an access network device transmission method, wherein the access network device transmission method indicates that the first AI model is transmitted from the access network device to the first communication device; a third-party server OTT transmission method, wherein the OTT transmission method indicates that the first AI model is transmitted from the OTT to the first communication device; or, a core network device transmission method, wherein the core network device transmission method indicates that the first AI model is transmitted from the core network device to the first communication device.

[0008] In one possible design, the first information indicates one or more of the following: expected time; an identifier of the first AI model; metadata of the first AI model; a first AI function supported by the first AI model; an expected minimum transmission delay; a second AI function to be executed after completing the first AI function; or a moving path of the first communication device.

[0009] In one possible design, the method also includes: receiving third information from a first communication device, the third information including first sub-information, the first sub-information indicating an ability parameter of the first communication device to receive the first AI model, and the third information is used to determine a transmission method of the first AI model.

[0010] In the above method, the terminal device side reports its own receiving AI model capability parameters to the access network device side, so that the access network device side can determine a better transmission method of the AI ​​model that can match the capabilities of the terminal device for the terminal device based on the current capabilities of the terminal device and the needs of the terminal device.

[0011] In one possible design, the first sub-information includes one or more of the following: storage capacity parameters of the first communication device; computing capacity parameters of the first communication device; communication capacity parameters of the first communication device; transmission rate allowed by the first communication device; arrival time of the first AI model expected by the first communication device; and model transmission mode supported by the first communication device.

[0012] In the above method, the storage capacity, computing capacity and communication capacity of the terminal device side change in real time. The access network device side configures the model transmission mode for the terminal device according to these capacity parameters of the terminal device side, which can improve the accuracy of determining the model transmission mode and improve the transmission efficiency.

[0013] In one possible design, the third information also includes second sub-information, where the second sub-information indicates capability parameters of a second AI model supported by the third-party application service OTT and provided to the first communication device, and the second AI model includes the first AI model.

[0014] In one possible design, the second sub-information includes one or more of the following: an identifier of the second AI model; an identifier of the OTT; metadata of the second AI model; a transmission rate supported by the OTT for transmitting the second AI model; a transmission delay supported by the OTT for transmitting the second AI model; an arrival time supported by the OTT for transmitting the second AI model; or a model transmission method supported by the OTT.

[0015] In one possible design, the access network device may request capability parameters of the second AI model supported by OTT from OTT.

[0016] In one possible design, receiving third information from a first communication device includes: periodically receiving third information from the first communication device.

[0017] In the above method, the terminal device periodically reports its own capability parameters, or periodically reports its own capability parameters and the capability parameters of the second AI model supported by OTT. After receiving the transmission requirements of the terminal device, the access network device no longer needs to obtain these capability parameters from the terminal device, which can improve transmission efficiency.

[0018] In one possible design, the method also includes: sending first indication information to the first communication device, the first indication information being used to indicate conditions that need to be met when the first communication device reports the third information.

[0019] In the above design, the access network device side instructs the terminal device side to report the capability parameters when the conditions are met, to prevent invalid reporting caused by the reported parameters on the terminal device side not changing, thereby reducing waste of resources.

[0020] In one possible design, the first indication information includes one or more of the following:

[0021] an identifier of a triggering event, where the triggering event indicates triggering the first communication device to report the third information;

[0022] triggering reporting of a transmission rate variation range satisfied by the third information; or

[0023] The receiving delay variation range satisfied by triggering reporting of the third information.

[0024] In one possible design, the method also includes: sending second indication information to the first communication device, the second indication information being used to instruct the first communication device to select to report part or all of the parameters in the third information based on current capabilities.

[0025] In the above design, the access network device side instructs the terminal device side to choose which parameters to report based on the current capabilities. For example, if some capabilities are not restricted, they do not need to be reported, which can reduce the waste of resources caused by reporting invalid capability parameters.

[0026] In one possible design, the method further includes: sending third indication information to the first communication device, where the third indication information is used to indicate parameters requested to be reported by the first communication device.

[0027] In the above design, the access network device side instructs the terminal device side which parameters to report according to its own needs, which can reduce the waste of resources caused by reporting invalid capability parameters.

[0028] In one possible design, the method also includes: receiving fourth information from a core network device, wherein the fourth information is used to indicate that the core network device supports providing capability parameters of a third AI model for the first communication device, and the third AI model includes the first AI model.

[0029] In one possible design, the fourth information includes one or more of the following: an identifier of the third AI model; metadata of the third AI model; a transmission rate supported by the core network device for transmitting the third AI model; a transmission delay supported by the core network device for transmitting the third AI model; an arrival time supported by the core network device for transmitting the third AI model; or a model transmission method supported by the core network device.

[0030] In one possible design, the method also includes: transmitting model transmission information corresponding to the first AI model to the first communication device; the model transmission information includes model information of the first AI model; or, the model transmission information includes the model information, and the model transmission information also includes an adopted transmission method and / or an estimated completion time; the adopted transmission method includes a user plane signaling method or a control plane signaling method.

[0031] In one possible design, the model information includes one or more of the following: an identifier of the first AI model; metadata of the first AI model; and fourth indication information, where the fourth indication information is used to indicate a structure of the first AI model.

[0032] In a second aspect, an embodiment of the present application provides a communication method, which can be performed by a first communication device. The first communication device can be a terminal device or a chip system (or, chip) or other functional module in the terminal device. The method includes: sending a first message to a second communication device, the first information being used to indicate the transmission requirements of the first communication device for the first artificial intelligence AI model to be applied; the first information being used to determine the transmission method of the first AI model; receiving a second message from the second communication device, the second information being used to indicate the transmission method of the first AI model.

[0033] In one possible design, the transmission method of the first AI model includes one or more of the following: an access network device transmission method, wherein the access network device transmission method indicates that the first AI model is transmitted from the access network device to the first communication device; an OTT transmission method, wherein the OTT transmission method indicates that the first AI model is transmitted from the OTT to the first communication device; or, a core network device transmission method, wherein the core network device transmission method indicates that the first AI model is transmitted from the core network device to the first communication device.

[0034] In one possible design, the first information indicates one or more of the following: expected time; an identifier of the first AI model; metadata of the first AI model; a first AI function supported by the first AI model; an expected minimum transmission delay; a second AI function to be executed after completing the first AI function; or a moving path of the first communication device.

[0035] In one possible design, the method also includes: sending third information to the second communication device, the third information including first sub-information, the first sub-information indicating the ability parameters of the first communication device to receive the first AI model, and the third information is used to determine the transmission method of the first AI model.

[0036] In one possible design, the first sub-information includes one or more of the following: storage capacity parameters of the first communication device; computing capacity parameters of the first communication device; communication capacity parameters of the first communication device; transmission rate allowed by the first communication device; arrival time of the first AI model expected by the first communication device; and model transmission mode supported by the first communication device.

[0037] In one possible design, the third information also includes second sub-information, where the second sub-information indicates capability parameters of a second AI model supported by the third-party application service OTT and provided to the first communication device, and the second AI model includes the first AI model.

[0038] In one possible design, the second sub-information includes one or more of the following: an identifier of the second AI model; an identifier of the OTT; metadata of the second AI model; a transmission rate supported by the OTT for transmitting the second AI model; a transmission delay supported by the OTT for transmitting the second AI model; an arrival time supported by the OTT for transmitting the second AI model; or a model transmission method supported by the OTT.

[0039] In one possible design, the method further includes: receiving sixth information from OTT, the sixth information indicating that OTT supports capability parameters of the second AI model provided to the first communication device. Exemplarily, the sixth information includes one or more of the following: an identifier of the second AI model; an identifier of the OTT; metadata of the second AI model; a transmission rate at which the OTT supports the transmission of the second AI model; a transmission delay at which the OTT supports the transmission of the second AI model; an arrival time at which the OTT supports the transmission of the second AI model; or a model transmission method supported by the OTT.

[0040] In one possible design, sending the third information to the second communication device includes: periodically sending the third information to the second communication device.

[0041] In one possible design, the method also includes: receiving first indication information from the second communication device, the first indication information being used to indicate conditions that need to be met when the first communication device reports the third information.

[0042] In one possible design, the first indication information includes one or more of the following: an identifier of a triggering event, wherein the triggering event indicates triggering the first communication device to report the third information; a transmission rate variation range satisfied by triggering reporting of the third information; or a reception delay variation range satisfied by triggering reporting of the third information.

[0043] In one possible design, the method also includes: receiving second indication information from a second communication device, wherein the second indication information is used to instruct the first communication device to select to report part or all of the parameters in the third information based on current capabilities.

[0044] In one possible design, the method further includes: receiving third indication information from the second communication device, the third indication information being used to indicate parameters requested to be reported by the first communication device.

[0045] In one possible design, the method also includes: receiving model transmission information corresponding to the first AI model from the second communication device; the model transmission information includes model information of the first AI model; or, the model transmission information includes the model information, and the model transmission information also includes an adopted transmission method and / or an estimated completion time; the adopted transmission method includes a user plane signaling method or a control plane signaling method.

[0046] In one possible design, the model information includes one or more of the following: an identifier of the first AI model; metadata of the first AI model; or fourth indication information, where the fourth indication information is used to indicate a structure of the first AI model.

[0047] In a third aspect, an embodiment of the present application provides a communication method, which can be executed by a second communication device, or by other devices including the functions of the second communication device, or by a chip system (or, chip) or other functional module, which can realize the functions of the second communication device, and the chip system or functional module is, for example, set in the second communication device. The second communication device can be an access network device or a module (such as a chip or circuit) in the access network device, or a CU, or a CU-CP, or a CU-CP2, etc. The communication method includes: sending fifth information to a first communication device, the fifth information is used to indicate the transmission requirements of a first artificial intelligence AI model, and the first AI model is an AI model requested to be configured by the first communication device; receiving third information from the first communication device, the third information includes first sub-information, the first sub-information indicates the ability parameters of the first communication device to receive the first AI model, and the third information is used to determine the transmission mode of the first AI model; sending second information to the first communication device, the second information is used to indicate the transmission mode of the first AI model.

