Model processing method and apparatus

Abstract

Provided in the present application are a model processing method and an apparatus. The method comprises: sending first indication information, the first indication information being used for indicating a first association identifier corresponding to first intelligent model information, and the first intelligent model information being used for indicating at least one type of configuration information related to a first intelligent model; and receiving second indication information, the second indication information being used for indicating the first association identifier and part or all of the at least one type of configuration information under the first association identifier. The present application allows the terminal device and the network device to have a unified understanding of the configuration information related to a model and the association identifier corresponding to the model, thereby ensuring the consistency of understanding between both parties.

Description

A model processing method and apparatus

[0001] This application claims priority to Chinese Patent Application No. 202411948567.1, filed on December 25, 2024, entitled “A Model Processing Method and Apparatus”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of artificial intelligence technology, and in particular to a model processing method and apparatus. Background Technology

[0003] Currently, with the development and application of artificial intelligence (AI) technology, AI models have shown broad application prospects in many fields, covering industrial automation, gaming, agriculture, energy, environmental protection, and many others. They have also been widely used in the field of wireless communication, achieving superior performance. However, when AI models are actually deployed for inference, terminal devices and network devices do not have a unified understanding of the model-related configuration information and the corresponding association identifiers. Summary of the Invention

[0004] This application proposes a model processing method and apparatus that enables terminal devices and network devices to have a unified understanding of model-related configuration information and model-corresponding association identifiers, ensuring consistency in understanding between the two parties.

[0005] In a first aspect, embodiments of this application provide a model processing method, which can be applied to a terminal-side device. The terminal-side device can be a terminal device, a component in the terminal device (e.g., a processor, chip, circuit, or chip system), or a logic module or software capable of implementing all or part of the functions of the terminal device. The method includes: sending first indication information, wherein the first indication information is used to indicate a first association identifier corresponding to first intelligent model information, and the first intelligent model information is used to indicate at least one configuration information related to the first intelligent model; and receiving second indication information, wherein the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier.

[0006] In the above method, by sending the first association identifier corresponding to the first intelligent model information, that is, the first association identifier corresponding to at least one configuration information related to the first intelligent model, the terminal device and the network device have a unified understanding of the mapping relationship between at least one configuration information related to the first intelligent model and the first association identifier, ensuring consistency in their understanding. Furthermore, by receiving the second indication information from the network device, the terminal device can perform subsequent processing based on the second indication information. For example, it can determine the first intelligent model based on the first association identifier, and perform model inference based on some or all of the configuration information under the first intelligent model and the first association identifier to obtain the inference result. When the inference result is used in beam management, it can reduce the overhead of beam scanning.

[0007] In one possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority.

[0008] In the above method, the terminal device sends a first indication information to the network device. The reporting order of the first association identifier and the second association identifier in the first indication information implicitly indicates the priority order of the first association identifier and the second association identifier, and the reporting order of the first intelligent model information and the second intelligent model information implicitly indicates the priority order of the first intelligent model information and the second intelligent model information. This can reduce signaling overhead. In addition, it can enable the terminal device and the network device to have a unified understanding of the priority order of the first association identifier and the second association identifier and / or the priority order of the first intelligent model information and the second intelligent model information, ensuring the consistency of understanding between the two parties.

[0009] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the method further includes: sending third indication information, the third indication information being used to indicate the priority order of the first association identifier and the second association identifier, and / or the priority order of the first intelligent model information and the second intelligent model information.

[0010] In the above method, by sending third instruction information from the terminal device to the network device, the terminal device and the network device can have a unified understanding of the priority order of the first association identifier and the second association identifier and / or the priority order of the first intelligent model information and the second intelligent model information, thus ensuring the consistency of understanding between the two parties.

[0011] In another possible implementation, at least one configuration information related to the first intelligent model is sorted by priority.

[0012] In the above method, the priority order of at least one configuration information related to the first intelligent model is implicitly indicated by the reporting order of at least one configuration information related to the first intelligent model, which can reduce signaling overhead.

[0013] In another possible implementation, the method further includes: sending fourth indication information, the fourth indication information being used to notify updates of at least one of the following information, the at least one of the following information including: a first priority sort, a second priority sort, or a third priority sort, wherein the first priority sort is the priority sort of the first association identifier and the second association identifier, the second priority sort is the priority sort of the first intelligent model information and the second intelligent model information, and the third priority sort is the priority sort of at least one configuration information related to the first intelligent model.

[0014] In the above method, by sending a fourth instruction information from the terminal device to the network device, the network device can be notified in a timely manner when at least one of the above information changes, thereby ensuring the accuracy of the above at least one information.

[0015] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the second indication information is further used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier. The method further includes: determining the first inference result of the first intelligent model and the second inference result of the second intelligent model based on the second indication information; and sending the first inference result and / or the second inference result according to a first priority order, wherein the first priority order is the priority order of the first association identifier and the second association identifier.

[0016] In the above method, by sending the first inference result and / or the second inference result according to the first priority order, the problem of the terminal device being unable to process the resource contention between the first and second inference results can be solved. For example, the first priority order is that the priority of the first association identifier is higher than the priority of the second association identifier. When resources are sufficient, the terminal device can report the first inference result and the second inference result in order of priority of the first association identifier being higher than the priority of the second association identifier, thereby making the reporting more orderly. When resources are insufficient, the terminal device can report the first inference result in order of priority of the first association identifier being higher than the priority of the second association identifier, and discard the second inference result.

[0017] In another possible implementation, the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier. The method further includes: determining a third inference result corresponding to the first configuration information under the first intelligent model and a fourth inference result corresponding to the second configuration information under the first intelligent model based on the second indication information; sending the third inference result and / or the fourth inference result according to a third priority order, wherein the third priority order is the priority order of at least one configuration information related to the first intelligent model.

[0018] In the above method, by sending the third inference result and / or the fourth inference result according to the third priority order, the problem of the terminal device being unable to process the resource contention between the third and fourth inference results can be solved. For example, the third priority order is that the priority of the first configuration information is higher than the priority of the second configuration information. When resources are sufficient, the terminal device can report the third inference result and the fourth inference result in sequence according to the priority of the first configuration information being higher than the priority of the second configuration information, thereby making the reporting more orderly. When resources are insufficient, the terminal device can report the third inference result according to the priority of the first configuration information being higher than the priority of the second configuration information, and discard the fourth inference result.

[0019] In another possible implementation, the method further includes: determining whether the first intelligent model is available based on the second indication information; if available, performing model inference based on the first intelligent model to determine a first inference result; if unavailable, sending a fifth indication information, the fifth indication information being used to notify that the first intelligent model is unavailable, or that the configuration information indicated by the second indication information is unavailable in some or all of the configuration information.

[0020] In the above method, by determining the first inference result when the first intelligent model is available, the overhead of beam scanning can be reduced when the first inference result is used in beam management; by sending the fifth indication information when the first intelligent model is unavailable, the network device can be notified in a timely manner, so that the network device can perform the next step of processing in a timely manner.

[0021] Secondly, embodiments of this application provide a model processing method, which can be applied to a network-side device. The network-side device can be a network device, a component within the network device (e.g., a processor, chip, circuit, or chip system), or a logic module or software capable of implementing all or part of the functions of the network device. The method includes: receiving first indication information, wherein the first indication information is used to indicate a first association identifier corresponding to first intelligent model information, and the first intelligent model information is used to indicate at least one configuration information related to the first intelligent model; and sending second indication information, wherein the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier.

[0022] In the above method, by sending the first association identifier corresponding to the first intelligent model information, that is, the first association identifier corresponding to at least one configuration information related to the first intelligent model, the terminal device and the network device have a unified understanding of the mapping relationship between at least one configuration information related to the first intelligent model and the first association identifier, ensuring consistency in their understanding. Furthermore, by sending the second instruction information to the terminal device, it is beneficial for the terminal device to perform subsequent processing based on the second instruction information.

[0023] In one possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority.

[0024] In the above method, the network device receives first indication information from the terminal device. The reporting order of the first association identifier and the second association identifier in the first indication information implicitly indicates the priority order of the first association identifier and the second association identifier, and the reporting order of the first intelligent model information and the second intelligent model information implicitly indicates the priority order of the first intelligent model information and the second intelligent model information. This can reduce signaling overhead. In addition, it can enable the terminal device and the network device to have a unified understanding of the priority order of the first association identifier and the second association identifier and / or the priority order of the first intelligent model information and the second intelligent model information, ensuring the consistency of understanding between the two parties.

[0025] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the method further includes: receiving third indication information, the third indication information being used to indicate the priority order of the first association identifier and the second association identifier, and / or the priority order of the first intelligent model information and the second intelligent model information.

[0026] In the above method, by receiving third instruction information from the terminal device through the network device, the terminal device and the network device can have a unified understanding of the priority order of the first association identifier and the second association identifier and / or the priority order of the first intelligent model information and the second intelligent model information, thus ensuring the consistency of understanding between the two parties.

[0027] In another possible implementation, at least one configuration information related to the first intelligent model is sorted by priority.

[0028] In the above method, the priority order of at least one configuration information related to the first intelligent model is implicitly indicated by the reporting order of at least one configuration information related to the first intelligent model, which can reduce signaling overhead.

[0029] In another possible implementation, the method further includes: receiving fourth indication information, the fourth indication information being used to notify updates of at least one of the following information, the at least one of the following information including: a first priority sort, a second priority sort, or a third priority sort, wherein the first priority sort is a priority sort of the first association identifier and the second association identifier, the second priority sort is a priority sort of the first intelligent model information and the second intelligent model information, and the third priority sort is a priority sort of at least one configuration information related to the first intelligent model.

[0030] In the above method, by receiving the fourth indication information from the terminal device through the network device, it is possible to promptly determine that at least one of the above information has changed, thereby ensuring the accuracy of at least one of the above information.

[0031] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the second indication information is further used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier. The method further includes: receiving a first inference result and / or a second inference result, wherein the first inference result is the inference result of the first intelligent model, and the second inference result is the inference result of the second intelligent model.

[0032] In another possible implementation, the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier, and the method further includes: receiving a third inference result and / or a fourth inference result, wherein the third inference result is the inference result corresponding to the first configuration information under the first intelligent model, and the fourth inference result is the inference result corresponding to the second configuration information under the first intelligent model.

[0033] In another possible implementation, the method further includes: receiving fifth indication information, the fifth indication information being used to notify that the first intelligent model is unavailable, or that the configuration information indicated by the second indication information is unavailable in some or all of the configuration information.

[0034] In the above method, it is possible to promptly understand that the first intelligent model is unavailable, or that the configuration information indicated by the second instruction information is unavailable in part or all of the configuration information, so as to proceed with the next step of processing in a timely manner.

[0035] Thirdly, embodiments of this application provide a model processing apparatus, which can be a terminal-side device. The terminal-side device can be a terminal device, a component in the terminal device (e.g., a processor, chip, circuit, or chip system), or a logic module or software that can implement all or part of the functions of the terminal device.

[0036] In one possible implementation, the model processing device may include modules, units, or means that correspond one-to-one with the methods / operations / steps / actions described in the first aspect. These modules, units, or means may be hardware circuits, software, or a combination of hardware circuits and software.

[0037] In one possible implementation, the model processing device includes a processing unit and a transceiver unit. The processing unit is configured to send first indication information through the transceiver unit. The first indication information is used to indicate a first association identifier corresponding to first intelligent model information, and the first intelligent model information is used to indicate at least one configuration information related to the first intelligent model. The transceiver unit is configured to receive second indication information, which is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier.

[0038] In one possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority.

[0039] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the transceiver unit is further used to send third indication information, the third indication information is used to indicate the priority order of the first association identifier and the second association identifier, and / or the priority order of the first intelligent model information and the second intelligent model information.

[0040] In another possible implementation, at least one configuration information related to the first intelligent model is sorted by priority.

[0041] In another possible implementation, the transceiver unit is further configured to send fourth indication information, which is used to notify updates of at least one of the following information, including: a first priority sort, a second priority sort, or a third priority sort, wherein the first priority sort is the priority sort of the first association identifier and the second association identifier, the second priority sort is the priority sort of the first intelligent model information and the second intelligent model information, and the third priority sort is the priority sort of at least one configuration information related to the first intelligent model.

[0042] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, the second indication information is further used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier, the processing unit is further used to determine the first inference result of the first intelligent model and the second inference result of the second intelligent model based on the second indication information; the transceiver unit is further used to send the first inference result and / or the second inference result according to a first priority order, the first priority order being the priority order of the first association identifier and the second association identifier.

[0043] In another possible implementation, the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier; the processing unit is further used to determine, based on the second indication information, a third inference result corresponding to the first configuration information under the first intelligent model and a fourth inference result corresponding to the second configuration information under the first intelligent model; the transceiver unit is further used to send the third inference result and / or the fourth inference result according to a third priority order, wherein the third priority order is the priority order of at least one configuration information related to the first intelligent model.

[0044] In another possible implementation, the processing unit is further configured to determine whether the first intelligent model is available based on the second indication information; the processing unit is further configured to determine a first inference result based on the first intelligent model if the first intelligent model is available; the processing unit is further configured to send a fifth indication information through the transceiver unit if the first intelligent model is unavailable, the fifth indication information being used to notify that the first intelligent model is unavailable, or that the configuration information indicated by the second indication information is unavailable in some or all of the configuration information.