[0048] In the embodiment of the present application, after the access network device sends a transmission requirement to the terminal device, the terminal device reports its own receiving AI model capability parameters to the access network device, so that the access network device can determine a better transmission mode of the AI ​​model that can match the capabilities of the terminal device for the terminal device based on the current capabilities of the terminal device and its own needs. The determined transmission mode can adapt to the needs of the terminal device in different mobile scenarios and reduce the waste of transmission resources.

[0049] In one possible design, the transmission mode of the first AI function includes one or more of the following: an access network device transmission mode, wherein the access network device transmission mode indicates that the first AI model is transmitted from the access network device to the first communication device; an OTT transmission mode, wherein the OTT transmission mode indicates that the first AI model is transmitted from the OTT to the first communication device; or, a core network device transmission mode, wherein the core network device transmission mode indicates that the first AI model is transmitted from the core network device to the first communication device.

[0050] In one possible design, the fifth information indicates one or more of the following: the first AI model; the first AI function supported by the first AI model; the capability requested to be reported by the first communication device, or the reason for transmission.

[0051] In one possible design, the first sub-information includes one or more of the following: storage capacity parameters of the first communication device; computing capacity parameters of the first communication device; communication capacity parameters of the first communication device; transmission rate allowed by the first communication device; arrival time of the first AI model expected by the first communication device; or model transmission method supported by the first communication device.

[0052] In one possible design, the third information also includes second sub-information, where the second sub-information indicates capability parameters of a second AI model supported by the third-party application service OTT and provided to the first communication device, and the structure of the second AI model includes the first AI model.

[0053] In one possible design, the second sub-information includes one or more of the following: an identifier of the second AI model; an identifier of the OTT; metadata of the second AI model; a transmission rate supported by the OTT for transmitting the second AI model; a transmission delay supported by the OTT for transmitting the second AI model; an arrival time supported by the OTT for transmitting the second AI model; or a model transmission method supported by the OTT.

[0054] In one possible design, the method also includes: receiving fourth information from a core network device, the fourth information being used to indicate that the core network device supports capability parameters of a third AI model provided for the first communication device, the third AI model including the first AI model.

[0055] In one possible design, the fourth information includes one or more of the following: an identifier of the third AI model; metadata of the third AI model; a transmission rate supported by the core network device for transmitting the third AI model; a transmission delay supported by the core network device for transmitting the third AI model; an arrival time supported by the core network device for transmitting the third AI model; or a model transmission method supported by the core network device.

[0056] In one possible design, the method also includes: sending a request message to a core network device, wherein the request message is used to request the core network device to support capability parameters of the third AI model provided for the first communication device.

[0057] In one possible design, the method also includes: transmitting model transmission information corresponding to the first AI model to the first communication device; the model transmission information includes model information of the first AI model; or, the model transmission information includes the model information, and the model transmission information also includes a transmission method and / or an estimated completion time; the transmission method includes using user plane signaling or using control plane signaling.

[0058] In one possible design, the model information includes one or more of the following: an identifier of the first AI model; metadata of the first AI model; or fourth indication information, where the fourth indication information is used to indicate a structure of the first AI model.

[0059] In a fourth aspect, an embodiment of the present application provides a communication method, which can be performed by a first communication device. The first communication device can be a terminal device or a chip system (or, chip) or other functional module in the terminal device. The method includes: receiving fifth information from a second communication device, the fifth information is used to indicate the transmission requirements of a first artificial intelligence AI model, and the first AI model is an AI model requested to be configured by the first communication device; sending third information to the second communication device, the third information includes first sub-information, the first sub-information indicates the first communication device to receive the capability parameters of the first AI model, and the third information is used to determine the transmission mode of the first AI model; receiving second information from the second communication device, the second information is used to indicate the transmission mode of the first AI model.

[0060] In one possible design, the transmission manner of the first AI function includes one or more of the following:

[0061] An access network device transmission mode, wherein the access network device transmission mode indicates that the first AI model is transmitted from the access network device to the first communication device; an OTT transmission mode, wherein the OTT transmission mode indicates that the first AI model is transmitted from the OTT to the first communication device; or, a core network device transmission mode, wherein the core network device transmission mode indicates that the first AI model is transmitted from the core network device to the first communication device.

[0062] In one possible design, the fifth information indicates one or more of the following: the first AI model; the first AI function supported by the first AI model; the capability requested to be reported by the first communication device, or the reason for transmission.

[0063] In one possible design, the first sub-information includes one or more of the following: storage capacity parameters of the first communication device; computing capacity parameters of the first communication device; communication capacity parameters of the first communication device; transmission rate allowed by the first communication device; arrival time of the first AI model expected by the first communication device; or model transmission method supported by the first communication device.

[0064] In one possible design, the third information also includes second sub-information, where the second sub-information indicates capability parameters of a second AI model supported by the third-party application service OTT and provided to the first communication device, and the structure of the second AI model includes the first AI model.

[0065] In one possible design, the second sub-information includes one or more of the following: an identifier of the second AI model; an identifier of the OTT; metadata of the second AI model; a transmission rate supported by the OTT for transmitting the second AI model; a transmission delay supported by the OTT for transmitting the second AI model; an arrival time supported by the OTT for transmitting the second AI model; or a model transmission method supported by the OTT.

[0066] In one possible design, the method also includes: receiving model transmission information corresponding to the first AI model from the second communication device; the model transmission information includes model information of the first AI model; or, the model transmission information includes the model information, and the model transmission information also includes a transmission method and / or an estimated completion time; the transmission method includes using user plane signaling or using control plane signaling.

[0067] In one possible design, the model information includes one or more of the following: an identifier of the first AI model; metadata of the first AI model; or fourth indication information, where the fourth indication information is used to indicate a structure of the first AI model.

[0068] In a fifth aspect, a communication device is provided. The communication device may be the second communication device described in any one of the first to fourth aspects. The communication device has the function of the second communication device. The communication device is, for example, a second communication device, or a larger device including the second communication device, or a functional module in the second communication device, such as a baseband device or a chip system. In an optional implementation, the communication device includes a baseband device and a radio frequency device. In another optional implementation, the communication device includes a processing unit (sometimes also referred to as a processing module) and a transceiver unit (sometimes also referred to as a transceiver module). The transceiver unit can implement a sending function and a receiving function. When the transceiver unit implements the sending function, it can be referred to as a sending unit (sometimes also referred to as a sending module), and when the transceiver unit implements the receiving function, it can be referred to as a receiving unit (sometimes also referred to as a receiving module). The sending unit and the receiving unit can be the same functional module, which is called a transceiver unit, and the functional module can implement a sending function and a receiving function; or, the sending unit and the receiving unit can be different functional modules, and the transceiver unit is a general term for these functional modules.

[0069] In an optional embodiment, the transceiver unit (or, the receiving unit) is used to receive first information, where the first information indicates the transmission requirement of the first communication device for the first artificial intelligence AI model to be applied; the first information is used to determine the transmission method of the first AI model; the transceiver unit (or, the sending unit) sends second information to the first communication device, where the second information is used to indicate the transmission method of the first AI model.

[0070] In an optional implementation, the transceiver unit (or, the sending unit) is used to send fifth information to the first communication device, the fifth information is used to indicate the transmission requirements of the first artificial intelligence AI model, the first AI model is the AI ​​model that requests the first communication device to be configured; the transceiver unit (or, the receiving unit) is used to receive third information from the first communication device, the third information includes first sub-information, the first sub-information indicates the ability parameters of the first communication device to receive the first AI model, and the third information is used to determine the transmission method of the first AI model. The transceiver unit (or, the sending unit) is used to send second information to the first communication device, the second information is used to indicate the transmission method of the first AI model.

[0071] In an optional embodiment, the communication device also includes a storage unit (sometimes also referred to as a storage module), and the processing unit is used to couple with the storage unit and execute the program or instructions in the storage unit, so that the communication device can perform the function of the second communication device described in any one of the first to fourth aspects above.

[0072] In a sixth aspect, a communication device is provided. The communication device may be the first communication device described in any one of the first to fourth aspects. The communication device has the functions of the first communication device. The communication device is, for example, a terminal device, or a larger device including a terminal device, or a functional module in a terminal device, such as a baseband device or a chip system. In an optional implementation, the communication device includes a baseband device and a radio frequency device. In another optional implementation, the communication device includes a processing unit (sometimes also referred to as a processing module) and a transceiver unit (sometimes also referred to as a transceiver module). For the implementation of the transceiver unit, reference may be made to the introduction of the seventh aspect.

[0073] In an optional embodiment, the transceiver unit (or, the sending unit) is used to send first information to a second communication device, wherein the first information is used to indicate the transmission requirement of the first communication device for a first artificial intelligence AI model to be applied; the first information is used to determine the transmission method of the first AI model; the transceiver unit (or, the receiving unit) is used to receive second information from the second communication device, wherein the second information is used to indicate the transmission method of the first AI model.

[0074] In an optional embodiment, the transceiver unit (or, the receiving unit) is used to receive fifth information from the second communication device, the fifth information is used to indicate the transmission requirements of the first artificial intelligence AI model, and the first AI model is the AI ​​model that requests the first communication device to configure; the transceiver unit (or, the sending unit) is used to send third information to the second communication device, the third information includes first sub-information, the first sub-information indicates the ability parameters of the first communication device to receive the first AI model, and the third information is used to determine the transmission method of the first AI model. The transceiver unit (or, the receiving unit) is used to receive second information from the second communication device, and the second information is used to indicate the transmission method of the first AI model.

[0075] In an optional embodiment, the communication device also includes a storage unit (sometimes also referred to as a storage module), and the processing unit is used to couple with the storage unit and execute the program or instructions in the storage unit, so that the communication device can perform the functions of the terminal device described in any one of the first to fourth aspects above.

[0076] In the seventh aspect, a communication system is provided, comprising a first communication device and a second communication device, wherein the first communication device is used to execute the method executed by the first communication device as described in the second aspect or the fourth aspect, and the second communication device is used to execute the method executed by the second communication device as described in the first aspect or the third aspect.

[0077] In an eighth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium is used to store a computer program or instruction, which, when executed, enables the method performed by the first communication device or terminal equipment in the above aspects to be implemented.

[0078] According to a ninth aspect, a computer program product comprising instructions is provided, which, when executed on a computer, enables the methods described in the above aspects to be implemented.

[0079] In a tenth aspect, a chip system is provided, comprising a processor and an interface, wherein the processor is used to call and execute instructions from the interface so that the chip system implements the above-mentioned methods.

[0080] Based on the implementations provided in the above aspects, the present application can also be further combined to provide more implementations. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A structural diagram of an access network device;

[0082] Figure 2 This is a framework diagram for applying AI in NR;

[0083] Figure 3 A schematic diagram of a network architecture used in an embodiment of the present application;

[0084] Figure 4 A flow chart of a communication method provided in an embodiment of the present application;

[0085] Figure 5 A flowchart of another communication method provided in an embodiment of the present application;

[0086] Figure 6 A flowchart of another communication method provided in an embodiment of the present application;

[0087] Figure 7 A flowchart of another communication method provided in an embodiment of the present application;

[0088] Figure 8 A flowchart of another communication method provided in an embodiment of the present application;

[0089] Fig. 9 A schematic diagram of the structure of a device 900 provided in an embodiment of the present application;

[0090] Fig.10 A schematic diagram of the structure of a device 1000 provided in an embodiment of the present application. DETAILED DESCRIPTION

[0091] The technical solution provided in the embodiment of the present application can be applied to the fourth generation mobile communication technology (the 4th generation, 4G) system, such as the long term evolution (long term evolution, LTE) system, or can be applied to the fifth generation mobile communication technology (the 5th generation, 5G) system, such as the NR system, or can also be applied to the next generation mobile communication system or other similar communication systems, such as the sixth generation mobile communication technology (the 6th generation, 6G) system, etc., without specific limitation. In addition, the technical solution provided in the embodiment of the present application can also be applied to device-to-device (D2D) scenarios, such as NR-D2D scenarios, etc., or can be applied to vehicle networking (vehicle to everything, V2X) scenarios, such as NR-V2X scenarios, or intelligent driving, assisted driving, or intelligent networked vehicles and other fields. If applied to the D2D scenario, both parties of the communication can be UE; if applied to the non-D2D scenario, one party of the communication can be UE, and the other party is a network device (such as an access network device), or both parties of the communication may be network devices. In the following introduction process, the communication parties are network devices and UEs as an example.

[0092] In the embodiments of the present application, the number of nouns, unless otherwise specified, means "singular noun or plural noun", that is, "one or more". "At least one" means one or more, and "plural" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. For example, A / B means: A or B. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c means: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0093] The ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the size, content, order, timing, priority or importance of multiple objects. For example, the first resource unit and the second resource unit can be the same resource unit or different resource units, and this name does not indicate the difference in the position, size, application scenario, priority or importance of the two resource units. In addition, the numbering of the steps in the various embodiments introduced in the present application is only to distinguish different steps, and is not used to limit the order of the steps. For example, S401 may occur before S402, or may occur after S402, or may occur at the same time as S402.

[0094] Below, some terms or concepts in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0095] 1. In the embodiment of the present application, the terminal device is a device with wireless transceiver function, which can be a fixed device, a mobile device, a handheld device (such as a mobile phone), a wearable device, a vehicle-mounted device, or a wireless device built into the above device (such as a communication module, a modem, or a chip system, etc.). The terminal device is used to connect people, objects, machines, etc., and can be widely used in various scenarios, such as but not limited to the following scenarios: cellular communication, device-to-device communication (device-to-device, D2D), vehicle to everything (vehicle to everything, V2X), machine-to-machine / machine-type communication (machine-to-machine / machine-type communications, M2M / MTC), Internet of Things (Internet of Things, IoT), virtual reality (virtual reality, VR), augmented reality (augmented reality, AR), industrial control (industrial control), self-driving, remote medical, smart grid (smart grid), smart furniture, smart office, smart wear, smart transportation, smart city (smart city), drones, robots and other scenarios of terminal devices. The terminal device may sometimes be referred to as user equipment (UE), terminal, access station, UE station, remote station, wireless communication device, or user device, etc. For ease of description, the terminal device is described below by taking UE as an example.

[0096] 2. The network device in the embodiment of the present application is a device in a wireless network, for example, a radio access network (RAN) node or a radio access network device that connects a terminal device to a wireless network. At present, some examples of RAN nodes are: gNB, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), base station controller (BSC), base transceiver station (BTS), home base station (e.g., home evolved NodeB, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wifi) access point (AP), integrated access and backhaul (IAB), etc. In a network structure, a RAN node may also refer to a central unit (CU) or a distributed unit (DU), or a RAN node may also be composed of a CU and a DU. CU and DU can be understood as the division of RAN nodes from the perspective of logical functions. CU and DU are connected through the F1 interface; CU can represent gNB and is connected to the core network through the NG interface. Among them, CU and DU can be physically separated or deployed together, and the embodiment of the present application does not specifically limit this. A CU can be connected to a DU, or multiple DUs can share a CU, which can save costs and facilitate network expansion. The division of CU and DU can be divided according to the protocol stack. One possible way is to deploy the radio resource control (RRC), service data adaptation protocol stack (SDAP) and packet data convergence protocol (PDCP) layer in the CU, and the remaining radio link control (RLC) layer, media access control (MAC) layer and physical layer (PHY) in the DU. The embodiment of the present application does not completely limit the above-mentioned protocol stack division method, and there may be other division methods.

[0097] The RAN node in the embodiment of the present application may also include a centralized unit control plane (CU-CP) node and / or a centralized unit user plane (CU-UP) node, which can be referenced Figure 1 . Among them, CU-CP is responsible for control plane functions, mainly including RRC and PDCP-control (control, C). PDCP-C is mainly responsible for encryption and decryption, integrity protection, data transmission, etc. of control plane data. The control plane CU-CP of CU also includes a further segmented architecture, that is, the existing CU-CP is further divided into CU-CP1 and CU-CP2. Among them, CU-CP1 includes various wireless resource management functions, and CU-CP2 only includes RRC functions and PDCP-C functions (that is, the basic functions of control plane signaling at the PDCP layer). CU-UP is responsible for user plane functions, mainly including SDAP and PDCP-user (user, U). Among them, SDAP is mainly responsible for processing the data of the core network and mapping the flow to the bearer. PDCP-U is mainly responsible for encryption and decryption, integrity protection, header compression, sequence number maintenance, data transmission, etc. of the data plane. Among them, CU-CP and CU-UP are connected through the E1 interface. CU-CP can represent the RAN node, connected to the core network through the NG interface, and connected to the DU through F1-C (control plane). Another possible implementation is to also set PDCP-C in CU-UP.

[0098] In the embodiment of the present application, the communication device for realizing the function of the network device may be a network device, or may be a device capable of supporting the network device to realize the function, such as a chip system, which may be installed in the network device. In the technical solution provided in the embodiment of the present application, the technical solution provided in the embodiment of the present application is described by taking the device for realizing the function of the network device as an example that the network device is used as the device.

[0099] The network equipment and terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons and satellites in the air. The embodiments of the present application do not limit the application scenarios of the network equipment and terminal equipment.

[0100] Network devices and terminal devices, as well as terminal devices and terminal devices, can communicate through licensed spectrum, or through unlicensed spectrum, or through both licensed spectrum and unlicensed spectrum. Network devices and terminal devices, as well as terminal devices and terminal devices, can communicate through spectrum below 6 gigahertz (GHz), or through spectrum above 6G, or through spectrum below 6G and spectrum above 6G at the same time. The embodiments of the present application do not limit the spectrum resources used between network devices and terminal devices.

[0101] 3. Currently, 3GPP proposes that AI can be applied to NR systems. By intelligently collecting and analyzing data, network performance and user experience can be improved. 3GPP has initially defined a framework for the application of AI in NR. Figure 2 Among them, the data source can store data from gNB, gNB-CU, gNB-DU, UE or other management entities. The data source can be used as a database for AI model training and data analysis and reasoning, for example Figure 2 The "data collection" in the data source can refer to the data source. The model training host can provide the optimal AI model by analyzing the training data provided by the data source, such as Figure 2 The "Model Training" box in the figure represents the model training host. The model inference host uses the AI ​​model to make reasonable AI-based predictions about the operation of the network based on the data provided by the data source, or guide the network to make policy adjustments, such as Figure 2 The "Model Reasoning" box in the figure represents the model reasoning host. The relevant policy adjustments are planned uniformly by the actor entity and sent to multiple network entities for execution. At the same time, after the network applies the adjusted policy, the specific performance information of the network will be input into the data source again for storage.

[0102] 4. AI model.

[0103] AI model, also known as AI algorithm (or AI operator), is a general term for mathematical algorithms built on the principles of artificial intelligence, and is also the basis for using AI to solve specific problems. The type of AI model is not limited in the embodiments of the present application. For example, the AI ​​model can be a machine learning model, a deep learning model, or a reinforcement learning model. The AI ​​model can also be replaced by a model level (ML) model.

[0104] Machine learning is a method for implementing artificial intelligence. The goal of this method is to design and analyze some algorithms (i.e., models) that allow computers to "learn" automatically. The designed algorithms are called machine learning models. Machine learning models are a type of algorithm that automatically analyzes data to obtain patterns and uses the patterns to predict unknown data. There are many types of machine learning models. For example, depending on whether the model training needs to rely on the labels corresponding to the training data, machine learning models can be divided into supervised learning models and unsupervised learning models.

[0105] Deep learning is a new technical field that emerged in the process of machine learning research. Specifically, deep learning is a method in machine learning based on deep representation learning of data. Deep learning interprets data by establishing a neural network that simulates the human brain for analysis and learning. Since in machine learning methods, almost all features need to be determined by industry experts and then encoded. However, deep learning algorithms try to learn features from data by themselves. Algorithms designed based on deep learning ideas are called deep learning models.

[0106] Reinforcement learning is a special field in machine learning. It is a process of continuously learning the optimal strategy, making sequential decisions, and obtaining the maximum reward through the interaction between the agent and the environment. In layman's terms, reinforcement learning is learning "what to do (i.e. how to map the current situation into actions) to maximize the numerical benefit signal". The agent will not be told what action to take, but must try to find out which actions will produce the most lucrative benefits. Reinforcement learning is different from supervised learning and unsupervised learning in the field of machine learning. Supervised learning is the process of learning from labeled training data provided externally (task-driven), and unsupervised learning is the process of finding implicit structures in unlabeled data (data-driven). Reinforcement learning is the process of finding a better solution through "trials". The agent must develop existing experience to obtain benefits, and also conduct trials so that it can obtain a better action selection space in the future (i.e. learn from mistakes). The algorithm designed based on reinforcement learning is called a reinforcement learning model.