[0045] For information on the technical effects of the third aspect or possible implementation, please refer to the description of the technical effects of the first aspect or corresponding implementation.

[0046] Fourthly, embodiments of this application provide a model processing apparatus, which can be a network-side device. The network-side device can be a network device, a component in the network device (e.g., a processor, chip, circuit, or chip system), or a logic module or software that can implement all or part of the functions of the network device.

[0047] In one possible implementation, the model processing device may include modules, units, or means that correspond one-to-one with the methods / operations / steps / actions described in the second aspect. These modules, units, or means may be hardware circuits, software, or a combination of hardware circuits and software.

[0048] In one possible implementation, the model processing device includes a processing unit and a transceiver unit. The processing unit is configured to receive first indication information via the transceiver unit. The first indication information is used to indicate a first association identifier corresponding to first intelligent model information, and the first intelligent model information is used to indicate at least one configuration information related to the first intelligent model. The transceiver unit is configured to send second indication information, which is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier.

[0049] In one possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority.

[0050] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the transceiver unit is further used to receive third indication information, the third indication information being used to indicate the priority order of the first association identifier and the second association identifier, and / or the priority order of the first intelligent model information and the second intelligent model information.

[0051] In another possible implementation, at least one configuration information related to the first intelligent model is sorted by priority.

[0052] In another possible implementation, the transceiver unit is further configured to receive fourth indication information, which is used to notify updates of at least one of the following information, including: a first priority order, a second priority order, or a third priority order, wherein the first priority order is the priority order of the first association identifier and the second association identifier, the second priority order is the priority order of the first intelligent model information and the second intelligent model information, and the third priority order is the priority order of at least one configuration information related to the first intelligent model.

[0053] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, the second indication information is further used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier, and the transceiver unit is further used to receive the first inference result and / or the second inference result, the first inference result being the inference result of the first intelligent model, and the second inference result being the inference result of the second intelligent model.

[0054] In another possible implementation, the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier, and the transceiver unit is further used to receive a third inference result and / or a fourth inference result, wherein the third inference result is the inference result corresponding to the first configuration information under the first intelligent model, and the fourth inference result is the inference result corresponding to the second configuration information under the first intelligent model.

[0055] In another possible implementation, the transceiver unit is further configured to receive a fifth indication message, which is used to notify that the first intelligent model is unavailable, or that the configuration information indicated by the second indication message is unavailable in some or all of the configuration information.

[0056] For the technical effects of the fourth aspect or possible implementation, please refer to the introduction of the technical effects of the second aspect or corresponding implementation.

[0057] Fifthly, embodiments of this application provide a model processing apparatus, which includes at least one processor that invokes a computer program or instructions stored in a memory to execute the method described in the first aspect or a possible implementation thereof.

[0058] In one possible implementation, the model processing device also includes a memory and a communication interface. Optionally, the memory and processor are integrated together.

[0059] In one possible implementation, the memory is located outside the model processing device.

[0060] In a sixth aspect, embodiments of this application provide a model processing apparatus, which includes at least one processor that invokes a computer program or instructions stored in a memory to execute the method described in the second aspect or a possible implementation thereof.

[0061] In one possible implementation, the model processing device also includes a memory and a communication interface. Optionally, the memory and processor are integrated together.

[0062] In one possible implementation, the memory is located outside the model processing device.

[0063] In a seventh aspect, embodiments of this application provide a chip device including at least one processor, the at least one processor being configured to execute computer programs or instructions to implement any of the above aspects or possible implementations of any of the above aspects.

[0064] In one possible implementation, the input of the chip device corresponds to the receiving operation in any of the above-mentioned aspects or possible implementations, and the output of the chip device corresponds to the transmitting operation in any of the above-mentioned aspects or possible implementations.

[0065] Optionally, the processor is coupled to the memory via an interface.

[0066] Optionally, the chip device may also include a memory storing computer program instructions.

[0067] Eighthly, embodiments of this application provide a computer-readable storage medium storing a computer program or instructions that, when executed on a processor, implement the methods described above.

[0068] Ninthly, embodiments of this application provide a computer program product that includes a computer program or instructions that, when executed on a processor, implement the method described in any of the above aspects.

[0069] In a tenth aspect, embodiments of this application provide a communication system comprising: the apparatus as described in the fifth aspect and the apparatus as described in the sixth aspect. Attached Figure Description

[0070] Figure 1A is a schematic diagram of the architecture of a communication system provided in an embodiment of this application;

[0071] Figure 1B is a schematic diagram of the architecture of another communication system provided in an embodiment of this application;

[0072] Figure 1C is a schematic diagram of a possible application framework in the communication system provided in an embodiment of this application;

[0073] Figure 1D is a schematic diagram of another possible application framework in the communication system provided in the embodiments of this application;

[0074] Figure 2 is a schematic diagram of a neuron structure;

[0075] Figure 3 is a schematic diagram of a neural network structure;

[0076] Figure 4 is a schematic diagram of wide beam and narrow beam;

[0077] Figure 5 is a schematic diagram of a periodic CSI-RS configuration;

[0078] Figure 6 is a schematic diagram of an aperiodic CSI-RS configuration;

[0079] Figure 7 is a schematic diagram of BM-Case 1;

[0080] Figure 8 is a schematic diagram of BM-Case 2;

[0081] Figure 9 is a schematic diagram of model inference on the terminal device side;

[0082] Figure 10 is a schematic diagram of a model processing method provided in an embodiment of this application;

[0083] Figure 11 is a schematic diagram of a first instruction information provided in an embodiment of this application;

[0084] Figure 12 is a schematic diagram of determining the reasoning result provided in an embodiment of this application;

[0085] Figure 13 is a schematic diagram of another method for determining the reasoning result provided in an embodiment of this application;

[0086] Figure 14 is a schematic diagram of another model processing method provided in an embodiment of this application;

[0087] Figure 15 is a schematic diagram of a model processing device provided in an embodiment of this application;

[0088] Figure 16 is a schematic diagram of another model processing device provided in an embodiment of this application;

[0089] Figure 17 is a schematic diagram of a chip system architecture provided in an embodiment of this application;

[0090] Figure 18 is a schematic diagram of the interaction between a terminal-side device and a network-side device according to an embodiment of this application. Detailed Implementation

[0091] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0092] References to "one embodiment" or "some embodiments" as described in this application mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0093] In the description of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c; a and b; a and c; b and c; or a and b and c. Where a, b, and c can be single or multiple.

[0094] It is understood that in this application, "instruction" can include direct instruction, indirect instruction, explicit instruction, and implicit instruction. When describing a certain instruction information to indicate A, it can be understood that the instruction information carries A, directly indicates A, or indirectly indicates A.

[0095] In this application, the information indicated by the instruction information is called the information to be instructed. In specific implementations, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index; indirectly instructing the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed; or instructing only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent.

[0096] The information to be instructed can be sent as a whole or divided into multiple sub-information messages, and the sending period and / or timing of these sub-information messages can be the same or different. This application does not limit the specific sending method. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the transmitting device by sending configuration information to the receiving device.

[0097] It is understood that "send" and "receive" in this application refer to the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which can include direct transmission via the air interface or indirect transmission via the air interface from other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which can include direct reception from YY via the air interface or indirect reception from YY via the air interface from other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0098] In other words, sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via buses, wiring, or interfaces.

[0099] It is understandable that information may undergo necessary processing, such as encoding and modulation, between the source and destination, but the destination can understand the valid information from the source. Similar statements in this application can be interpreted in a similar way and will not be elaborated further.

[0100] The communication method provided in this application can be applied to cellular communication systems related to the 3rd generation partnership project (3GPP), such as 4th generation (4G) communication systems, such as long term evolution (LTE) communication systems. For example, LTE communication systems may include LTE frequency division duplex (FDD) communication systems and LTE time division duplex (TDD) communication systems. It can also be applied to 5th generation (5G) communication systems, such as 5G new radio (NR) communication systems, or to various future communication systems and future communication networks. The method provided in this application can also be applied to Bluetooth systems, Wireless Fidelity (WiFi) systems, LoRa systems, or vehicle-to-everything (V2X) systems, communication systems supporting the integration of multiple wireless technologies, device-to-device (D2D) systems, vehicle-to-everything (V2X) communication systems, machine-to-machine (M2M) communication systems, machine-type communication (MTC) systems, and Internet of Things (IoT) communication systems or other communication systems. The method provided in this application can also be applied to satellite communication systems, wherein the satellite communication system can be integrated with the above-mentioned communication systems. The wireless communication systems involved in this application also include, but are not limited to, wireless local area network (WLAN) systems and narrowband Internet of Things (NB-IoT) systems.

[0101] In a communication system, one network element can send signals to or receive signals from another network element. These signals can include information, signaling, or data. The term "network element" can also be replaced by an entity, network entity, device, communication equipment, communication module, node, communication node, etc. This application uses a network element as an example for description. For instance, a communication system can include at least one terminal device and at least one network device. The network device can send downlink signals to the terminal device, and / or the terminal device can send uplink signals to the network device. It is understood that the terminal device in this application can be replaced by a first network element, and the network device can be replaced by a second network element, both performing the corresponding communication methods described in this application.

[0102] In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, thus requiring increasingly diverse demands. For example, networks need to support ultra-high speeds, ultra-low latency, and / or massive connectivity. This characteristic makes network planning, network configuration, and / or resource scheduling increasingly complex. Furthermore, as network functions become more powerful, such as supporting higher spectrum, supporting higher-order multiple-input multiple-output (MIMO) technologies, supporting beamforming, and / or supporting beam management, network energy efficiency has become a hot research topic. These new demands, new scenarios, and new characteristics bring unprecedented challenges to network planning, operation, and efficient operation. To meet these challenges, artificial intelligence (AI) technology can be introduced into wireless communication networks to achieve network intelligence. To support AI technology in wireless networks, AI nodes may also be introduced.

[0103] Please refer to Figure 1A, which is a schematic diagram of the architecture of a communication system provided in an embodiment of this application. The application scenario of this application will be described using the communication system 100 architecture shown in Figure 1A as an example. The communication system 100 includes at least one network device, such as network device 110 shown in Figure 1A. The communication system 100 may also include at least one terminal device, such as terminal device 120 and terminal device 130 shown in Figure 1A. Network device 110 and terminal devices (such as terminal devices 120 and 130) can communicate via a wireless link. The communication devices in this communication system, for example, network device 110 and terminal device 120, can communicate via multi-antenna technology.

[0104] Please refer to Figure 1B, which is a schematic diagram of the architecture of another communication system provided in this application embodiment. The communication system 200 includes at least one network device, such as network device 110 shown in Figure 1B. The communication system 200 may also include at least one terminal device, such as terminal device 120 and terminal device 130 shown in Figure 1B. Compared to the communication system 100 shown in Figure 1A, the communication system 200 shown in Figure 1B further includes an AI network element 140. The AI ​​network element 140 is used to perform AI-related operations, such as building training datasets or training AI models.

[0105] In one possible implementation, network device 110 can send data related to the training of the AI ​​model to AI network element 140, which then constructs a training dataset and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. AI network element 140 can send the results of operations related to the AI ​​model to network device 110, which then forwards them to the terminal device. For example, the results of operations related to the AI ​​model may include at least one of the following: a trained AI model, model evaluation results, or test results. Exemplarily, a portion of the trained AI model may be deployed on network device 110, and another portion on the terminal device. Alternatively, the trained AI model may be deployed on network device 110. Or, the trained AI model may be deployed on the terminal device.

[0106] It should be understood that Figure 1B illustrates the example of AI network element 140 being directly connected to network device 110. In other scenarios, AI network element 140 can also be connected to a terminal device. Alternatively, AI network element 140 can be connected to both network device 110 and a terminal device simultaneously. Alternatively, AI network element 140 can also be connected to network device 110 through a third-party network element. This application embodiment does not limit the connection relationship between AI network element and other network elements. Figure 1B uses AI network element 140 as a single network element as an example; AI network element 140 can also be configured as a module in network device and / or terminal device, for example, in network device 110 or terminal device as shown in Figure 1B. This application does not impose limitations.

[0107] It should be noted that Figures 1A and 1B are simplified schematic diagrams for ease of understanding. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 1A and 1B. In practical applications, the communication system may include multiple network devices or multiple terminal devices. This application does not limit the number of network devices and terminal devices included in the communication system.

[0108] It should be understood that the network devices and terminal devices in Figures 1A and 1B can be hardware, software based on functional division, or a combination of both. The network devices and terminal devices described below can be any of the network devices and terminal devices described below. It should be noted that the methods described in the embodiments of this application can be applied to the communication systems shown in Figures 1A and 1B.

[0109] (1) Terminal equipment, also known as user equipment (UE), user unit, user station, mobile station (MS), remote station, mobile device, mobile terminal (MT), terminal, wireless communication equipment, etc., is a device that provides voice or data connectivity to a user. Specifically, it includes devices that provide voice connectivity to a user, devices that provide data connectivity to a user, or devices that provide both voice and data connectivity to a user. For example, it may include handheld devices with wireless connectivity or processing devices connected to a wireless modem. This terminal equipment can communicate with the core network via a radio access network (RAN), exchanging voice or data with the RAN, or interacting with the RAN to exchange voice and data. Currently, terminal devices can be: mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices (such as smartwatches, smart bracelets, pedometers, etc.), in-vehicle devices (such as cars, bicycles, electric vehicles, airplanes, ships, trains, high-speed trains, etc.), virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, smart home devices (such as refrigerators, televisions, air conditioners, electricity meters, etc.), intelligent robots, workshop equipment, wireless terminals in self-driving vehicles, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, or flying devices (such as intelligent robots, hot air balloons, drones, airplanes), etc. Terminal devices can also be other devices with terminal functions; for example, a terminal device can also be a device that performs terminal functions in D2D communication.Terminal devices can also include vehicle-to-everything (V2X) terminal devices, machine-to-machine / machine-type communications (M2M / MTC) terminal devices, internet of things (IoT) terminal devices, light UEs, reduced capability UEs (REDCAP UEs), subscriber units, subscriber stations, mobile stations, remote stations, access points (APs), remote terminals, access terminals, user terminals, user agents, or user devices, and drone equipment. For example, this can include mobile phones (or "cellular" phones), computers with mobile terminal devices, portable, pocket-sized, handheld, and computer-embedded mobile devices, etc. Examples include personal communication service (PCS) telephones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices, or other processing devices connected to a wireless modem. It also includes limited devices, such as devices with low power consumption, limited storage capacity, or limited computing power. Examples include information sensing devices such as barcode scanners, radio frequency identification (RFID), sensors, global positioning systems (GPS), and laser scanners. In this application, terminal devices with wireless transceiver capabilities and chips that can be installed in the aforementioned terminal devices are collectively referred to as terminal devices.

[0110] As an example and not a limitation, in the embodiments of this application, when the terminal device can be a wearable device, wearable devices can also be called wearable smart devices. Wearable devices are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, accessories, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly defined, wearable smart devices include those with comprehensive functions, large size, and the ability to achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those focused on a specific application function that require cooperation with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0111] It should be noted that, in the embodiments of this application, the device used to implement the functions of the terminal device can be the terminal device itself, or it can be any device capable of supporting the terminal device in implementing the functions, such as a chip system, hardware circuit, software module, or a hardware circuit plus a software module. This device can be installed in the terminal device or used in conjunction with the terminal device. In the embodiments of this application, the chip system can be composed of chips, or it can include chips and other discrete devices. In this embodiment, the terminal device is used as an example to illustrate the device used to implement the functions of the terminal device, and this does not constitute a limitation on the solutions of the embodiments of this application.

[0112] (2) A network device is a device deployed in a wireless access network to provide wireless communication functions for terminal devices. A network device may also be called a wireless access network (RAN) entity, access network equipment, wireless access network device, access node, wireless node, network node, or communication device, etc.

[0113] For example, the network device can be an access network device for a cellular system related to the 3GPP (3rd Generation Partnership Project). For instance, a fourth-generation (4G) mobile communication system or a 5G mobile communication system. The network device can also be an access network device in an open RAN (O-RAN or ORAN) or cloud radio access network (CRAN). Alternatively, the network device can also be an access network device in a communication system formed by the integration of two or more of the above communication systems.

[0114] Network equipment includes, but is not limited to: 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 Node B, or home Node B (HNB)), baseband unit (BBU), access point (AP), relay station, macro base station, micro base station, wireless relay node, donor node or similar, or combinations thereof, in Wi-Fi systems; radio controller, wireless backhaul node, transmitting and receiving point (TRP), transmitting point (TP), master station, slave station, motor slide retainer (MSR) node, transmission node, or transceiver node in CRAN scenarios. Network equipment can also be access network equipment in 5G mobile communication systems. For example, a next-generation NodeB (gNB), TRP, TP in a New Radio (NR) system, or one or a group of antenna panels (including multiple antenna panels) of a base station in a 5G mobile communication system. Alternatively, network equipment can also be a network node constituting a gNB or transmission point. For example, a central unit (CU), a distributed unit (DU), or a radio unit (RU). Network equipment can also be a baseband unit (BBU), a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH). Network equipment can also refer to communication modules, modems, or chips used in the aforementioned equipment or devices. Network equipment can also be a mobile switching center and equipment that performs base station functions in D2D, V2X, and M2M communications, network-side equipment in next-generation communication networks, and equipment that performs base station functions in future communication systems. Network equipment can support networks with the same or different access technologies. Network devices can also be servers, wearable devices, vehicles, or in-vehicle equipment. For example, in V2X technology, network devices can be roadside units (RSUs).

[0115] Network equipment can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move depending on the location of that mobile base station. In other examples, a helicopter or drone can be configured as a device to communicate with another base station.

[0116] In some deployments, the network devices mentioned in the embodiments of this application may be devices including CU, DU, or CU and DU, or devices with control plane CU nodes (central unit-control plane (CU-CP)) and user plane CU nodes (central unit-user plane (CU-UP)) and DU nodes. For example, the network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0117] In some deployments, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be CUs, DUs, CU-CPs, CU-UPs, or RUs. CUs and DUs can be configured separately or included in the same network element, such as a BBU. RUs can be included in radio frequency equipment or radio frequency units, such as RRUs, AAUs, or RRHs.

[0118] RAN nodes can support one or more types of fronthaul interfaces, each corresponding to a DU and RU with different functions. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is another type of interface, relative to CPRI, some downlink and / or uplink baseband functions, such as, for downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix addition (CP), are moved from the DU to the RU; and for uplink, digital beamforming (BF), or one or more of fast Fourier transform (FFT) / cyclic prefix removal (CP), are moved from the DU to the RU. In one possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the segmentation between DU and RU differs, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0119] Taking eCPRI Cat A as an example, for downlink transmission, the DU is configured to implement one or more functions before and after layer mapping (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions after layer mapping (e.g., resource element (RE) mapping, digital beamforming (BF), or one or more functions of inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP)) are moved to the RU. For uplink transmission, the DU is configured to implement one or more functions before and after demapping (i.e., decoding, rate matching de-matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and demapping), while other functions after demapping (e.g., digital BF or one or more functions of fast Fourier transform (FFT) / removing CP) are moved to the RU. It is understandable that the functional descriptions of the DU and RU corresponding to various types of eCPRI can be found in the eCPRI protocol, and will not be elaborated here.

[0120] In one possible implementation, the processing unit in the BBU used to implement baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH used to implement baseband functions is called the baseband low (BBL) unit.

[0121] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0122] It should be noted that, in the embodiments of this application, the device used to implement the functions of the network device can be a network device itself; it can also be a device capable of supporting the network device in implementing the functions, such as a chip system, hardware circuit, software module, or hardware circuit plus software module. This device can be installed in the network device or used in conjunction with the network device. In the embodiments of this application, the chip system can be composed of chips or may include chips and other discrete devices. In this embodiment, the device used to implement the functions of the network device is described as a network device, and this does not constitute a limitation on the solutions of the embodiments of this application.

[0123] Network devices and / or terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can also be deployed in the air on airplanes, balloons, and satellites. This application does not limit the scenario in which the network devices and terminal devices are located. Furthermore, terminal devices and network devices can be hardware devices, or software functions running on dedicated hardware or general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities that include dedicated or general-purpose hardware devices and software functions. This application does not limit the specific form of the terminal devices and network devices.

[0124] Optionally, the AI ​​node can be deployed in one or more of the following locations within the communication system: access network devices, terminal devices, or core network devices, etc. Alternatively, the AI ​​node can be deployed independently, for example, in a location other than any of the aforementioned devices, such as in the host or cloud server of an over-the-top (OTT) system. The AI ​​node can communicate with other devices in the communication system, which can be one or more of the following: network devices, terminal devices, or core network elements, etc.

[0125] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, these nodes can be divided based on function, such as different AI nodes being responsible for different functions.

[0126] It can also be understood that AI nodes can be independent devices, integrated into the same device to implement different functions, or they can be network elements in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the AI ​​nodes described above. Optionally, an AI node can be an AI network element or an AI module.

[0127] Please refer to Figure 1C, which is a schematic diagram of a possible application framework in a communication system provided in this application embodiment. As shown in Figure 1C, network elements in the communication system are connected through interfaces (e.g., NG, Xn) or air interfaces. These network element nodes, such as core network equipment, access network nodes (RAN nodes), terminals, or one or more devices in operation administration and maintenance (OAM), are equipped with one or more AI modules (only one is shown in Figure 1C for clarity). The access network node can be a single RAN node or can include multiple RAN nodes, for example, including CU and DU. The CU and / or DU can also be equipped with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are set in CU-CP and / or CU-UP. The method described in this application embodiment can be applied to the communication system shown in Figure 1C. It should be noted that the method described in this application embodiment can be applied to the communication system described in Figure 1C.

[0128] The AI ​​module in Figure 1C is used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. Depending on the parameter configuration, the AI ​​module can implement different functions. The AI ​​module model can be configured based on one or more of the following parameters: structural parameters (e.g., at least one of the following: number of neural network layers, neural network width, inter-layer connections, neuron weights, neuron activation function, or bias in the activation function), input parameters (e.g., type and / or dimension of input parameters), or output parameters (e.g., type and / or dimension of output parameters). The bias in the activation function can also be referred to as the neural network bias.

[0129] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or they can be deployed on the same node or device.

[0130] Please refer to Figure 1D, which is a schematic diagram of another possible application framework in the communication system provided in the embodiments of this application. As shown in Figure 1D, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI ​​module shown in Figure 1C, used to implement AI-related functions. The RIC includes near-real-time RIC (near-RT RIC) and non-real-time RIC (non-RT RIC). The non-real-time RIC mainly processes non-real-time information, such as data that is not sensitive to latency, with a latency in the order of seconds. The real-time RIC mainly processes near-real-time information, such as data that is relatively sensitive to latency, with a latency in the order of tens of milliseconds. The method described in the embodiments of this application can be applied to the communication system shown in Figure 1D.

[0131] The near real-time RIC is used for model training and inference. For example, it can be used to train an AI model and then use that AI model for inference. The near real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data. Optionally, the near real-time RIC can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the near real-time RIC delivers the inference result to the DU, and the DU sends it to the RU.

[0132] The non-real-time RIC is also used for model training and inference. For example, it can be used to train an AI model and then use that model for inference. The non-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to the RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the non-real-time RIC delivers the inference results to the DU, which then forwards them to the RU.

[0133] The near real-time RIC and non-real-time RIC can also be set up as separate network elements. Optionally, the near real-time RIC and non-real-time RIC can also be part of other devices. For example, the near real-time RIC can be set in the RAN node (e.g., in CU, DU), while the non-real-time RIC can be set in the OAM, cloud server, core network device, or other network device.

[0134] In another possible implementation, the network device can be a network device equipped with one or more AI modules. The network device can be one or more devices in the core network, access network (RAN) node, or OAM as shown in Figure 1C. For example, the AI ​​module can be a RIC as shown in Figure 1D, such as a near real-time RIC or a non-real-time RIC. For example, the near real-time RIC is located in the RAN node (e.g., in the CU, DU), while the non-real-time RIC is located in the OAM, a cloud server, a core network device, or other network devices. The RIC can obtain subsets from multiple terminal devices from the RAN node (e.g., CU, CU-CP, CU-UP, DU, and / or RU), reassemble them into a training dataset #2, and be trained based on the training dataset #2. Exemplarily, the near real-time RIC and the non-real-time RIC can also be set up separately as a network element, and the network device can be either a near real-time RIC or a non-real-time RIC.

[0135] To better understand the solutions provided in the embodiments of this application, some terms, concepts or processes involved in the embodiments of this application will be introduced below.

[0136] I. Terminology Explanation

[0137] (1) Artificial intelligence: to give machines human intelligence, using computer hardware and software to simulate certain intelligent behaviors of humans, including machine learning and many other methods.

[0138] (2) Machine learning (ML): Learning models or rules from raw data. There are many different machine learning methods, such as neural networks, decision trees, support vector machines, etc.

[0139] (3) AI Model: This refers to a function model that maps an input of a certain dimension to an output of a certain dimension, and its parameters are obtained through machine learning training. For example, f(x) = ax 2 +b is a quadratic function model, which can be viewed as an AI model. a and b correspond to the parameters of the model, which can be obtained through machine learning training.

[0140] (4) Neural network: Here it refers to artificial neural network, which is a mathematical model that imitates the behavior characteristics of animal neural networks to perform distributed parallel information processing. It is a special form of AI model.

[0141] (5) Dataset: Data used for model training, validation and testing in machine learning. The quantity and quality of the data will affect the effect of machine learning.

[0142] (6) Model training: By selecting a suitable loss function, the model parameters are trained using an optimization algorithm to minimize the value of the loss function.

[0143] (7) Hyperparameters: parameters such as the number of layers in the neural network, the number of neurons, activation function, and loss function.

[0144] (8) Loss function: used to measure the difference between the model’s predicted value and the true value.

[0145] (9) Model testing: After training, the model performance is evaluated using test data.

[0146] (10) Model application: Use the trained model to solve practical problems.