[0107] Any AI model needs to be trained before it can be used to solve specific technical problems. AI model training refers to the process of using a specified initial model to calculate the training data, and adjusting the parameters in the initial model using a certain method based on the calculation results, so that the model gradually learns certain rules and has specific functions. After training, the AI ​​model with stable functions can be used for reasoning. AI model reasoning is the process of using the trained AI model to calculate the input data and obtain the predicted reasoning results (also called output data).

[0108] 5. AI functions, or AI-based use cases.

[0109] Currently, 3GPP has designed some basic application scenarios for AI applications on the RAN side, such as channel state information (CSI)-reference signal (RS) feedback enhancement (referred to as CSI feedback enhancement), beam management enhancement or positioning accuracy enhancements, etc. The following briefly introduces these scenarios. Among them, if the UE supports a certain AI scenario, it can also be considered that the UE has the AI ​​function.

[0110] (1) CSI-RS feedback enhancement.

[0111] CSI is the channel attribute of the communication link and is the channel quality information sent by the UE to the access network device. The UE sends the downlink CSI to the access network device so that the access network device can select a more appropriate modulation and coding scheme (MCS) for the UE to better adapt to the changing wireless channel. The CSI sent by the UE may include one or more of the following: channel quality indicator (CQI), precoding matrix indicator (PMI), CSI-RS resource indicator (CRI), SS / PCH block resource indicator (SSBRI), layer indicator (LI), rank indicator (RI), or layer 1 reference signal receiver power (L1-RSRP). Among them, rank is the rank of the antenna matrix in the multiple input multiple output (MIMO) scheme, representing one or more parallel valid data streams, and RI is used to indicate the number of valid data layers of the physical downlink shared channel (PDSCH).

[0112] CSI-RS feedback enhancement may include sub-scenarios such as CSI compression, CSI prediction, and CSI-RS configuration signaling reduction. Among them, CSI compression may include compression in at least one of the spatial domain, the time domain, or the frequency domain.

[0113] Taking CSI-RS compression as an example, an implementation process is as follows:

[0114] A1. The access network device and the UE first exchange a dictionary.

[0115] In addition, the access network device trains the model in advance according to factors such as the capabilities of the UE and the requirements of the access network device. The model includes, for example, an encoder model and a corresponding decoder model. The access network device can send the encoder model to the UE, and can also send tools such as a quantizer to the UE. The quantizer is used to quantize the compressed signal.

[0116] A2. The UE may obtain a first matrix according to the measured downlink channel matrix and the encoder network.

[0117] For example, the UE may input the measured downlink channel matrix into the encoder network to obtain a first matrix output by the encoder network. Alternatively, the UE may preprocess the measured downlink channel matrix, and then input the preprocessed downlink channel matrix into the encoder network to obtain a first matrix output by the encoder network. Further, the UE may compress the first matrix using an existing dictionary, and may quantize the compressed signal using a quantizer, and then send the quantization result to the access network device.

[0118] A3. The access network device reversely recovers the original downlink channel matrix based on the dictionary, the decoder network and the quantization result from the UE.

[0119] (2) Enhanced beam management.

[0120] AI-based beam management enhancement can include sub-scenarios such as beam scanning matrix prediction and optimal beam prediction. One implementation process of beam management enhancement is as follows:

[0121] B1. Generation of the initial model. A certain number of UEs perform omnidirectional beam scanning on the synchronization signal and physical broadcast channel (PBCH) block (SSB). These UEs can send the scanning results to the access network device. The scanning results include, for example, the received signal quality information on each beam. The access network device can be trained based on the scanning results of these UEs to obtain a sparse scanning matrix, or a sparse model, which is usually unique to a cell. The sparse model may include information about a better beam.

[0122] B2. The access network device sends the sparse model to the UE (it may be sent through a system information block (SIB) message or the like), and the UE may perform beam scanning in the P1 phase according to the sparse matrix, and the UE may send the scanning result to the access network device, which is called a sparse scanning result, for example. In the P1 phase, the access network device sends a reference signal through a beam in a wider range, and the UE selects the best beam according to the measurement, and sends the information of the best beam to the access network device.

[0123] B3. The access network device obtains the optimal beam based on the sparse scanning result.

[0124] For example, the access network device can perform P2 scanning on the UE based on the beam, and the UE can send the identifier of the best beam to the access network device. P2 scanning means that the access network device sends a reference signal through a narrower beam within a narrower range, and the UE selects the best beam based on the measurement and sends the information of the best beam to the access network device.

[0125] (3) Enhanced positioning accuracy.

[0126] AI-based positioning accuracy enhancement is mainly to improve positioning accuracy. One implementation process of positioning accuracy enhancement is as follows:

[0127] C1. Collecting raw data through a reference UE controlled by an operator. The raw data includes, for example, UE coordinate information and / or line of sight (LOS) / non-line of sight (NLOS) status information.

[0128] C2, location management function (LMF) and access network equipment train models respectively. The model trained by LMF can be used to infer the final positioning information of the UE (such as the longitude and latitude of the UE) based on the original data, and the model trained by gNB can be used to infer the LOS / NLOS judgment result based on the original data.

[0129] 6. Collaboration level of AI functions.

[0130] Taking into account the application of AI functions (such as the deployment of AI models) and factors such as the interaction between the UE and the network side, three levels of collaboration are currently included, namely level 0, level 1 and level 2, which are introduced below.

[0131] Level 0, also known as collaboration level 0, is a no collaboration level.

[0132] At level 0, the AI ​​model on the access network device side is completely invisible to the UE. The training and reasoning processes of the AI ​​model are all completed inside the access network device and have no impact on the air interface.

[0133] Level 1, also known as collaboration level 1, is a signaling-based collaboration without model transfer.

[0134] At level 1, the access network device sends a dictionary to the UE, so that the UE can assist the access network device in AI reasoning, which has a certain impact on the air interface. The AI ​​model can also be deployed inside the UE, and the UE can compress and encode the downlink channel matrix according to the AI ​​model and dictionary deployed by the UE. The UE and the access network device can jointly train the AI ​​model, for example, through federated learning. On the air interface, the AI ​​model will not be directly transmitted between the UE and the access network device, but the relevant parameters of the AI ​​model can be transmitted. In addition, considering the deployment of the AI ​​model, level 1 can also be further subdivided into level 1A and level 1B.

[0135] Level 1A, signaling-based collaboration for single-sided model without model transfer. Under level 1A, the AI ​​model can be deployed in the access network device or UE, that is, the AI ​​model is deployed on a single side.

[0136] Level 1B, signaling-based collaboration for two-sided model without model transfer. Under level 1B, the access network equipment and UE can deploy AI models separately, that is, the AI ​​model is deployed on both sides.

[0137] Level 2, also known as collaboration level 2, is a signaling-based collaboration with model transfer level.

[0138] At level 2, both the access network device and the UE can have AI functions. For example, the access network device and the UE can deploy AI models respectively, and the access network device and the UE can perform reasoning and other processes based on the deployed AI models. On the air interface, the access network device and the UE can directly transmit the AI ​​model.

[0139] In addition to the above three collaboration levels, other collaboration levels may also be included. For example, future communication systems may also support some collaboration levels, which is not limited in the embodiments of the present application.

[0140] 7. Distribution of AI models.

[0141] In some possible application scenarios, the UE side needs to have AI functions and needs to download AI models from the network side to implement AI functions. Currently, the situations where UEs have AI functions can be divided into two categories: Category 1: The UE side needs to independently complete specific services based on the AI ​​model, such as AI-based encoding of the CSI channel. Category 2: The UE side needs to assist the access network equipment to complete the reasoning or training of the AI ​​model, but the UE currently does not have an AI model or needs to update the AI ​​model.

[0142] The transmission method of the AI ​​model designed in the embodiment of the present application may include: (1) access network device transmission method, in which the access network device transmits the AI ​​model to the UE. (2) core network device transmission method, in which the core network device transmits the AI ​​model to the UE. (3) third-party server transmission method, such as OTT (over-the-top) transmission method or OAM transmission method. Among them, OTT refers to providing various application services to users through the Internet. This application is different from the communication services currently provided by operators. It only uses the operator's network, and the service is provided by a third party outside the operator.

[0143] Exemplarily, the node that sends the AI ​​model to the UE may be an access network device, a core network device other than LMF, LMF, or a third-party server.

[0144] For example, Figure 3 A communication network architecture applicable to an embodiment of the present application is provided. A first communication device can communicate with a network device. The first communication device may be a UE, or a chip or chip system in the UE, or a functional module in the UE, such as a processing module. The second communication device can communicate with the UE, for example, the UE can send AI capability information of the UE to the second communication device. The second communication device is, for example, an access network device; or, the second communication device is a chip or chip system in the access network device, or the second communication device is a functional module included in the access network device, such as a CU, or CU-CP, or CU-CP2, etc.; or, the second communication device is a larger device including an access network device. The access network device is, for example, the aforementioned RAN node, and reference can be made to the previous text for the introduction of the RAN node and the UE.

[0145] In order to better describe the embodiments of the present application, the method provided by the embodiments of the present application is described below in conjunction with the accompanying drawings. In the accompanying drawings corresponding to the various embodiments of the present application, all steps indicated by dotted lines are optional steps.

[0146] The present application embodiment provides a communication method, referring to Figure 4 The figure is a flow chart of the communication method. The method can be applied to Figure 3 For example, the first communication device in the method is Figure 3 The first communication device in the method is the second communication device Figure 3The second communication device in the UE. The first communication device may be a UE, or a chip or chip system in the UE, or a functional module in the UE. In the subsequent description, the first communication device is taken as an example of a UE. The second communication device is, for example, an access network device; or, the second communication device is a chip or chip system in the access network device, or the second communication device is a functional module included in the access network device, such as a CU, or CU-CP, or CU-CP2, etc.; or, the second communication device is a larger device that includes an access network device. In the subsequent description, the second communication device is taken as an access network device as an example.