[0147] II. Artificial Intelligence and Machine Learning

[0148] (1) Machine Learning:

[0149] Machine learning is an important technological approach to achieving artificial intelligence. Machine learning can be divided into supervised learning, unsupervised learning, and reinforcement learning.

[0150] (2) Supervised learning:

[0151] Supervised learning, based on collected sample values ​​and labels, uses machine learning algorithms to learn the mapping relationship between sample values ​​and labels, and expresses this learned mapping relationship using a machine learning model. The process of training the machine learning model is the process of learning this mapping relationship. For example, in signal detection, the noisy received signal is the sample, and the corresponding real constellation point is the label. Machine learning aims to learn the mapping relationship between samples and labels through training, that is, to enable the machine learning model to learn a signal detector. During training, the model parameters are optimized by calculating the error between the model's predicted values ​​and the real labels. Once the mapping relationship is learned, it can be used to predict the sample label of each new sample. The mapping relationship learned in supervised learning can include linear mappings and nonlinear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0152] (3) Unsupervised learning:

[0153] Unsupervised learning relies solely on collected sample values, using algorithms to discover inherent patterns within the samples. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals; that is, the model learns the mapping relationship from sample to sample, which is called self-supervised learning. During training, model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.

[0154] (4) Reinforcement learning:

[0155] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and the optimal decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action." Reinforcement learning training is achieved through iterative interaction with the environment.

[0156] (5) Deep Neural Networks:

[0157] Deep neural networks (DNNs) are a specific implementation of machine learning. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Traditional communication systems rely on extensive expert knowledge to design communication modules, while DNN-based deep learning communication systems can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

[0158] The idea behind DNNs originates from the neuronal structure of the brain. Each neuron performs a weighted summation of its input values, and the result is passed through a non-linear function to produce the output. See Figure 2, which is a schematic diagram of a neuron structure. Specifically, assume the neuron's input is x = [x0, ..., x...]. n The weights corresponding to the inputs are w = [w0, ..., w0]. n The bias of the weighted summation is b. The nonlinear function can take many forms; one example is the max{0,x} maximum value function. The effect of a neuron's execution can be... Please refer to Figure 3, which is a schematic diagram of a neural network structure. A DNN typically has a multi-layered structure, with each layer containing multiple neurons. The input layer processes the received values ​​through neurons and then passes them to the hidden layers. Similarly, the hidden layers then pass the computation results to the final output layer, producing the final output of the DNN.

[0159] DNNs typically have more than one hidden layer, and these hidden layers often directly affect the ability to extract information and fit functions. Increasing the number of hidden layers or widening the width of each layer can improve the function fitting ability of a DNN. The weights in each neuron are the parameters of the DNN network model. The model parameters are optimized through the training process, enabling the DNN network to extract data features and express mapping relationships. DNNs generally use supervised or unsupervised learning strategies to optimize model parameters.

[0160] Based on their construction methods, DNNs can be divided into feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Figure 3 shows an FNN network, characterized by complete pairwise connections between neurons in adjacent layers. This makes FNNs typically require a large amount of storage space, resulting in high computational complexity.

[0161] CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (discrete sampling along the time axis) and image data (two-dimensional discrete sampling) can both be considered grid-like data. CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters. Furthermore, depending on the type of information extracted by the window (such as people and objects in an image representing different types of information), each window can use different convolution kernels, allowing CNNs to better extract features from the input data.

[0162] Recurrent Neural Networks (RNNs) are a type of distributed neural network (DNN) that utilizes feedback time-series information. Their input includes the current input value and their own output value from the previous time step. RNNs are well-suited for acquiring temporally correlated sequence features, and are particularly applicable to applications such as speech recognition and channel coding / decoding.

[0163] The FNN, CNN, and RNN mentioned above are common neural network structures, all built upon neurons. As introduced above, each neuron performs a weighted summation operation on its input values, and the result is passed through a nonlinear function to produce the output. We call the weights of the weighted summation operation and the nonlinear function in the neural network the parameters of the neural network. Taking a neuron with max{0,x} as the nonlinear function as an example, we perform... The parameters of the operated neuron are weights w = [w0, ..., w nThe weighted summation bias is b, and the nonlinear function is max{0,x}. The parameters of all neurons in a neural network constitute the parameters of that neural network.

[0164] III. Beam Management

[0165] With the advancement of wireless communication technology, communication systems face increasing service demands, which place higher requirements on system capacity and communication latency. To address these challenges, fifth-generation mobile communication systems (5G) have introduced high-frequency bands above 6 GHz. These high-frequency bands offer advantages in both bandwidth and frequency compared to mid- and low-frequency bands below 6 GHz, thus providing higher transmission rates and system capacity. However, due to the weak penetration and strong path fading effect of high-frequency signals, their propagation distance is limited, and coverage is also restricted. Thanks to massive MIMO technology, high-frequency communication systems typically employ a large number of antennas for beamforming, thereby achieving considerable beam gain to compensate for the limited propagation distance caused by the high-frequency propagation characteristics. Effective beam management becomes crucial to achieving beamforming gain. To achieve beam management, existing technologies employ methods such as layered scanning to reduce beam scanning overhead, i.e., first scanning a wide beam, and then scanning a small portion of a narrow beam within the wide beam, thereby achieving the goal of reducing overhead. Please refer to Figure 4, which is a schematic diagram of a wide beam and a narrow beam. Beam selection is primarily accomplished through reference signals and corresponding beam measurements. Specifically, the reference signals mainly include the synchronization signal block (SSB) and the channel state information-reference signal (CSI-RS). The SSB is a cell broadcast signal, comprising the primary synchronization signal (PSS), secondary synchronization signal (SSS), physical broadcast channel (PBCH), and demodulation reference signal (DMRS). The SSB is transmitted periodically according to the cell configuration, and its function extends beyond beam management, also including initial access and time-frequency synchronization. Simply put, the SSB signal can be considered a wide-beam signal. Correspondingly, the CSI-RS signal is a user-level signal; the network configures one or more CSI-RS resources for users based on actual conditions. Likewise, the CSI-RS signal is not only used for beam management but also for channel quality measurements; it can be simply understood as a narrow-beam signal.

[0166] Traditional beam management systems perform a two-step beam scan during the service beam selection phase: The first phase scans the SSB (wide beam), during which the terminal device measures and reports the reference signal received power (RSRP) of the SSB beam to the network side. The second phase, based on the RSRP reported by the terminal device, the network side selects the SSB beam with the highest RSRP and configures CSI-RS resource scanning to scan the narrow beams covered by this SSB beam to determine the optimal beam. During the narrow beam scanning process configured by the network side, a Transmission Configuration Indication (TCI) status message containing quasico-location (QCL) information instructs the terminal device to use a fixed wide beam for reception. The terminal device feeds back the measurement results to the network side, which then determines the optimal beam for subsequent data transmission based on the measurement results. Each TCI status message may include a reference signal resource identifier. The reference signal resource identifier can be at least one of the following: non-zero power (NZP) channel state information reference signal (CSI-RS) resource identifier (NZP-CSI-RS-ResourceId) or SSB index (SSB-Index).

[0167] In this context, a beam is a communication resource. In the NR protocol, a beam can be represented as a spatial filter, or spatial parameters. The beam used to transmit signals can be called the transmission beam (Tx beam), or a spatial domain transmit filter or spatial domain transmit parameter; the beam used to receive signals can be called the reception beam (Rx beam), or a spatial domain receiver filter or spatial domain receive parameter.

[0168] The transmitting beam can refer to the distribution of signal strength in different directions in space after a signal is transmitted through an antenna, while the receiving beam can refer to the distribution of signal strength in different directions in space of a wireless signal received from an antenna.

[0169] Beams can be identified by their identifier (ID). For example, a beam ID can be a Channel State Information Reference Signal Resource Indicator (CSI-RS, CRI), or it can be a bit in a bitmap corresponding to the beam. For instance, the number of bits in the bitmap is equal to the total number of beams associated with the network device in a single beam management inference task.

[0170] Beams can be categorized into wide beams and narrow beams. A wide beam refers to a beam with a relatively large radiation range for the transmitting or receiving antenna when transmitting or receiving signals. Wide beams are typically used in applications requiring broadcast signals to a large area or wide coverage. They can provide a wider coverage area, but the signal strength is relatively weaker. A narrow beam refers to a beam with a relatively small radiation range for the transmitting or receiving antenna. Narrow beams are typically used in applications requiring signal focusing on a specific target or area. They can provide higher signal strength and higher directivity, but the coverage area is relatively smaller.

[0171] In recent years, AI technology has played a significant role in beam management, particularly in reducing beam scanning overhead. Typically, the AI ​​takes the received power of a wide beam or a sparsely scanned narrow beam measured at the terminal device as input. The model infers and outputs a Top-k list of candidate narrow beams (the RSRP value or ID of the output beam set). The network then performs a scan based on the Top-k candidate beams to ultimately determine the optimal beam. AI models can typically be deployed at either the terminal device or the network device. This application primarily focuses on the application of terminal device-side models in serving beam selection scenarios, and will not be elaborated upon further here.

[0172] IV. CSI-RS Measurement Resources and CSI Report Configuration

[0173] In the existing protocol, the configuration of CSI-RS measurement resources and the feedback of CSI reports both support three configuration modes: periodic, semi-persistent, and aperiodic. Please refer to Table 1, as shown in the table below:

[0174] Table 1

[0175] In the periodic CSI-RS configuration, the network side configures the CSI transmission period (every N slots) and offset (symbol offset within the period) and informs the UE, and transmits according to the configured period and offset. Please refer to Figure 5, which is a schematic diagram of a periodic CSI-RS configuration. In Figure 5(a), the network side configures the CSI transmission period to be every 5 slots and offset = 0, and transmits CSI according to the configured CSI transmission period. In Figure 5(b), the network side configures the CSI transmission period to be every 5 slots and offset = 3, and transmits CSI according to the configured CSI transmission period. In Figure 5(c), the network side configures the CSI transmission period to be every 10 slots and offset = 3, and transmits CSI according to the configured CSI transmission period.

[0176] In the semi-static CSI-RS configuration, the network side configures the CSI transmission period (every N slots) and offset (symbol offset within the period) and informs the UE, but whether or not transmission actually occurs is determined by the media access control element (MAC CE). The MAC CE activates / deactivates CSI-RS transmission and notifies the terminal device.

[0177] In the aperiodic CSI-RS configuration, the terminal device is notified of each CSI-RS transmission via DCI signaling. The aperiodic CSI-RS configuration also supports configuring the transmission of multiple CSI-RS resources at once, configured using a set of parameters [m, K], where m is the interval of the CSI-RS resources and K is the number of CSI-RS resources. See Figure 6, which is a schematic diagram of an aperiodic CSI-RS configuration. Aperiodic CSI is configured via DCI 1, specifically using parameters [m, K], where m is the interval of the CSI-RS resources and K is the number of CSI-RS resources, where K = 4. The interval between predicted CSIs is n. The configuration of the three types of CSI report feedback is similar to the above and will not be repeated here.

[0178] V. Spatial and Temporal Prediction in Beam Management

[0179] The applications of AI beam management are mainly in two aspects: spatial domain prediction (beam management example 1 (BM-Case 1)) and temporal domain prediction (beam management example 2 (BM-Case 2)), as detailed below:

[0180] Beam management refers to the prediction of downlink (DL) transmit (Tx) beams using both UE-side and network-side models, including [RANI / RAN2]:

[0181] Specifically, based on the measurement results of beam set B, spatial DL Tx beam prediction (i.e., "BM Casel") is performed on beam set A.

[0182] Among them, based on the historical measurement results of beam set B, time-domain DL Tx beam prediction (i.e., "BM-Case2") is performed on beam set A.

[0183] This specifies the necessary signals / mechanisms to facilitate life cycle management (LCM) operations (if any) specific to beam management use cases.

[0184] The method enables consistency between training and inference regarding network device-side additional conditions (if identified) for inference on the terminal device.

[0185] In BM-Case 1, please refer to Figure 7. Figure 7 is a schematic diagram of BM-Case 1. The input of the AI ​​model is the beam information (usually RSRP value) of a specific pattern scanned at a certain time. The set of this beam information is called set B. Specifically, the measurement quantities in set B are input into the AI ​​beam prediction model. The AI ​​model predicts the complete beam set set A. Set A includes the indices of the top-K candidate beams. The top-K beams and related information are selected and reported to the network side.

[0186] In BM-Case2, please refer to Figure 8, which is a schematic diagram of BM-Case2. A sliding time window collects the input information of the AI ​​model. Figure 8 shows the set B beam RSRP from time (t-N+1) to time (t) within the time period T1. The AI ​​model processes the input information and outputs the prediction results set A (regression model) or top-K beam IDs (classification model) for future time windows. For example, in the time period T2 shown in Figure 8, the prediction results set A or top-K beam IDs from time (t+1) to time (t+M) are shown. Finally, the top-k beams and related information are selected and reported to NW. It should be noted that this application is applicable to multi-task parallel scenarios of BM-Case1 and BM-Case2.

[0187] VI. Network-side conditions corresponding to the AI ​​model

[0188] Currently, both beam management and CSI prediction applications mention the need for alignment between network-side physical conditions and terminal-side conditions. These physical conditions may include network-side transceiver unit (TxRU) mappings, downtilt angles, base station types, and carrier frequencies. Changes in these physical conditions can lead to significant differences in the magnitude and distribution of the RSRP value of the transmitted beam. If the terminal device does not distinguish between these network-side physical conditions and mixes data from different network-side conditions for training, or trains the model under condition A and infers under condition B, this can result in the model failing to converge or exhibiting poor inference performance. Therefore, associated IDs are designed to represent the changing physical conditions on the network side. Each associated ID corresponds to a specific combination of network-side physical conditions. This associated ID is sent along with the reference signal resource set so that the terminal device can distinguish the data sent by the network side under different physical environments. For example, when the network needs to send reference signal resources, it also sends an associated ID representing its physical conditions. The terminal device generates a dataset and classifies it according to the associated ID, and trains the corresponding model. Therefore, the default scheme is that a set of network-side physical conditions corresponds to an associated ID, an associated ID corresponds to a type of data, and an AI model. During the inference phase, when the network sends model input data, it also sends an associated ID to identify the current state. After obtaining the ID, the terminal device selects the corresponding model for inference and obtains accurate results.

[0189] Associated ID is applicable when a model performs poorly in generalization across different scenarios. There are three typical scenarios for measuring model generalization:

[0190] Example 1 (Case 1): The model is trained under physical condition A on the network side (e.g., downtilt angle = 102 degrees) and inference is performed under physical condition A.

[0191] Example 2 (Case 2): The model is trained under physical condition B on the network side (e.g., downtilt angle = 110 degrees) and inference is performed under physical condition A.

[0192] Example 3 (Case 3): The model is trained under physical conditions A and B on the network side (e.g., downtilt angle = 110 degrees & 102 degrees), and inference is performed under physical conditions A and B.

[0193] Typically, the inference accuracy of Case 2 and Case 3 is compared with that of Case 1 to verify the generalization performance of the model and indirectly measure the impact of different network-side physical conditions on the size and distribution of the data (if the performance of Case 2 / 3 is very poor, it means that the datasets used for training and inference are too different, which means that the physical conditions have a greater impact on the data; conversely, it means that the physical conditions have a smaller impact on the data).

[0194] Please refer to Figure 9, which is a schematic diagram of model inference on the terminal device side, as follows:

[0195] Step 1: The network device sends a UECapabilityEnquiry request message to the terminal device.

[0196] The request message is used to instruct the terminal device to report its capability information.

[0197] Step 2: The terminal device sends its capability information (UECapabilityInformation) to the network device.

[0198] The capability information of the terminal device can refer to the AI ​​capabilities that the terminal device can support. For example, it can indicate the AI / ML functions or models in beam management scenarios, or the AI / ML functions or models in positioning scenarios.

[0199] Step 3: The network device sends RRC configuration information to the terminal device.

[0200] Specifically, based on the AI / ML functions or models supported by the terminal device, the network device can send RRC configuration information to the terminal device according to the network's needs, in order to request the terminal device to report available AI / ML functions or models.

[0201] The RRC configuration information may include parameters related to report configuration or inference.

[0202] Step 4: The terminal device sends its applicable functionality reporting to the network device.

[0203] Among them, the auxiliary information of the terminal device can refer to the availability judgment made by the terminal device based on the configuration of the network device. For example, RRC configuration information can include the judgment of whether the parameters related to the report configuration or inference are available, and the reporting of available AI / ML functions or models.

[0204] Step 5: The network device sends RRC configuration information to the terminal device.

[0205] The network device provides AI / ML configurations to the terminal device based on the information reported by the terminal device, namely the reported available AI / ML functions or models, such as report configuration and resource configuration.

[0206] Step 6: The terminal device performs functions such as registration, activation, inference, and monitoring.

[0207] Specifically, based on RRC configuration information, the terminal device performs AI inference, monitoring, and other functions.

[0208] Because AI models exhibit a certain degree of generalization across different CSI report configurations (CSI-reportConfig), multiple configuration information may correspond to a single model on the terminal device side. During actual deployment and inference of the AI ​​model, the terminal device and network device lack a unified understanding of the model-related configuration information and the associated identifiers. To address these issues, this application proposes the following solutions.

[0209] Please refer to Figure 10. Figure 10 is a schematic diagram of a model processing method provided in an embodiment of this application. The method shown in Figure 10 can be applied to terminal-side devices and network-side devices. The terminal-side device can be a terminal device, or a component applied in the terminal device (e.g., a processor, chip, circuit, or chip system), or a logic module or software that can implement all or part of the functions of the terminal device. The network-side device can be a network device, or a component applied in the network device (e.g., a processor, chip, circuit, or chip system), or a logic module or software that can implement all or part of the functions of the network device. In the embodiments shown in Figure 10 below, the terminal-side device is described as a terminal device and the network-side device is a network device. The method includes, but is not limited to, the following steps:

[0210] Step S1001: The terminal device sends the first instruction information.

[0211] In this process, the terminal device sends a first instruction message to the network device, and the network device receives the first instruction message from the terminal device.

[0212] The first indication information is used to indicate the first associated identifier corresponding to the first intelligent model information, or the first indication information is used to indicate the mapping relationship between the first associated identifier and the first intelligent model information. Optionally, the mapping relationship can also be described as an association relationship or a correspondence relationship, which is not limited in this embodiment. Optionally, the first associated identifier corresponding to the first intelligent model information can be represented in the form of a list, which includes the first associated identifier and the first intelligent model information. In one example, the first indication information is used to indicate the first associated identifier corresponding to the first intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2], where associated ID#1 is the first associated identifier, and [CSI-reportConfig#1,CSI-reportConfig#2] is the first intelligent model information.

[0213] The first intelligent model information is used to indicate at least one configuration information related to the first intelligent model. This at least one configuration information includes: at least one CSI report configuration (CSI-reportConfig), or at least one inference-related parameter. For example, the CSI report configuration may include at least one of the following: associated CSI resource information, uplink time-frequency domain resources corresponding to the CSI report, or the content type and quantity of the CSI report. Similarly, the inference-related parameter may include at least one of the following: relevant information for set A, relevant information for set B, relevant information for the reported content, or relevant information for time instances used for measurement in time-domain prediction. Of course, the at least one configuration information may also include other types of information, which are not limited in this application embodiment. It should be noted that this application describes at least one configuration information including: at least one CSI report configuration, i.e., the first intelligent model information is used to indicate at least one CSI report configuration related to the first intelligent model. Furthermore, the first intelligent model can be alternatively described as a first AI model, a first ML model, a first AI function, or a first ML function.

[0214] The first associated ID can be network-side condition identification information, the first intelligent model identification information (model ID), or the first function identification information (functionality ID). The first intelligent model identification information refers to information used to characterize the first intelligent model, which may include the model's name, version, creator, or creation time. The network-side condition identification information can refer to the network-side conditions applicable to the first intelligent model, or it can refer to the network-side conditions under which the training data used by the first intelligent model was collected. Since the training data used by the first intelligent model is collected under certain network-side conditions, when configuring the terminal device to execute the first intelligent model, the configured first intelligent model needs to be applicable to the current network-side conditions. That is, the network-side conditions during inference should be consistent with the network-side conditions when the training data of the first model was determined, so that the first model can achieve better inference performance. The network-side conditions corresponding to the network-side condition identification information can include one or more of the following: spatial arrangement of physical beams, beam gain, antenna height, antenna downtilt angle, or environmental parameters. For example, environmental parameters may include building density or terrain. The identification information for the first function can be found in the description of the identification information for the first intelligent model, and will not be repeated here.

[0215] Optionally, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model. For explanations of the second intelligent model information and the second association identifier, please refer to the relevant explanations of the first intelligent model information and the first association identifier, which will not be repeated here. The first indication information is also used to indicate the third association identifier corresponding to the third intelligent model information, and the third intelligent model information is used to indicate at least one configuration information related to the third intelligent model. Without loss of generality, the first indication information is used to indicate the i-th association identifier corresponding to the i-th intelligent model information, where the i-th intelligent model information is used to indicate at least one configuration information related to the i-th intelligent model, and i is a positive integer greater than or equal to 1. In one example, please refer to Figure 11. Figure 11 is a schematic diagram of a first indication information provided in an embodiment of this application. The first indication information is used to indicate the first association identifier corresponding to the first intelligent model information and the second association identifier corresponding to the second intelligent model information. Specifically, it is {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#3], associated ID#2:[CSI-reportConfig#4,CSI-reportConfig#5,CSI-reportConfig#6]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#3] is the first intelligent model information, associated ID#2 is the second association identifier, and [CSI-reportConfig#4,CSI-reportConfig#5,CSI-reportConfig#6] is the second intelligent model information.

[0216] The above describes how the first indication information is used to indicate the i-th associated identifier corresponding to the i-th intelligent model information. The following describes the priority order of the terminal device reporting the i-th intelligent model information and / or the i-th associated identifier, where "and / or" indicates reporting either one or both. There are two ways to prioritize the terminal device reporting the i-th intelligent model information and / or the i-th associated identifier: Method 1: The reporting order of the i-th associated identifier corresponding to the i-th intelligent model information implicitly indicates the priority order of the i-th intelligent model information and / or the i-th associated identifier. Method 2: The terminal device sends a separate message or signaling to indicate the priority order of the i-th intelligent model information and / or the i-th associated identifier. The following uses i=2 as an example, where the first indication information is used to indicate the first associated identifier corresponding to the first intelligent model information and the second associated identifier corresponding to the second intelligent model information, to illustrate the above two methods:

[0217] Method 1: The first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein, the first association identifier and the second association identifier are sorted according to priority, and / or the first intelligent model information and the second intelligent model information are sorted according to priority. Wherein, "and / or" represents the following three cases: Case 1, the first association identifier and the second association identifier are sorted according to priority; Case 2, the first intelligent model information and the second intelligent model information are sorted according to priority; Case 3: the first association identifier and the second association identifier are sorted according to priority, and the first intelligent model information and the second intelligent model information are sorted according to priority. It should be noted that in this application embodiment, "sorted according to priority" can be replaced by "arranged according to priority" or "presented in order of high to low priority," and this application embodiment does not limit this. The above can be understood as the reporting order of the first association identifier and the second association identifier implicitly indicating the order of priority of the first association identifier and the second association identifier, and the reporting order of the first intelligent model information and the second intelligent model information implicitly indicating the order of priority of the first intelligent model information and the second intelligent model information.

[0218] In one example, the first indication information is used to indicate the first association identifier corresponding to the first intelligent model information and the second association identifier corresponding to the second intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2], associated ID#2:[CSI-reportConfig#3,CSI-reportConfig#4]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2] is the first intelligent model information, associated ID#2 is the second association identifier, and [CSI-reportConfig#3,CSI-reportConfig#4] is the second intelligent model information. The associated ID#1 has a higher priority than associated ID#2, and CSI-reportConfig#1 and CSI-reportConfig#2 have a higher priority than CSI-reportConfig#3 and CSI-reportConfig#4.

[0219] In the above method, the terminal device sends a first indication information to the network device. The reporting order of the first association identifier and the second association identifier in the first indication information implicitly indicates the priority order of the first association identifier and the second association identifier, and the reporting order of the first intelligent model information and the second intelligent model information implicitly indicates the priority order of the first intelligent model information and the second intelligent model information. This can reduce signaling overhead. In addition, it can enable the terminal device and the network device to have a unified understanding of the priority order of the first association identifier and the second association identifier and / or the priority order of the first intelligent model information and the second intelligent model information, ensuring the consistency of understanding between the two parties.

[0220] Method 2: The first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information. The second intelligent model information is used to indicate at least one configuration information related to the second intelligent model. The method further includes: the terminal device sending third indication information, which is used to indicate that the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority. The terminal device sending the third indication information includes the terminal device sending the third indication information to the network device, and correspondingly, the network device receiving the third indication information from the terminal device.

[0221] This includes three scenarios, which can be found in scenarios 1, 2, and 3 of method 1. The above process can be understood as the terminal device sending a single message or signaling to instruct the first and second association identifiers to be ordered according to priority, and / or the first smart model information and the second smart model information to be ordered according to priority.

[0222] In one example, the first indication information is used to indicate the first association identifier corresponding to the first intelligent model information and the second association identifier corresponding to the second intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2], associated ID#2:[CSI-reportConfig#3,CSI-reportConfig#4]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2] is the first intelligent model information, associated ID#2 is the second association identifier, and [CSI-reportConfig#3,CSI-reportConfig#4] is the second intelligent model information. The terminal device sends a third indication information, which is used to indicate that the priority of associated ID#1 is higher than the priority of associated ID#2, and / or the priority of CSI-reportConfig#1 and CSI-reportConfig#2 is higher than the priority of CSI-reportConfig#3 and CSI-reportConfig#4.

[0223] In the above method, by sending third instruction information from the terminal device to the network device, the terminal device and the network device can have a unified understanding of the priority order of the first association identifier and the second association identifier and / or the priority order of the first intelligent model information and the second intelligent model information, thus ensuring the consistency of understanding between the two parties.

[0224] The above describes the priority order of different association identifiers, and / or the priority order of reporting different intelligent model information corresponding to different association identifiers. The following describes the priority order of different configuration information under the same association identifier, divided into Method A and Method B, as follows:

[0225] Method A: At least one configuration information related to the first intelligent model is sorted by priority.