[0147] S401, UE sends first information to access network equipment. Correspondingly, the access network equipment receives the first information from the UE.

[0148] The first information is used to indicate the transmission requirement of the UE for the first AI model to be applied. The first information may also be referred to as model transmission requirement information, or other names may be used. Optionally, the first information may indicate one or more of the following: expected time, an identifier of the first AI model, metadata of the first AI model, a first AI function supported by the first AI model, an expected minimum transmission delay, a second AI function to be executed after completing the first AI function, or a mobile path of the UE. The expected minimum transmission delay may also be a requirement for transmission delay. AI functions (such as the first AI function and the second AI function) may be understood as AI use cases, the first AI function may be referred to as the first AI use case, and the second AI function may be referred to as the second AI use case. In some embodiments, the first AI function supported by the first AI model may also be understood as the first AI use case to be executed. The expected time may be any one of a time period, a time point, or a time index. The first AI use case may be CSI feedback enhancement, beam enhancement, or positioning enhancement, or other AI use cases. The second AI use case may be CSI feedback enhancement, beam enhancement, or positioning enhancement, or other AI use cases.

[0149] Exemplarily, the metadata of the model may indicate one or more of the following: the input parameters used by the model, the output parameters of the model, the version number corresponding to the model, the formats supported by the model, the requirements of the model on UE capabilities, the identifiers of equipment vendors to which the model can be applied, the scenarios in which the model can be used, the AI ​​use cases supported by the model, the complexity of the model calculations, the processing power requirements, the range of the model size, the performance of the model (such as accuracy, etc.), or the functions possessed by the model.

[0150] After receiving the first information, the access network device can determine the transmission method of the first AI model based on the first information.

[0151] S402, the access network device sends second information to the UE. Correspondingly, the UE receives the second information from the access network device. The second information is used to indicate the transmission mode of the first AI model.

[0152] The transmission mode of the first AI model may include one or more of the following: access network device transmission mode, third-party server transmission mode or core network device transmission mode. The access network device transmission mode indicates that the first AI model is transmitted from the access network device to the UE. The third-party server transmission mode indicates that the first AI model is transmitted from the third-party server to the UE. The core network device transmission mode indicates that the first AI model is transmitted from the core network device to the UE. The third-party server may be a third-party device such as OAM or OTT.

[0153] Exemplarily, the access network transmission mode may include one or more of the following:

[0154] In mode 1a, the access network device transmits / sends the first AI model to the UE via control plane signaling. For example, the control plane signaling may be RRC signaling.

[0155] In mode 1b, the access network device transmits / sends the first AI model to the UE through user plane (UP) data.

[0156] Exemplarily, the core network device transmission mode may include one or more of the following:

[0157] Method 2a: The core network equipment other than LMF transmits / sends the first AI model to the UE through non-access stratum (NAS) signaling.

[0158] Mode 2b: The core network device other than LMF transmits / sends the first AI model to the UE through user plane data. For example, the core network device other than LMF may be an AMF or SMF device.

[0159] Mode 3a: LMF transmits / sends the first AI model to the UE through LTE positioning protocol (LPP) signaling.

[0160] Mode 3b, LMF transmits / sends the first AI model to the UE through user plane data.

[0161] In some embodiments, different AI use cases and model deployment have different requirements for models. Therefore, different requirements are placed on the model's functionality, size, and transmission latency. As an example, see Table 1, which describes the relationship between different methods and AI use cases. For ease of description, in Table 1, method 4 is a third-party server transmission method.

[0162] Table 1

[0163]

[0164] In a possible implementation, the information used to determine the transmission method of the first AI model may include, in addition to the first information, one or more of the following: the UE's ability parameters for receiving the first AI model, the OTT's ability parameters for transmitting the AI ​​model, or the core network device's ability parameters for transmitting the AI ​​model.

[0165] Exemplarily, the capability parameters of the UE for receiving the first AI model may include one or more of the following: a storage capability parameter of the UE, a computing capability parameter of the UE, a communication capability parameter of the UE, a transmission rate allowed by the UE, an arrival time of the first AI model expected by the UE, and a model transmission mode supported by the UE.

[0166] Exemplarily, the capability parameters of the OTT transmission AI model may include one or more of the following: an identifier of the second AI model, an identifier of OTT, metadata (meta) of the second AI model, a transmission rate supported by OTT for transmitting the second AI model, a transmission delay supported by OTT for transmitting the second AI model, an arrival time supported by OTT for transmitting the second AI model, or a model transmission mode supported by OTT. The second AI model may be an AI model that OTT supports providing to the UE. The second AI model may include the first AI model. For example, the first AI model is a sub-model in the second AI model, or the second AI model is the first AI model. In some embodiments, the second AI model may include multiple AI models (also referred to as multiple AI sub-models), and the embodiments of the present application do not limit the number of AI models of the second AI model.

[0167] Exemplarily, the capability parameters of the core network device for transmitting the AI ​​model may include one or more of the following: an identifier of a third AI model, metadata of a third AI model, a transmission rate supported by the core network device for transmitting the third AI model, a transmission delay supported by the core network device for transmitting the third AI model, an arrival time supported by the core network device for transmitting the third AI model, or a model transmission mode supported by the core network device. The third AI model may be an AI model provided by the core network device to the UE. The third AI model may include the first AI model. For example, the first AI model is a sub-model in the third AI model, or the third AI model is the first AI model. In some embodiments, the third AI model may include multiple AI models (also referred to as multiple AI sub-models), and the embodiments of the present application do not limit the number of AI models of the third AI model.

[0168] There may be multiple ways for the access network device to obtain information used to determine the transmission method of the first AI model, as shown below.

[0169] (1) The information used by the access network device to determine the transmission mode of the first AI model includes the capability parameter of the UE to receive the first AI model. The UE sends the capability parameter of the UE to receive the first AI model to the access network device, for example, the UE sends third information to the access network device, and the third information includes first sub-information, and the first sub-information indicates the capability parameter of the UE to receive the first AI model. For example, the first sub-information may include one or more capability parameters of the UE to receive the first AI model.

[0170] Mode A1: The first possible mode in which the access network device obtains the capability parameters of the UE to receive the first AI model.

[0171] The UE may periodically send the capability parameters of the model transmission of the AI ​​model supported by itself to the access network device. For example, the UE periodically sends the third information to the access network device. Optionally, the access network device may indicate to the UE the period of sending its own capability parameters. Thus, the UE may periodically send the capability parameters of the model transmission of the AI ​​model supported by itself to the access network device according to the period indicated by the access network device.

[0172] Method A2, a second possible method in which the access network device obtains the capability parameters of the UE to receive the first AI model.

[0173] The access network device may send the first indication information to the UE, and the first indication information is used to indicate the conditions that need to be met when the UE reports the third information. Thus, the UE can send the third information to the access network device according to the first indication information when the conditions are met, that is, send the UE's ability parameter for receiving the first AI model to the access network device.

[0174] Exemplarily, the first indication information may include one or more of the following: an identifier of a triggering event, a transmission rate variation range that triggers the UE to report the third information, a transmission rate range that triggers the UE to report the third information, a reception delay variation range that triggers the reporting of the third information, or a reception delay range that triggers the reporting of the third information.

[0175] The trigger event identifier may be the ID of the triggered event, which is used to identify the trigger event. The trigger event indicates that the UE is triggered to report the third information. The transmission rate change range satisfied by the trigger UE to report the third information may adopt the transmission rate change threshold or the transmission rate change interval. The transmission rate change threshold may indicate that the UE sends the third information to the access network device when the transmission rate change value for a period of time is greater than the rate change threshold. The transmission rate change interval may indicate that the UE sends the third information to the access network device when the transmission rate change value is within the transmission rate change interval. The transmission rate range satisfied by the trigger UE to report the third information may adopt the transmission rate threshold or the transmission rate interval. The transmission rate threshold may indicate that the UE sends the third information to the access network device when the transmission rate of the UE is greater than the transmission rate threshold. The transmission rate interval may indicate that the UE sends the third information to the access network device when the transmission rate of the UE is within the transmission rate interval. The reception delay change range satisfied by the trigger to report the third information may adopt the reception delay change threshold or the reception delay change interval. The reception delay change threshold indicates that when the change value of the UE's reception delay over a period of time is greater than the reception delay change threshold, the third information is sent to the access network device. The reception delay change interval indicates that when the change value of the UE's reception delay is within the reception delay change interval, the third information is sent to the access network device. The reception delay range that triggers reporting of the third information may adopt the reception delay threshold or the reception delay interval. When the reception delay threshold indicates that the UE's reception delay is greater than the reception delay threshold, the third information is sent to the access network device. When the reception delay of the UE in the reception delay interval is within the reception delay interval, the third information is sent to the access network device.

[0176] In a possible implementation, the access network device may also instruct the UE which capability parameters to report specifically. In one example, the access network device sends a second indication message to the UE. The second indication message is used to instruct the UE to choose to report some or all of the parameters in the third information based on the current capability. For example, the UE's current computing power is sufficient, and it is not necessary to report its own computing power parameters. For another example, the UE supports all model transmission modes, and it is not necessary to report the supported model transmission modes. For another example, if the UE's storage capacity is limited, it is possible to report its own storage capacity parameters. The UE can choose which capability parameters to report based on its own capabilities. The access network device may send the first indication message and the second indication message to the UE together, or may send them separately.

[0177] In another example, the access network device sends a third indication information to the UE, and the third indication information is used to indicate the parameters requested to be reported by the UE. In this example, the access network device can indicate which parameters the UE should report according to the requirements, and indicate the specific parameters to the UE. The access network device can send the first indication information and the third indication information to the UE together, or separately.

[0178] Method A3, the third possible method for the access network device to obtain the capability parameters of the UE to receive the first AI model.

[0179] The UE sends the first information to the access network device, and may also send the UE's ability parameter for receiving the first AI model to the access network device, that is, send the third information to the access network device. The UE may send the first information and the third information to the access network device together, or send them separately to the access network device.

[0180] (2) The information used by the access network device to determine the transmission method of the first AI model includes the capability parameters of the OTT transmission AI model.

[0181] Method B1 is the first possible method for the access network device to obtain the capability parameters of the OTT transmission AI model.