[0226] For example, at least one type of configuration information related to the first intelligent model includes first configuration information and second configuration information, wherein the first configuration information and the second configuration information are ordered according to priority. The above process can be understood as the reporting order of at least one type of configuration information related to the first intelligent model implicitly indicating the priority order of the at least one type of configuration information related to the first intelligent model.

[0227] In one example, the first indication information is used to indicate the first association identifier corresponding to the first intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2] is the first intelligent model information, CSI-reportConfig#1 is the first configuration information related to the first intelligent model, and CSI-reportConfig#2 is the second configuration information related to the first intelligent model. The priority of the first configuration information is higher than the priority of the second configuration information, that is, the priority of CSI-reportConfig#1 is higher than the priority of CSI-reportConfig#2.

[0228] In the above method, the priority order of at least one configuration information related to the first intelligent model is implicitly indicated by the reporting order of that configuration information, thereby reducing signaling overhead. Furthermore, it enables terminal devices and network devices to have a unified understanding of the priority order of the at least one configuration information related to the first intelligent model, ensuring consistency in their understanding.

[0229] Method B: The terminal device sends a sixth instruction message, which is used to indicate at least one configuration information related to the first intelligent model in order of priority.

[0230] The sixth instruction information sent by the terminal device includes the terminal device sending the sixth instruction information to the network device, and the network device receiving the sixth instruction information from the terminal device.

[0231] For example, at least one configuration information related to the first intelligent model includes first configuration information and second configuration information. A sixth indication information is sent, which indicates that the first configuration information has a higher priority than the second configuration information. The above process can be understood as the terminal device sending a single message or signaling to indicate that at least one configuration information related to the first intelligent model is ordered according to priority.

[0232] In one example, the first indication information is used to indicate the first association identifier corresponding to the first intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2] is the first intelligent model information, CSI-reportConfig#1 is the first configuration information related to the first intelligent model, and CSI-reportConfig#2 is the second configuration information related to the first intelligent model. The terminal device sends a sixth indication information, which is used to indicate that the priority of the first configuration information is higher than the priority of the second configuration information, that is, the priority of CSI-reportConfig#1 is higher than the priority of CSI-reportConfig#2.

[0233] In the above method, by sending the sixth instruction information through the terminal device, the terminal device and the network device can have a unified understanding of the priority order of at least one configuration information related to the first intelligent model, thus ensuring the consistency of their understanding.

[0234] It should be noted that the above explanation uses the priority order of at least one configuration information related to the first intelligent model as an example. The priority order of at least one configuration information related to the second intelligent model can refer to the priority order of at least one configuration information related to the first intelligent model, the priority order of at least one configuration information related to the third intelligent model can refer to the priority order of at least one configuration information related to the first intelligent model, and so on.

[0235] The priority order has been described above. When the priority order changes, the terminal device sends a fourth indication message, which is used to indicate the update of the priority order, as follows:

[0236] In another possible implementation, the method further includes: the terminal device sending a fourth indication message for notifying updates to at least one of the following information.

[0237] The information includes at least one of the following: a first priority sort, a second priority sort, or a third priority sort, wherein the first priority sort is the priority sort of the first association identifier and the second association identifier, the second priority sort is the priority sort of the first intelligent model information and the second intelligent model information, and the third priority sort is the priority sort of at least one configuration information related to the first intelligent model.

[0238] In one example, the first indication information is used to indicate the first associated identifier corresponding to the first intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2]}. The third priority order is that CSI-reportConfig#1 has a higher priority than CSI-reportConfig#2. The terminal device sends a fourth indication information, which is used to notify the update of the third priority order, specifically {associated ID#1:[CSI-reportConfig#2,CSI-reportConfig#1]}, that is, CSI-reportConfig#2 has a higher priority than CSI-reportConfig#1.

[0239] In the above method, by sending a fourth instruction information from the terminal device to the network device, the network device can be notified in a timely manner when at least one of the above information changes, thereby ensuring the accuracy of the above at least one information.

[0240] It should be noted that the above describes the order of priorities reported by the terminal device, such as first priority order, second priority order, or third priority order. The network side can also dynamically indicate the order of priorities reported by the terminal device.

[0241] In another possible implementation, before the terminal device sends the first indication information, the method further includes: the network device sending a UECapabilityEnquiry request message to the terminal device; the terminal device sending UECapabilityInformation to the network device; the network device sending RRC configuration information to the terminal device; and the terminal device sending applicable functionality reporting to the network device. See Figure 9 for details.

[0242] Optionally, the first instruction information may or may not be carried in the applicable functionality reporting of the terminal device; this embodiment does not impose any limitation. The applicable functionality reporting of the terminal device includes the intelligent model information indicated in the first instruction information.

[0243] In one example, the applicable functionality reporting of the terminal device includes: CSI-reportConfig#1, CSI-reportConfig#2, CSI-reportConfig#3, and CSI-reportConfig#4. A first indication information is carried within the applicable functionality reporting of the terminal device. This first indication information is used to indicate the first association identifier corresponding to the first intelligent model information and the second association identifier corresponding to the second intelligent model information. Specifically, it is {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2], associated ID#2:[CSI-reportConfig#3,CSI-reportConfig#4]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2] is the first intelligent model information, associated ID#2 is the second association identifier, and [CSI-reportConfig#3,CSI-reportConfig#4] is the second intelligent model information.

[0244] Step S1002: The network device sends a second instruction message.

[0245] The sending of the second instruction information by the network device includes: the network device sending the second instruction information to the terminal device, and correspondingly, the terminal device receiving the second instruction information from the network device.

[0246] The second indication information is used to indicate part or all of the first associated identifier and at least one configuration information under the first associated identifier. For example, the second indication information can be the index information of the first associated identifier and the index information of part or all of the configuration information under the first associated identifier. In one example, the second indication information is 0001-1, where 0001 is the index information of associated ID#1, used to indicate associated ID#1, and 1 is the index information of CSI-reportConfig#1 under associated ID#1, used to indicate CSI-reportConfig#1 under associated ID#1. It should be noted that the index information can be replaced by the identification information. By issuing index information instead of issuing configuration information in the above manner, compared to the prior art of issuing configuration information for at least one type of configuration information, the configuration overhead can be reduced.

[0247] Optionally, when the first indication information is used to indicate a first association identifier corresponding to the first intelligent model information, and the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, the part or all of the first association identifier and at least one configuration information under the first association identifier indicated by the second indication information can be determined based on the first association identifier corresponding to the first intelligent model information indicated by the first indication information. Specifically, the first association identifier indicated by the second indication information can be determined based on the first association identifier indicated by the first indication information, and part of the at least one configuration information under the first association identifier indicated by the second indication information can be determined by selecting a portion from at least one configuration information in the first intelligent model information corresponding to the first association identifier indicated by the first indication information. All of the at least one configuration information under the first association identifier indicated by the second indication information can refer to at least one configuration information in the first intelligent model information corresponding to the first association identifier indicated by the first indication information. The above process can be understood as follows: when the first indication information indicates an association relationship, the content indicated by the second indication information is determined based on the association relationship indicated by the first indication information.

[0248] In one example, the first indication information is used to indicate the first association identifier corresponding to the first intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#3]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#3] is the first intelligent model information, and CSI-reportConfig#1, CSI-reportConfig#2, and CSI-reportConfig#3 are three configuration information related to the first intelligent model. For example, the second indication information is 0001-1, where 0001-1 represents associated ID#1 and CSI-reportConfig#1 under associated ID#1. Another example is the second indication information being 0001-2, where 0001-2 represents associated ID#1 and CSI-reportConfig#2 under associated ID#1. Yet another example is the second indication information being 0001-3, where 0001-3 represents associated ID#1 and CSI-reportConfig#2 under associated ID#1. CSI-reportConfig#3 under ID#1 and associated ID#1. For example, the second indication information is 0001-1, 0001-2, and 0001-3, where 0001-1 represents associated ID#1 and associated ID#1 under CSI-reportConfig#1, 0001-2 represents associated ID#1 and associated ID#1 under CSI-reportConfig#2, and 0001-3 represents associated ID#1 and associated ID#1 under CSI-reportConfig#3.

[0249] Optionally, when the first indication information is used to indicate the first association identifier corresponding to the first intelligent model information and the second association identifier corresponding to the second intelligent model information, the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier. Specifically, the first association identifier indicated by the second indication information can be selected from the first association identifier corresponding to the first intelligent model information and the second association identifier corresponding to the second intelligent model information. More specifically, the first association identifier indicated by the second indication information can be selected from the first association identifier and the second association identifier indicated by the first indication information, and part or all of the at least one configuration information under the first association identifier indicated by the second indication information can be selected from at least one configuration information in the first intelligent model information corresponding to the first association identifier and at least one configuration information in the second intelligent model information corresponding to the second association identifier. The above process can be understood as follows: when the first indication information indicates multiple associations, the content indicated by the second indication information is determined based on one or more associations selected from the multiple associations indicated by the first indication information.

[0250] In one example, the first indication information is used to indicate the first association identifier corresponding to the first intelligent model information and the second association relationship corresponding to the second intelligent model information. Specifically, it is {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#3], associated ID#2:[CSI-reportConfig#4,CSI-reportConfig#5,CSI-reportConfig#6]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#3] is the first intelligent model information, and CSI-reportConfig#1, CSI-reportConfig#2, and CSI-reportConfig#3 are three types of configuration information related to the first intelligent model. ID#2 is the second association identifier, [CSI-reportConfig#4, CSI-reportConfig#5, CSI-reportConfig#6] is the second smart model information, and CSI-reportConfig#4, CSI-reportConfig#5, and CSI-reportConfig#6 are three configuration information related to the second smart model. For example, the second indication information is 0001-1, where 0001-1 represents associated ID#1 and CSI-reportConfig#1 under associated ID#1. Another example is the second indication information being 0001-1, 0001-2, and 0001-3, where 0001-1 represents associated ID#1 and CSI-reportConfig#1 under associated ID#1, 0001-2 represents associated ID#1 and CSI-reportConfig#2 under associated ID#1, and 0001-3 represents associated ID#1 and CSI-reportConfig#3 under associated ID#1. For example, the second instruction information is 0002-4, where 0002-4 represents associated ID#2 and CSI-reportConfig#4 under associated ID#2.For example, the second instruction information is 0002-4, 0002-5, and 0002-6, where 0002-4 represents associated ID#2 and CSI-reportConfig#4 under associated ID#2, 0002-5 represents associated ID#2 and CSI-reportConfig#5 under associated ID#2, and 0002-6 represents associated ID#2 and CSI-reportConfig#6 under associated ID#2.

[0251] In another possible implementation, the method further includes: determining whether the first intelligent model is available based on the second indication information; if available, performing model inference based on the first intelligent model to determine the first inference result; if unavailable, sending a fifth indication information, the fifth indication information being used to notify that the first intelligent model is unavailable, or that the configuration information indicated by the second indication information is unavailable in some or all of the configuration information.

[0252] The process of determining whether the first intelligent model is available based on the second instruction information includes: the terminal device querying the configuration information related to the first intelligent model in the second instruction information, such as whether the first intelligent model corresponding to reportConfig is occupied; if it is not occupied, the first intelligent model is available; if it is occupied, the first intelligent model is unavailable. Alternatively, the terminal device determines whether there are sufficient central processing unit (CPU) resources for inference; if there are sufficient CPU resources, the first intelligent model is available; if there are insufficient CPU resources, the first intelligent model is unavailable.

[0253] For example, determining the first inference result based on the first intelligent model may include: inputting the beam information (usually RSRP values) of a specific pattern scanned at a certain time into the first intelligent model and outputting a predicted complete beam set set A, which includes the indices of the Top-K candidate beams. For example, inputting the RSRP values ​​of beam set B from time (t-N+1) to time (t) within time period T1 into the first intelligent model and outputting the predicted result set A or the top-K beam IDs from time (t+1) to time (t+M) within time period T2.

[0254] In one example, the first indication information is used to indicate the first association identifier corresponding to the first intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#5]}. The second indication information is used to indicate a portion of the first association identifier and at least one configuration information under the first association identifier, specifically 0001-1 and 0001-2, where 0001-1 represents associated ID#1 and CSI-reportConfig#1 under associated ID#1, and 0001-2 represents associated ID#1 and CSI-reportConfig#2 under associated ID#1. Based on the second indication information, the terminal device determines that the first intelligent model is unavailable. The terminal device then sends a fifth indication information, which is used to indicate unavailable configuration information in the portion of configuration information indicated by the second indication information. For example, CSI-reportConfig#1 under associated ID#1 is unavailable, or CSI-reportConfig#2 under associated ID#1 is unavailable, or associated ID#1... CSI-reportConfig#1 under ID#1 and CSI-reportConfig#2 under associated ID#1 are both unavailable.

[0255] In the above method, by determining the first inference result when the first intelligent model is available, the overhead of beam scanning can be reduced when the first inference result is used in beam management; by sending the fifth indication information when the first intelligent model is unavailable, the network device can be notified in a timely manner, so that the network device can perform the next step of processing in a timely manner.

[0256] Because AI models typically exhibit a certain degree of generalization across different CSI-reportConfig levels (e.g., data corresponding to multiple CSI report quantities can correspond to one AI model), there may be multiple CSI-reportConfigs that correspond to a single model on the terminal side. In this case, if multiple models corresponding to different CSI-reportConfigs simultaneously generate at least two inference results during inference and result reporting (e.g., the terminal device moves across cells, the base station switches additional conditions, or different CSI-reportConfigs may correspond to different tasks, and these different tasks are performed concurrently), then how to handle at least two inference results when resource conflicts occur is addressed, divided into two scenarios: Case 1 and Case 2, as detailed below:

[0257] Case 1: The first indication information is also used to indicate the second association identifier corresponding to the second intelligent model information. The second intelligent model information is used to indicate at least one configuration information related to the second intelligent model. The second indication information is also used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier. The method further includes: determining the first inference result of the first intelligent model and the second inference result of the second intelligent model based on the second indication information; sending the first inference result and / or the second inference result according to a first priority order, wherein the first priority order is the priority order of the first association identifier and the second association identifier.

[0258] The first inference result of determining the first intelligent model based on the second indication information includes: determining the first intelligent model based on the first association identifier, and performing model inference based on part or all of the configuration information under the first intelligent model and the first association identifier to obtain the first inference result. The second inference result of determining the second intelligent model based on the second indication information includes: determining the second intelligent model based on the second association identifier, and performing model inference based on part or all of the configuration information under the second intelligent model and the second association identifier to obtain the second inference result.

[0259] The priority ordering of the first association identifier and the second association identifier includes: the first association identifier has a higher priority than the second association identifier. Sending the first inference result and / or the second inference result according to the first priority order includes: when there are sufficient reporting resources, the first and second inference results are reported sequentially according to the priority of the first association identifier (higher than the second association identifier); when there are insufficient reporting resources, the first inference result is determined to be reported according to the priority of the first association identifier (higher than the second association identifier), and the second inference result is discarded. In other words, when the amount of data reported by the terminal device is too large to be fully reported, the inference results with higher task priority can be reported first based on the priority order of the association identifiers.

[0260] In one example, taking the terminal device executing two tasks simultaneously, Task 1 and Task 2, different configuration information under different models corresponds to different tasks. The first indication information is used to indicate the first association identifier corresponding to the first intelligent model information and the second association identifier corresponding to the second intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#3], associated ID#2:[CSI-reportConfig#4,CSI-reportConfig#5,CSI-reportConfig#6]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2,CSI-reportConfig#3] is the first intelligent model information, associated ID#2 is the second association identifier, and [CSI-reportConfig#4,CSI-reportConfig#5,CSI-reportConfig#6] is the second intelligent model information. The network device sends second indication information to the terminal device. This second indication information indicates part or all of the first association identifier and at least one configuration information under the first association identifier, and part or all of the second association identifier and at least one configuration information under the second association identifier. For example, the second indication information is 0001-1, 0001-2, 0001-3, 0002-4, and 0002-5. Here, 0001-1 represents associated ID#1 and CSI-reportConfig#1 under associated ID#1, 0001-2 represents associated ID#1 and CSI-reportConfig#2 under associated ID#1, 0001-3 represents associated ID#1 and CSI-reportConfig#3 under associated ID#1, 0002-4 represents associated ID#2 and CSI-reportConfig#4 under associated ID#2, and 0002-5 represents associated ID#2 and CSI-reportConfig#5 under associated ID#2. ID#1 corresponds to model 1, and associated ID#2 corresponds to model 2.Please refer to Figure 12, which is a schematic diagram of determining inference results according to an embodiment of this application. The terminal device executes task 1 and task 2 simultaneously. Task 1 involves performing model inference based on model 1 and CSI-reportConfig#1, CSI-reportConfig#2, and CSI-reportConfig#3 under associated ID#1 to obtain inference result 1. Task 2 involves performing model inference based on model 2 and CSI-reportConfig#4 and CSI-reportConfig#5 under associated ID#2 to obtain inference result 2. The terminal device then reports inference result 1 and / or inference result 2 according to the priority order of associated ID#1 and associated ID#2, where associated ID#1 has a higher priority than associated ID#2. For example, when there are sufficient resources, the terminal device reports inference result 1 and / or inference result 2 sequentially because associated ID#1 has a higher priority than associated ID#2. Conversely, when there are insufficient resources, the terminal device reports inference result 1 and / or inference result 2 sequentially because associated ID#1 has a higher priority than associated ID#2. Given the priority of ID#2, the terminal device reports inference result 1 and discards inference result 2.

[0261] In the above method, by sending the first inference result and / or the second inference result according to the first priority order, the problem of the terminal device being unable to process the resource contention between the first and second inference results can be solved. For example, the first priority order is that the priority of the first association identifier is higher than the priority of the second association identifier. When resources are sufficient, the terminal device can report the first inference result and the second inference result in order of priority of the first association identifier being higher than the priority of the second association identifier, thereby making the reporting more orderly. When resources are insufficient, the terminal device can report the first inference result in order of priority of the first association identifier being higher than the priority of the second association identifier, and discard the second inference result.

[0262] Case 2: The second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier. The method further includes: determining the third inference result corresponding to the first configuration information under the first intelligent model and the fourth inference result corresponding to the second configuration information under the first intelligent model based on the second indication information; sending the third inference result and / or the fourth inference result according to the third priority order, wherein the third priority order is the priority order of at least one configuration information related to the first intelligent model.

[0263] The process of determining the third inference result corresponding to the first configuration information under the first intelligent model and the fourth inference result corresponding to the second configuration information under the first intelligent model based on the second indication information includes: determining the first intelligent model based on the first association identifier, performing model inference based on the first intelligent model and the first configuration information to obtain the third inference result corresponding to the first configuration information, and performing model inference based on the first intelligent model and the second configuration information to obtain the fourth inference result corresponding to the second configuration information.

[0264] The priority ranking of at least one configuration information related to the first intelligent model includes prioritizing the first configuration information over the second configuration information, and sending the third inference result and / or the fourth inference result according to the third priority. This includes: when there are sufficient reporting resources, the third and fourth inference results are reported sequentially according to the priority of the first configuration information over the second configuration information; when there are insufficient reporting resources, the third inference result is determined to be reported according to the priority of the first configuration information over the second configuration information, and the fourth inference result is discarded. When the amount of data reported by the terminal device is too large and cannot be reported in its entirety, the inference results with higher task priority can be reported first according to the priority order of the configuration information.

[0265] In one example, taking the terminal device executing two tasks simultaneously, Task 1 and Task 2, different configuration information under the same model corresponds to different tasks. The first indication information is used to indicate the first association identifier corresponding to the first intelligent model information and the second association identifier corresponding to the second intelligent model information, specifically {associated ID#1:[CSI-reportConfig#1,CSI-reportConfig#2], associated ID#2:[CSI-reportConfig#3,CSI-reportConfig#4]}, where associated ID#1 is the first association identifier, [CSI-reportConfig#1,CSI-reportConfig#2] is the first intelligent model information, associated ID#2 is the second association identifier, and [CSI-reportConfig#3,CSI-reportConfig#4] is the second intelligent model information. The network device sends a second indication information to the terminal device. The second indication information is used to indicate all of the first association identifier and at least one configuration information under the first association identifier. For example, the second indication information is 0001-1 and 0001-2, where 0001-1 represents associated ID#1 and CSI-reportConfig#1 under associated ID#1, and 0001-2 represents associated ID#1 and CSI-reportConfig#2 under associated ID#1. Associated ID#1 corresponds to model 1.Please refer to Figure 13, which is a schematic diagram of another method for determining inference results provided in this application embodiment. The terminal device executes Task 1 and Task 2 simultaneously. Executing Task 1 means that the terminal device performs model inference based on Model 1 and CSI-reportConfig#1 under Associated ID#1 to obtain inference result 3. Executing Task 2 means that the terminal device performs model inference based on Model 1 and CSI-reportConfig#2 under Associated ID#1 to obtain inference result 4. Then, the terminal device reports inference result 3 and / or inference result 4 according to the priority order of CSI-reportConfig#1 and CSI-reportConfig#2, where the priority of CSI-reportConfig#1 is higher than the priority of CSI-reportConfig#2. For example, when there are sufficient reporting resources, since the priority of CSI-reportConfig#1 is higher than the priority of CSI-reportConfig#2, the terminal device reports inference result 3 and inference result 4 in sequence. For another example, when there are insufficient reporting resources, since the priority of CSI-reportConfig#1 is higher than the priority of CSI-reportConfig#2, the terminal device reports inference result 3 and discards inference result 4.

[0266] In the above method, by sending the third inference result and / or the fourth inference result according to the third priority order, the problem of the terminal device being unable to process the resource contention between the third and fourth inference results can be solved. For example, the third priority order is that the priority of the first configuration information is higher than the priority of the second configuration information. When resources are sufficient, the terminal device can report the third inference result and the fourth inference result in sequence according to the priority of the first configuration information being higher than the priority of the second configuration information, thereby making the reporting more orderly. When resources are insufficient, the terminal device can report the third inference result according to the priority of the first configuration information being higher than the priority of the second configuration information, and discard the fourth inference result.

[0267] In the method described in Figure 10, by sending the first association identifier corresponding to the first intelligent model information, i.e., the first association identifier corresponding to at least one configuration information related to the first intelligent model, the terminal device and the network device have a unified understanding of the mapping relationship between at least one configuration information related to the first intelligent model and the first association identifier, ensuring consistency in their understanding. Furthermore, by receiving the second indication information from the network device, the terminal device can perform subsequent processing based on the second indication information. For example, it can determine the first intelligent model based on the first association identifier, and perform model inference based on some or all of the configuration information under the first intelligent model and the first association identifier to obtain the inference result. When the inference result is used in beam management, it can reduce the overhead of beam scanning.

[0268] It should be noted that the deployment of AI models on the terminal device side can be implemented on the chip inside the terminal device, or it can be located outside the device, such as in the host of an over-the-top (OTT) system or in a cloud server. The network side needs to issue CSI-RS instructions and configure different RS resources to support the scanning of the terminal-side AI model. These functions can be completed by the network-side devices.

[0269] Please refer to Figure 14. Figure 14 is a schematic diagram of another model processing method provided in this application embodiment. The method shown in Figure 14 can be applied to terminal-side devices and network-side devices. The terminal-side device can be a terminal device, or a component applied in the terminal device (e.g., a processor, chip, circuit, or chip system), or a logic module or software that can implement all or part of the functions of the terminal device. The network-side device can be a network device, or a component applied in the network device (e.g., a processor, chip, circuit, or chip system), or a logic module or software that can implement all or part of the functions of the network device. In the embodiment shown in Figure 10 below, the terminal-side device is the terminal device, the network-side device is the network device, and the deployment of the AI ​​model on the terminal device side is described on the host or cloud server of the OTT system. The method includes, but is not limited to, the following steps:

[0270] Step S1401: Configure candidate inference parameters for the network device.

[0271] Step S1402: The network device sends candidate inference parameters to the terminal device.

[0272] Correspondingly, the terminal device receives candidate inference parameters from the network device.

[0273] Step S1403: The terminal device sends candidate inference parameters to the OTT.

[0274] Accordingly, the OTT receives candidate inference parameters from the terminal device.

[0275] Step S1404: OTT determines the availability of candidate inference parameters and identifies available inference parameters.

[0276] Step S1405: The OTT sends available inference parameters to the terminal device.

[0277] Accordingly, the terminal device receives available inference parameters from the OTT.

[0278] Step S1406: The terminal device sends available inference parameters to the network device.

[0279] Correspondingly, network devices receive available inference parameters from terminal devices.

[0280] Specifically, the terminal device sends first indication information to the network device. This first indication information indicates a first association identifier corresponding to the first intelligent model information. The first intelligent model information indicates at least one type of configuration information related to the first intelligent model. This at least one type of configuration information includes available inference parameters. For details, please refer to the relevant description in step S1001.

[0281] Step S1407: Configure the network device with the configuration information corresponding to the available inference parameters.

[0282] Step S1408: The network device sends configuration information to the terminal device.

[0283] Correspondingly, the terminal device receives configuration information from the network device.

[0284] This configuration information includes report configuration and resource configuration.

[0285] The process of the network device sending configuration information to the terminal device includes: the network device sending second indication information to the terminal device, the second indication information being used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, as described in step S1002.

[0286] Step S1409: The terminal device sends the measurement of beam set B to the OTT.

[0287] Accordingly, the OTT receives the measurement from set B of the terminal device.

[0288] Step S1410: OTT performs model inference based on the measurement of set B to determine the model monitoring results.

[0289] Optionally, the model monitoring results can also be referred to as model inference results.

[0290] Step S1411: The OTT sends the model monitoring results to the terminal device.

[0291] Correspondingly, the terminal device receives model monitoring results from the OTT.

[0292] Step S1412: The terminal device sends the model monitoring results to the network device.

[0293] Correspondingly, network devices receive model monitoring results from terminal devices.