[0182] The access network device sends indication information 1 to OTT, and indication information 1 instructs OTT to report the capability parameters of the transmission AI model. Therefore, when OTT receives indication information 1, it sends the capability parameters of the OTT transmission AI model to the access network device. In some embodiments, indication information 1 may also include one or more of the following: an identifier of the first AI model, metadata of the first AI model, or a reason for the request, etc. For example, the reason for the request may be an AI use case to be executed and / or a transmission capability limitation of the access network device, etc. Exemplarily, the access network device may send indication information 1 to OTT after receiving the first information sent by the UE.

[0183] Method B2 is the second possible method for access network equipment to obtain capability parameters of the OTT transmission AI model.

[0184] The UE can obtain the OTT transmission AI model capability parameters from the OTT and then send them to the access network device. Figure 5 The figure is a flow chart of the communication method provided in the embodiment of the present application.

[0185] S501, the UE obtains capability parameters of the OTT transmission AI model from the OTT. In some possible implementations, the UE may determine the first information based on the capability parameters of the OTT transmission AI model and the AI ​​use case that the UE needs to execute.

[0186] S502-S503, see 401-S402, which will not be repeated here.

[0187] Method B3 is the third possible method for access network equipment to obtain capability parameters of the OTT transmission AI model.

[0188] The UE can obtain the capability parameters of the OTT transmission AI model from the OTT and then send them to the access network device.

[0189] In one example, the UE can periodically obtain the capability parameters of the OTT transmission AI model, and then periodically send the capability parameters of the UE receiving the AI ​​model to the access network device. For example, the UE periodically sends the third information to the access network device, and the third information includes not only the first sub-information mentioned above, but also the second sub-information, and the second sub-information indicates the capability parameters of the OTT transmission AI model.

[0190] In another example, the access network device may indicate the conditions for the UE to report the third information, for example, the access network device sends first indication information to the UE, and the first indication information is used to indicate the conditions that the UE needs to meet to report the third information. Thus, the UE may send the third information to the access network device when the conditions are met according to the first indication information.

[0191] See also Figure 6 The figure is a flow chart of the communication method provided in the embodiment of the present application. Figure 6 Describes the process of exchanging capability parameters between access network equipment and UE.

[0192] S601, the access network device sends fourth information to the UE, the fourth information may include the first indication information, or the fourth information includes the first indication information and the second indication information, or the fourth information includes the first indication information and the third indication information. The description of the first indication information, the second indication information and the third indication information is as described above, and will not be repeated here.

[0193] S602, the UE obtains capability parameters of the OTT transmission AI model from the OTT.

[0194] S603, the UE sends a third message to the access network device. The third message includes the first sub-information and / or the second sub-information. For example, when the fourth message includes the second indication message, the second indication message indicates which parameters in the capability parameters the UE selects to report based on the current capability. The specific capability parameters include which parameters may be specified by the protocol or may be pre-configured by the access network device. In some scenarios, the UE may not report its own capability parameters, but may only report the capability parameters of the OTT transmission AI model. In other scenarios, the UE may also report its own capability parameters instead of the capability parameters of the OTT transmission model. In some other scenarios, the UE may also report both its own capability parameters and the capability parameters of the OTT transmission AI model. For another example, the fourth message includes the third indication message, and the third indication message indicates the parameters requested to be reported by the UE, such as a parameter identifier. The parameters requested to be reported by the UE may include only the capability parameters of the UE, or may include only the capability parameters of the OTT transmission AI model, or may include the capability parameters of the UE and the capability parameters of the OTT transmission AI model.

[0195] (2) The information used by the access network device to determine the transmission method of the first AI model includes the capability parameters of the core network device to transmit the AI ​​model.

[0196] Method C1 is the first possible method for an access network device to obtain capability parameters of a core network device for transmitting an AI model.

[0197] The access network device can instruct the core network device to periodically send the capability parameters of the core network transmission AI model. For example, the access network device can indicate the reporting period to the core network device, so that the core network device periodically sends the capability parameters of the core network transmission AI model to the access network device according to the reporting period.

[0198] Mode C2 is the second possible way for the access network device to obtain the capability parameters of the core network device for transmitting the AI ​​model. The access network device may also send reporting conditions to the core network device. For example, the reporting condition may be that the transmission capability of the core network device has changed. For another example, the reporting condition may be that the range of change of a capability parameter of the core network device exceeds a threshold or the current value of a capability parameter reaches a set threshold. The core network device sends the capability parameters of the core network device for transmitting the AI ​​model to the access network device according to the reporting conditions.

[0199] Mode C3, the third possible way for the access network device to obtain the capability parameters of the core network device for transmitting the AI ​​model. The access network device may also request the core network device to transmit the capability parameters of the AI ​​model after receiving the first information sent by the UE. For example, after receiving the first information sent by the UE, the access network device sends a request message to the core network device, and the request message is used to request the core network device to report the capability parameters of the AI ​​model. Thus, after receiving the request message, the core network device sends the capability parameters of the AI ​​model to the access network device. The request information may include one or more of the identifier of the AI ​​model, the metadata of the AI ​​model, or the reason for the request.

[0200] In some possible implementations, the method of transmitting AI models based on the user plane (UP) plane can build a PDU session through the core network device and then transmit it based on the UP plane, so the access network device can obtain the model transmission capability information of the core network device (for example, AMF) and confirm that some models can be transmitted by the user plane, which can include some models sent by the access network device locally directly based on user plane data (which can be understood as the DRB directly established by the gNB to transmit the AI ​​model), and some models that need to be executed by the core network device to send user plane data (which can be understood as the relevant DRB in the PDU session established by the core network device to transmit the model).

[0201] In a possible implementation, the access network device may also send the access network device to the UE a capability parameter for transmitting an AI model. Exemplarily, the access network device may send the access network device to the UE a capability parameter for transmitting an AI model in a broadcast / unicast manner. The capability parameter of the access network device for transmitting an AI model may be used by the UE to determine its own transmission requirements for the first AI model to be applied. In some application scenarios, the UE may determine the UE's transmission requirements for the first AI model to be applied based on the capability parameter of the access network device for transmitting the AI ​​model. In other application scenarios, the UE may determine the UE's transmission requirements for the first AI model to be applied based on the capability parameter of the access network device for transmitting the AI ​​model and the capability parameter of the OTT transmission AI model (and the AI ​​use case to be executed). For example, the capability parameter of the access network device for transmitting the AI ​​model may include one or more of the following: an identifier of an AI model supported by the access network device, metadata of an AI model supported by the access network device, whether the access network device supports dual connectivity mode (dual connectivity, DC) transmission, or information such as the model transmission capability of a neighboring access network device.

[0202] In some embodiments, after the access network device obtains the information used to determine the transmission mode of the AI ​​model for the UE, it can also send this information to the neighboring access network device. For example, when the UE switches from the access network device to the neighboring access network device, the access network device can carry this information in the switching request message. For another example, the access network device can periodically send information used to determine the transmission mode of the AI ​​model for the UE to the neighboring access network device. For another example, the access network device can send information used to determine the transmission mode of the AI ​​model for the UE to the neighboring access network device when the sending conditions are met. For example, the sending condition may be that the information used to determine the transmission mode of the AI ​​model for the UE changes. The neighboring access network device receives the information used to determine the transmission mode of the AI ​​model for the UE, and can also determine the transmission mode of the AI ​​model for the UE according to demand.

[0203] The communication method provided by the embodiment of the present application is described below in combination with the application scenario. Figure 7 The figure is a flow chart of a communication method provided in an embodiment of the present application. Figure 7 The information used to determine the transmission mode of the AI ​​model for the UE includes the transmission requirements of the UE for the first AI model to be applied, the capability parameters of the UE to receive the AI ​​model, the capability parameters of the OTT to transmit the AI ​​model, and the capability parameters of the core network device to transmit the AI ​​model. It should be noted that Figure 7 The way in which the access network device obtains the capability parameters of the UE receiving AI model, the capability parameters of the OTT transmitting AI model, and the capability parameters of the core network device transmitting the AI ​​model is only an example. Any of the above-described methods can be adopted, and this application is not limited.

[0204] S701, the UE obtains capability parameters of the OTT transmission AI model from the OTT. The UE may determine the first information based on the capability parameters of the OTT transmission AI model and the AI ​​use case to be executed by the UE.

[0205] S702, see S401, which will not be described in detail here.

[0206] S703, optionally, the UE sends the UE's capability information of receiving the first AI model and the capability information of the OTT transmission AI model to the access network device.

[0207] S704, optionally, the access network device obtains capability information of the core network device for transmitting the AI ​​model from the core network device.

[0208] S705: The access network device determines a transmission method for the first AI model.

[0209] In one example, the transmission mode of the first AI model includes the transmission mode of the access network device. S706 is executed, and the access network device sends the model transmission information corresponding to the first AI model to the UE. For example, the model transmission information may be included in the second information. The model transmission information may include model information of the first AI model; or, the model transmission information includes model information, and the model transmission information also includes the transmission mode and / or estimated completion time adopted by the access network device. The transmission mode adopted by the access network device includes adopting a user plane signaling mode or a control plane signaling mode. Exemplarily, the model information includes one or more of the following: an identifier of the first AI model; metadata of the first AI model; or, fourth indication information, wherein the fourth indication information is used to indicate the structure of the first AI model. Further, the access network device completes the transmission of part or all of the structure of the first AI model.

[0210] Optionally, the access network device may no longer send model transmission information corresponding to the first AI model to the UE, and directly complete the transmission of part or all of the structure of the first AI model to the UE.

[0211] In another example, the transmission mode of the first AI model includes an OTT transmission mode. Then S707 is executed, and the access network device sends model transmission information of the first AI model transmitted by OTT to the UE. For example, the model transmission information of the first AI model transmitted by OTT can be included in the second information.

[0212] S708, the UE forwards the model transmission information of the first AI model transmitted by OTT to OTT. The model transmission information of the first AI model transmitted by OTT includes the model information of the first AI model; or, the model transmission information includes the model information, and the model transmission information also includes an estimated completion time. Further, OTT completes the transmission of part or all of the structure of the first AI model.

[0213] In another example, the transmission mode of the first AI model includes a core network device transmission mode. S709 is executed, and the access network device sends model transmission information of the core network device transmitting the first AI model to the core network device. Further, the core network device can complete the transmission of part or all of the structure of the first AI model according to the model transmission information of the first AI model. The model transmission information of the core network device transmitting the first AI model includes the model information of the first AI model; or, the model transmission information of the core network device transmitting the first AI model includes the model information, and the model transmission information also includes the transmission mode and / or the estimated completion time adopted by the core network device. The transmission mode adopted by the core network device can be NAS signaling, LPP signaling or UP data. Further, the core network device completes the transmission of part or all of the structure of the first AI model.

[0214] Illustratively, the estimated completion time mentioned above may be one or more of a time point, a time period, or a time mark.

[0215] Exemplarily, the model information mentioned above includes one or more of the following: an identifier of the first AI model; metadata of the first AI model; or fourth indication information, where the fourth indication information is used to indicate the structure of the first AI model.

[0216] The fourth indication information may adopt one or more of the following indication methods: model segmentation indication, bit indication, structure indication or parameter indication. In some scenarios, the UE and the access network device need to cooperate to complete a certain function. The first AI model may be a sub-model of an AI model configured on the UE and the access network device. Alternatively, multiple models are trained on the access network device, the core network device or the OTT, and the first AI model is only one of the multiple models trained by the access network device, the core network device or the OTT.

[0217] It should be noted that the transmission mode of the first AI model determined by the access network device may include only one or multiple transmission modes. In the case of multiple transmission modes, the transmission of the first AI model may be completed by multiple devices together.

[0218] Model segmentation indication, indicating the segment to which the part transmitted by the device belongs in the first AI model, for example, the first AI model is divided into two segments, and the part transmitted by the device is the first 50% of the first AI model. Bit indication, indicating the position of the part transmitted by the device in the first AI model through the bit. Structure indication, indicating that the first AI model is packaged and sent directly. Parameter indication, which can indicate the specific structure of the model included in the first AI model (for example, the number of layers included in the first AI model, the number of neurons in each layer, or the identifier of the sub-model included in the first AI model) and the network parameters used in each layer (which can be network parameters that are not 0 or network parameters that need to be updated, and compressed transmission can be used).

[0219] S710, at least one of the core network device, the access network device or the OTT device transmits the first AI model to the UE according to the received model transmission information.

[0220] Through the solution provided in the embodiment of the present application, the UE can determine the transmission requirements of the AI ​​model, so that the access network device determines which transmission mode to use for the UE and indicates the adopted transmission mode to the UE. The transmission mode determined for the UE is adapted to the transmission requirements of the UE to avoid the problem of mismatch between the transmission requirements and the transmission mode.

[0221] The above description is from the perspective of the UE sending the model transmission requirement to the access network device. Next, another communication method provided by an embodiment of the present application is introduced. In this method, the access network device determines the transmission requirement of the first AI model and sends it to the UE. The access network device determines the transmission mode of the first AI model for the UE and indicates the adopted transmission mode to the UE. Figure 8 As shown, it is a flow chart of the method. For example, the first communication device in the method is Figure 3 The first communication device in the method is the second communication device Figure 3 The second communication device in the UE. The first communication device may be a UE, or a chip or chip system in the UE, or a functional module in the UE. In the subsequent description, the first communication device is taken as an example of a UE. The second communication device is, for example, an access network device; or, the second communication device is a chip or chip system in the access network device, or the second communication device is a functional module included in the access network device, such as a CU, or CU-CP, or CU-CP2, etc.; or, the second communication device is a larger device that includes an access network device. In the subsequent description, the second communication device is taken as an access network device as an example.

[0222] S801, the access network device sends fifth information to the UE. Correspondingly, the UE receives the fifth information from the access network device.

[0223] The fifth information is used to indicate the transmission requirement of the first AI model, where the first AI model is the AI ​​model requested to be configured by the UE.

[0224] S802, the UE sends third information to the access network device. Correspondingly, the access network device receives the third information from the UE.

[0225] The third information is the first sub-information, the first sub-information indicates the capability parameter of the UE to receive the first AI model, and the third information is used to determine the transmission mode of the first AI model. The relevant description of the third information is as described above and will not be repeated here.

[0226] In some embodiments, the access network device may also adopt the above-mentioned A1 or A2 method to obtain the UE's ability parameter to receive the AI ​​model. Then the UE may not execute S802 here. Exemplarily, in this case, the UE may send a reception confirmation or a transmission requirement confirmation indication to the access network device to confirm that the transmission requirement has been received, or to indicate confirmation of the transmission requirement.

[0227] In a possible implementation, the information used to determine the transmission mode of the first AI model may also include one or more of the following: capability parameters of the OTT transmission AI model or capability parameters of the core network device transmission AI model. The relevant content about the capability parameters of the OTT transmission AI model or the capability parameters of the core network device transmission AI model is as mentioned above and will not be repeated here.

[0228] In one example, when the information used to determine the transmission mode of the first AI model includes the capability parameters of the OTT transmission AI model, after the UE receives the fifth information, S804 can be executed before S802 to obtain the capability parameters of the OTT transmission AI model from OTT. The third information also includes the second sub-information, and the second sub-information is used to indicate that OTT supports the capability parameters of the AI ​​model provided to the UE. That is, the UE sends its own capability parameters and the capability parameters of the OTT transmission AI model to the access network device. In some embodiments, the access network device may also adopt the above-mentioned method B1 to obtain the capability parameters of the OTT transmission AI model. Exemplarily, the access network device may send indication information 1 to OTT after sending the fifth information to the UE. Indication information 1 indicates that OTT reports the capability parameters of the transmission AI model. Thus, when OTT receives indication information 1, it sends the capability parameters of the OTT transmission AI model to the access network device.

[0229] In another example, when the information used to determine the transmission of the first AI model includes the capability parameters of the core network device transmitting the AI ​​model, the access network device may also execute S805 to send a request message to the core network device, wherein the request message is used to request the core network device to report the capability parameters of the transmission AI model. Thus, after receiving the request message, the core network device executes S806 to send the fourth information to the access network device. The fourth information is used to indicate that the core network device supports the capability parameters of the third AI model provided to the UE, and the third AI model includes the first AI model. Optionally, the request information may include one or more of the following: an identifier of the AI ​​model, metadata of the AI ​​model, or a reason for the request. In some embodiments, the access network device may also adopt the above-mentioned method C1 and method C2 to obtain the capability parameters of the core network device transmitting the AI ​​model. S805 is located after S801 and before S803. The order of S805 and S802 is not specifically limited in this application.

[0230] S803, the access network device sends second information to the UE, where the second information is used to indicate the transmission mode of the first AI model. The relevant description of the second information is as described above and will not be repeated here. For example, the access network device can send the second information to the UE after determining the transmission mode of the first AI model.

[0231] S807-S811, refer to S706-S710, which will not be repeated here.

[0232] Fig. 9 A schematic diagram of the structure of a communication device provided in an embodiment of the present application is given. The communication device 900 may be Figure 4-Figure 8 The first communication device (or UE) or the circuit system of the first communication device described in any one of the embodiments shown in the figures is used to implement the method corresponding to the first communication device (UE) in the above method embodiment. Alternatively, the communication device 900 may be Figure 4-Figure 8 The second communication device or the circuit system of the second communication device (or access network device) of any of the embodiments shown is used to implement the method corresponding to the access network device in the above method embodiment. For specific functions, please refer to the description in the above method embodiment. Among them, for example, a circuit system is a chip system.

[0233] The communication device 900 includes at least one processor 901. The processor 901 can be used for internal processing of the device to implement certain control processing functions. Optionally, the processor 901 includes instructions. Optionally, the processor 901 can store data. Optionally, different processors can be independent devices, can be located in different physical locations, and can be located on different integrated circuits. Optionally, different processors can be integrated into one or more processors, for example, integrated on one or more integrated circuits.

[0234] Optionally, the communication device 900 includes one or more memories 903 for storing instructions. Optionally, data may also be stored in the memory 903. The processor and the memory may be provided separately or integrated together.

[0235] Optionally, the communication device 900 includes a communication line 902 and at least one communication interface 904. Since the memory 903, the communication line 902 and the communication interface 904 are all optional, Fig. 9 Indicated by dotted lines.

[0236] Optionally, the communication device 900 may further include a transceiver and / or an antenna. The transceiver may be used to send information to other devices or receive information from other devices. The transceiver may be referred to as a transceiver, a transceiver circuit, an input / output interface, etc., and is used to implement the transceiver function of the communication device 900 through an antenna. Optionally, the transceiver includes a transmitter and a receiver. Exemplarily, the transmitter may be used to generate a radio frequency signal from a baseband signal, and the receiver may be used to convert the radio frequency signal into a baseband signal.

[0237] The processor 901 may include a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.

[0238] The communication link 902 may include a pathway for transmitting information between the above-mentioned components.

[0239] The communication interface 904 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), wired access networks, etc.

[0240] The memory 903 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 903 may exist independently and be connected to the processor 901 via the communication line 902. Alternatively, the memory 903 may also be integrated with the processor 901.

[0241] The memory 903 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 901. The processor 901 is used to execute the computer-executable instructions stored in the memory 903, thereby realizing Figure 4-Figure 8 The steps performed by the first communication device (or UE) in any one of the embodiments shown in the embodiment, or, implementing Figure 4-Figure 8 The steps performed by the second communication device (or access network equipment) described in any one of the embodiments shown.

[0242] Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application code, which is not specifically limited in the embodiments of the present application.

[0243] In a specific implementation, as an embodiment, the processor 901 may include one or more CPUs, such as Fig. 9 CPU0 and CPU1 in.

[0244] In a specific implementation, as an embodiment, the communication device 900 may include multiple processors, such as Fig. 9 901 and processor 905 in the embodiment of the present invention. Each of these processors may be a single-CPU processor or a multi-CPU processor. The processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0245] when Fig. 9 When the device shown is a chip, for example, a chip of a first communication device, or a chip of a UE, the chip includes a processor 901 (may also include a processor 905), a communication line 902 and a communication interface 904, and optionally, the chip may include a memory 903. Specifically, the communication interface 904 may be an input interface, a pin or a circuit, etc. The memory 903 may be a register, a cache, etc. The processor 901 and the processor 905 may be a general-purpose CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of a program of the communication method of any of the above embodiments.

[0246] The embodiment of the present application can divide the functional modules of the device according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation. For example, in the case of dividing each functional module according to each function, Fig.10 A schematic diagram of a device is shown, and the device 1000 may be the first communication device (or UE) or the second communication device (or access network device) involved in the above-mentioned various method embodiments, or a chip in the first communication device (or UE) or a chip in the second communication device (or access network device). The device 1000 includes a sending unit 1001, a processing unit 1002, and a receiving unit 1003.

[0247] It should be understood that the apparatus 1000 can be used to implement the steps performed by the access network device or UE in the communication method of the embodiment of the present application, and the relevant features can refer to the above Figure 4-Figure 8 Any of the embodiments shown will not be described in detail here.

[0248] Optional, Fig.10 The functions / implementation processes of the sending unit 1001, the receiving unit 1003 and the processing unit 1002 can be Fig. 9 The processor 901 in the embodiment calls the computer execution instruction stored in the memory 903 to implement. Or, Fig.10 The function / implementation process of the processing unit 1002 in Fig. 9 The processor 901 in the embodiment calls the computer execution instruction stored in the memory 903 to implement, Fig.10 The functions / implementation processes of the sending unit 1001 and the receiving unit 1003 can be Fig. 9 It is implemented by the communication interface 904 in.

[0249] Optionally, when the device 1000 is a chip or a circuit, the functions / implementation processes of the sending unit 1001 and the receiving unit 1003 can also be implemented through pins or circuits.

[0250] The present application also provides a computer-readable storage medium, which stores a computer program or instruction. When the computer program or instruction is executed, the method performed by the first communication device or the second communication device in the aforementioned method embodiment is implemented. In this way, the functions described in the above embodiments can be implemented in the form of software functional units and sold or used as independent products. Based on this understanding, the technical solution of the present application can be essentially or in other words, the part that contributes or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0251] The present application also provides a computer program product, which includes: a computer program code, when the computer program code is executed on a computer, the computer executes the method executed by the first communication device or UE in any of the aforementioned method embodiments.

[0252] An embodiment of the present application also provides a processing device, including a processor and an interface; the processor is used to execute the method executed by the first communication device or UE involved in any of the above method embodiments.

[0253] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0254] The various illustrative logic units and circuits described in the embodiments of the present application can be implemented or operated by a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or any combination of the above. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any traditional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0255] The steps of the method or algorithm described in the embodiments of the present application can be directly embedded in the hardware, the software unit executed by the processor, or the combination of the two. The software unit can be stored in RAM, flash memory, ROM, erasable programmable read-only memory (erasable programmable read-only memory, EPROM), EEPROM, register, hard disk, removable disk, CD-ROM or other storage media of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a terminal device. Optionally, the processor and the storage medium can also be arranged in different components in the terminal device.

[0256] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0257] The contents of the various embodiments of the present application may refer to each other. If there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0258] It is understandable that in the embodiment of the present application, the first communication device or the second communication device can perform some or all of the steps in the embodiment of the present application, and these steps or operations are only examples. In the embodiment of the present application, other operations or variations of various operations can also be performed. In addition, the various steps can be performed in different orders presented in the embodiment of the present application, and it is possible that not all operations in the embodiment of the present application need to be performed.

[0259] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A communication method, characterized in that: include: Receiving first information from a first communication device, the first information being used to indicate a transmission requirement of the first communication device for a first artificial intelligence (AI) model to be applied; the first information being used to determine a transmission method of the first AI model; Sending second information to the first communication device, where the second information is used to indicate a transmission method of the first AI model.

2. The method according to claim 1, characterized in that The transmission method of the first AI model includes one or more of the following: An access network device transmission mode, where the access network device transmission mode indicates that the first AI model is transmitted from the access network device to the first communication device; OTT transmission mode, where the OTT transmission mode indicates that the first AI model is transmitted to the first communication device by OTT; or A core network device transmission mode, wherein the core network device transmission mode indicates that the first AI model is transmitted from the core network device to the first communication device.

3. The method according to claim 1 or 2, characterized in that The first information indicates one or more of the following: Desired time; an identifier of the first AI model; metadata of the first AI model; a first AI function supported by the first AI model; Expected minimum transmission delay; a second AI function to be executed after completing the first AI function; or, The moving path of the first communication device.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: Receive third information from a first communication device, the third information including first sub-information, the first sub-information indicating an ability parameter of the first communication device to receive the first AI model, and the third information is used to determine a transmission method of the first AI model.

5. The method according to claim 4, characterized in that The first sub-information includes one or more of the following: a storage capacity parameter of the first communication device; a computing capability parameter of the first communication device; a communication capability parameter of the first communication device; a transmission rate allowed by the first communication device; the arrival time of the first AI model expected by the first communication device; or, A model transmission mode supported by the first communication device.

6. The method according to claim 4, characterized in that The third information also includes second sub-information, where the second sub-information indicates that the third-party application service OTT supports capability parameters of a second AI model provided to the first communication device, where the second AI model includes the first AI model.

7. The method according to claim 6, characterized in that The second sub-information includes one or more of the following: an identifier of the second AI model; The identifier of the OTT; metadata of the second AI model; The OTT supports a transmission rate for transmitting the second AI model; The OTT supports a transmission delay of the second AI model; The OTT supports the arrival time of transmitting the second AI model; or, The model transmission method supported by the OTT.

8. The method according to any one of claims 4 to 7, characterized in that: The method further comprises: Receive fourth information from a core network device, where the fourth information is used to indicate that the core network device supports providing capability parameters of a third AI model for the first communication device, where the third AI model includes the first AI model.

9. The method according to claim 8, characterized in that The fourth information includes one or more of the following: an identifier of the third AI model; Metadata of the third AI model; The core network device supports a transmission rate for transmitting the third AI model; The core network device supports a transmission delay for transmitting the third AI model; The core network device supports the arrival time of transmitting the third AI model; or, The model transmission mode supported by the core network device.

10. The method according to any one of claims 2 to 9, characterized in that: The method further comprises: Transmitting model transmission information corresponding to the first AI model to the first communication device; The model transmission information includes model information of the first AI model; or, The model transmission information includes the model information, and the model transmission information also includes the adopted transmission method and / or the estimated completion time; The adopted transmission mode includes adopting a user plane signaling mode or a control plane signaling mode.

11. The method according to claim 10, characterized in that The model information includes one or more of the following: an identifier of the first AI model; metadata of the first AI model; or Fourth indication information, where the fourth indication information is used to indicate the structure of the first AI model.

12. A communication method, characterized in that: include: Sending first information to the second communication device, where the first information is used to indicate the transmission requirement of the first communication device for the first artificial intelligence AI model to be applied; The first information is used to determine a transmission method of the first AI model; Receive second information from the second communication device, where the second information is used to indicate a transmission method of the first AI model.

13. The method according to claim 12, characterized in that The transmission method of the first AI model includes one or more of the following: An access network device transmission mode, where the access network device transmission mode indicates that the first AI model is transmitted from the access network device to the first communication device; OTT transmission mode, where the OTT transmission mode indicates that the first AI model is transmitted to the first communication device by OTT; or A core network device transmission mode, wherein the core network device transmission mode indicates that the first AI model is transmitted from the core network device to the first communication device.

14. The method according to claim 12 or 13, characterized in that The first information indicates one or more of the following: Desired time; an identifier of the first AI model; metadata of the first AI model; a first AI function supported by the first AI model; Expected minimum transmission delay; a second AI function to be executed after completing the first AI function; or, The moving path of the first communication device.

15. The method according to any one of claims 12 to 14, characterized in that: The method further comprises: Send third information to the second communication device, the third information including first sub-information, the first sub-information indicating the ability parameter of the first communication device to receive the first AI model, and the third information is used to determine the transmission method of the first AI model.

16. The method according to claim 15, characterized in that The first sub-information includes one or more of the following: a storage capacity parameter of the first communication device; a computing capability parameter of the first communication device; a communication capability parameter of the first communication device; a transmission rate allowed by the first communication device; an arrival time of the first AI model expected by the first communication device; A model transmission mode supported by the first communication device.

17. The method according to claim 15, characterized in that The third information also includes second sub-information, where the second sub-information indicates that the third-party application service OTT supports capability parameters of a second AI model provided to the first communication device, where the second AI model includes the first AI model.

18. The method according to claim 17, characterized in that: The second sub-information includes one or more of the following: an identifier of the second AI model; The identifier of the OTT; metadata of the second AI model; The OTT supports a transmission rate for transmitting the second AI model; The OTT supports a transmission delay of the second AI model; The OTT supports the arrival time of transmitting the second AI model; or, The model transmission method supported by the OTT.

19. The method according to any one of claims 13 to 18, characterized in that: The method further comprises: receiving model transmission information corresponding to the first AI model from the second communication device; The model transmission information includes model information of the first AI model; or, The model transmission information includes the model information, and the model transmission information also includes a transmission method and / or an estimated completion time adopted by the first communication device; The transmission method adopted by the first communication device includes a user plane signaling method or a control plane signaling method.

20. The method of claim 19, wherein: The model information includes one or more of the following: an identifier of the first AI model; metadata of the first AI model; or Fourth indication information, where the fourth indication information is used to indicate the structure of the first AI model.

21. A communication device, characterized in that: The method comprises one or more modules or units for implementing the method steps described in any one of claims 1 to 20.

22. A communication device, characterized in that: The method comprises a processor and a memory, wherein the memory is coupled to the processor, and the processor is used to execute the method according to any one of claims 1 to 11, or to execute the method according to any one of claims 12 to 20.

23. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program. When the computer program runs on a computer, the computer executes the method according to any one of claims 1 to 11, or executes the method according to any one of claims 12 to 20.

24. A chip system, characterized in that: The chip system comprises: A processor and an interface, wherein the processor is used to call and run instructions from the interface, and when the processor executes the instructions, the method according to any one of claims 1 to 11 is implemented; or the method according to any one of claims 12 to 20 is implemented.

25. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is run on a computer, the computer is enabled to execute the method according to any one of claims 1 to 11, or execute the method according to any one of claims 12 to 20.

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