[0294] In the method described in Figure 14, by sending the first association identifier corresponding to the first intelligent model information, i.e., the first association identifier corresponding to at least one configuration information related to the first intelligent model, the terminal device and the network device have a unified understanding of the mapping relationship between at least one configuration information related to the first intelligent model and the first association identifier, ensuring consistency in their understanding. Furthermore, by receiving the second indication information from the network device, the terminal device can perform subsequent processing based on the second indication information. For example, it can determine the first intelligent model based on the first association identifier, and perform model inference based on some or all of the configuration information under the first intelligent model and the first association identifier to obtain the inference result. When the inference result is used in beam management, it can reduce the overhead of beam scanning.

[0295] The methods of the embodiments of this application have been described in detail above, and the apparatus of the embodiments of this application is provided below.

[0296] Please refer to Figure 15. Figure 15 is a schematic diagram of the structure of a model processing device 1500 provided in an embodiment of this application. The model processing device 1500 may include modules, units, or means that correspond one-to-one with the methods / operations / steps / actions executed by the terminal-side device or network-side device in the above method embodiments. The modules, units, or means may be hardware circuits, software, or a combination of hardware circuits and software.

[0297] In one possible implementation, the model processing device 1500 may include a processing unit 1501 and a transceiver unit 1502, the specific details of which are as follows:

[0298] The processing unit 1501 is used for data processing. The transceiver unit 1502 can implement corresponding communication functions. The transceiver unit 1502 can also be called a communication interface or a communication module.

[0299] Optionally, the model processing apparatus 1500 may further include a storage unit, which can be used to store instructions and / or data. The processing unit 1501 can read the instructions and / or data in the storage module to implement the aforementioned method embodiments.

[0300] Optionally, the transceiver unit 1502 may include a sending unit and a receiving unit. The sending unit is used to perform the sending operation in the above method embodiments. The receiving unit is used to perform the receiving operation in the above method embodiments.

[0301] It should be noted that the model processing device 1500 may include a transmitting unit but not a receiving unit. Alternatively, the model processing device 1500 may include a receiving unit but not a transmitting unit. Specifically, it depends on whether the above-described scheme executed by the model processing device 1500 includes both transmitting and receiving actions.

[0302] Optionally, the model processing device 1500 is used to perform the actions performed by the terminal-side device in the embodiment shown in FIG10 above. For details, please refer to the relevant description in the embodiment shown in FIG10 above; it will not be elaborated here. For example, the model processing device 1500 is used to perform the following scheme:

[0303] The processing unit 1501 is configured to send first indication information through the transceiver unit 1502. The first indication information is used to indicate a first association identifier corresponding to the first intelligent model information. The first intelligent model information is used to indicate at least one configuration information related to the first intelligent model. The transceiver unit 1502 is configured to receive second indication information. The second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier.

[0304] In one possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority.

[0305] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the transceiver unit 1502 is further used to send third indication information, the third indication information is used to indicate the priority order of the first association identifier and the second association identifier, and / or the priority order of the first intelligent model information and the second intelligent model information.

[0306] In another possible implementation, at least one configuration information related to the first intelligent model is sorted by priority.

[0307] In another possible implementation, the transceiver unit 1502 is further configured to send fourth indication information, which is used to notify the update of at least one of the following information, including: a first priority order, a second priority order, or a third priority order, wherein the first priority order is the priority order of the first association identifier and the second association identifier, the second priority order is the priority order of the first intelligent model information and the second intelligent model information, and the third priority order is the priority order of at least one configuration information related to the first intelligent model.

[0308] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the second indication information is further used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier. The processing unit 1501 is further used to determine the first inference result of the first intelligent model and the second inference result of the second intelligent model based on the second indication information. The transceiver unit 1502 is further used to send the first inference result and / or the second inference result according to a first priority order, wherein the first priority order is the priority order of the first association identifier and the second association identifier.

[0309] In another possible implementation, the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier; the processing unit 1501 is further used to determine, based on the second indication information, a third inference result corresponding to the first configuration information under the first intelligent model and a fourth inference result corresponding to the second configuration information under the first intelligent model; the transceiver unit 1502 is further used to send the third inference result and / or the fourth inference result according to a third priority order, wherein the third priority order is the priority order of at least one configuration information related to the first intelligent model.

[0310] In another possible implementation, the processing unit 1501 is further configured to determine whether the first intelligent model is available based on the second indication information; the processing unit 1501 is further configured to determine a first inference result based on the first intelligent model if the first intelligent model is available; the processing unit 1501 is further configured to send a fifth indication information through the transceiver unit 1502 if the first intelligent model is unavailable, the fifth indication information being used to notify that the first intelligent model is unavailable, or that the configuration information indicated by the second indication information is unavailable in some or all of the configuration information.

[0311] It should be noted that the implementation and beneficial effects of each module can also be described in the corresponding description of the method embodiment shown in FIG10.

[0312] Optionally, the model processing device 1500 is used to perform the actions performed by the network-side device in the embodiment shown in FIG10 above. For details, please refer to the relevant description in the embodiment shown in FIG10 above; it will not be elaborated here. For example, the model processing device 1500 is used to perform the following scheme:

[0313] The processing unit 1501 is configured to receive first indication information through the transceiver unit 1502, wherein the first indication information is used to indicate a first association identifier corresponding to the first intelligent model information, and the first intelligent model information is used to indicate at least one configuration information related to the first intelligent model; the transceiver unit 1502 is configured to send second indication information, wherein the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier.

[0314] In one possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority.

[0315] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the transceiver unit 1502 is further used to receive third indication information, the third indication information is used to indicate the priority order of the first association identifier and the second association identifier, and / or the priority order of the first intelligent model information and the second intelligent model information.

[0316] In another possible implementation, at least one configuration information related to the first intelligent model is sorted by priority.

[0317] In another possible implementation, the transceiver unit 1502 is further configured to receive fourth indication information, which is used to notify updates of at least one of the following information, including: a first priority order, a second priority order, or a third priority order, wherein the first priority order is the priority order of the first association identifier and the second association identifier, the second priority order is the priority order of the first intelligent model information and the second intelligent model information, and the third priority order is the priority order of at least one configuration information related to the first intelligent model.

[0318] In another possible implementation, the first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, the second indication information is further used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier, and the transceiver unit 1502 is further used to receive the first inference result and / or the second inference result, the first inference result being the inference result of the first intelligent model, and the second inference result being the inference result of the second intelligent model.

[0319] In another possible implementation, the second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier, and the transceiver unit 1502 is further used to receive a third inference result and / or a fourth inference result, wherein the third inference result is the inference result corresponding to the first configuration information under the first intelligent model, and the fourth inference result is the inference result corresponding to the second configuration information under the first intelligent model.

[0320] In another possible implementation, the transceiver unit 1502 is further configured to receive a fifth indication message, the fifth indication message being used to notify that the first intelligent model is unavailable, or that the configuration information indicated by the second indication message is unavailable in some or all of the configuration information.

[0321] It should be noted that the implementation and beneficial effects of each module can also be described in accordance with the corresponding description of the method embodiment shown in FIG10. The division of modules in this application embodiment is illustrative and is only a logical functional division. In actual implementation, there may be other division methods.

[0322] The processing unit 1501 in the above embodiments can be implemented by at least one processor or processor-related circuitry. The transceiver unit 1502 can be implemented by a transceiver or transceiver-related circuitry. The transceiver unit 1502 can also be referred to as a communication module or communication interface. The storage module can be implemented by at least one memory.

[0323] Please refer to Figure 16. Figure 16 is a schematic diagram of the structure of a model processing device 1600 provided in an embodiment of this application. The model processing device 1600 may include modules, units, or means that correspond one-to-one with the methods / operations / steps / actions executed by the terminal-side device or network-side device in the above method embodiments. The modules, units, or means may be hardware circuits, software, or a combination of hardware circuits and software.

[0324] The model processing device 1600 includes at least one processor 1601. Optionally, it also includes a communication interface 1603 and a memory 1602. The processor 1601, memory 1602, and communication interface 1603 are interconnected via a bus 1604. Optionally, the processor 1601 and memory 1602 can be integrated together.

[0325] The memory 1602 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), and is used for related computer programs and data. The communication interface 1603 is used for receiving and sending data.

[0326] Processor 1601 can be one or more central processing units (CPUs). If processor 1601 is a CPU, the CPU can be a single-core CPU or a multi-core CPU.

[0327] The processor 1601 in the model processing device 1600 is used to read computer programs or instructions stored in the memory 1602 to implement the functions of the above-mentioned processing unit, and the communication interface 1603 in the model processing device 1600 is used to implement the functions of the above-mentioned transceiver unit.

[0328] Please refer to Figure 17, which is a schematic diagram of a chip system architecture provided in an embodiment of this application. This chip system architecture can be used in network-side devices and / or terminal-side devices. Input / output control is used to manage the input and output signals of the device; for example, input / output control can be represented as a modem, keyboard, mouse, touchscreen, etc. Input / output control may also be part of a processor. In one possible implementation, the chip input corresponds to the receiving operation in any of the above embodiments, and the chip output corresponds to the transmitting operation in any of the above embodiments. The receiver / transmitter is used to communicate with other devices. The receiver / transmitter may include a modem for modulating information or demodulating modulated information. The antenna is used to transmit or receive signals. Communication control is used to establish a connection. Storage may include random access memory (RAM) or read-only memory (ROM). Storage may be used to store code that can be executed by a processor to implement corresponding functions. Processors may include intelligent hardware devices such as general-purpose processors, digital signal processors (DSPs), central processing units (CPUs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), neural processing units, etc.

[0329] In one example, please refer to Figure 18. Figure 18 is a schematic diagram of the interaction between a terminal-side device and a network-side device provided in an embodiment of this application. Taking a chip system architecture for the terminal-side device as an example, the terminal-side device establishes a communication connection with the network-side device through a communication control module. The terminal-side device receives signaling and reference signals sent by the network-side device through an antenna and a receiver, and sends a CSI report to the network-side device through a transmitter and an antenna. The terminal-side device processes the measurement results of each measurement through a processor and stores the measurement results in a memory.

[0330] This application also provides a computer-readable storage medium storing a computer program or instructions that, when executed on a processor, implement the method performed by a terminal-side device or a network-side device in the above method embodiments.

[0331] This application also provides a computer program product, which includes a computer program or instructions that, when run on a processor, implement the method executed by the terminal-side device or network-side device in the above method embodiments.

[0332] This application also provides a communication system, which includes the terminal-side device and the network-side device described in the above embodiments. The terminal-side device is used to perform some or all of the operations performed by the terminal-side device in the above method embodiments, and the network-side device is used to perform some or all of the operations performed by the network-side device in the above method embodiments.

[0333] It is understood that the processor in the embodiments of this application may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor or any conventional processor.

[0334] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a base station or terminal. Of course, the processor and storage medium can also exist as discrete components in the base station or terminal.

[0335] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0336] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0337] In the description of this application, terms such as "first", "second", "S1001" or "S1002" are used only for the purpose of distinguishing descriptions and for the convenience of context. Different sequence numbers do not have specific technical meanings themselves and should not be construed as indicating or implying relative importance, nor should they be construed as indicating or implying the order of execution of operations. The order of execution of each process should be determined by its function and internal logic.

Claims

1. A model processing method, characterized in that, include: Send first indication information, the first indication information being used to indicate a first association identifier corresponding to the first intelligent model information, the first intelligent model information being used to indicate at least one configuration information related to the first intelligent model; Receive second indication information, which is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier.

2. The method according to claim 1, characterized in that, The first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein, the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority.

3. The method according to claim 1, characterized in that, The first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information being used to indicate at least one configuration information related to the second intelligent model, and the method further includes: Send a third indication message, which is used to indicate the priority order of the first association identifier and the second association identifier, and / or the priority order of the first intelligent model information and the second intelligent model information.

4. The method according to any one of claims 1-3, characterized in that, At least one configuration information related to the first intelligent model is sorted by priority.

5. The method according to claim 4, characterized in that, The method further includes: Send a fourth indication message, which is used to notify the update of at least one of the following information, including: a first priority sort, a second priority sort, or a third priority sort, wherein the first priority sort is the priority sort of the first association identifier and the second association identifier, the second priority sort is the priority sort of the first intelligent model information and the second intelligent model information, and the third priority sort is the priority sort of at least one configuration information related to the first intelligent model.

6. The method according to claim 1, characterized in that, The first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the second indication information is further used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier, the method further includes: Based on the second indication information, the first reasoning result of the first intelligent model and the second reasoning result of the second intelligent model are determined; The first inference result and / or the second inference result are sent according to a first priority order, wherein the first priority order is the priority order of the first association identifier and the second association identifier.

7. The method according to claim 1, characterized in that, The second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier, and the method further includes: Based on the second indication information, determine the third reasoning result corresponding to the first configuration information under the first intelligent model, and the fourth reasoning result corresponding to the second configuration information under the first intelligent model; The third inference result and / or the fourth inference result are sent according to the third priority order, wherein the third priority order is the priority order of at least one configuration information related to the first intelligent model.

8. The method according to any one of claims 1-5, characterized in that, The method further includes: Determine whether the first intelligent model is available based on the second indication information; If available, perform model reasoning based on the first intelligent model to determine the first reasoning result; If unavailable, a fifth indication message is sent, which is used to notify the first intelligent model that it is unavailable, or that the configuration information in the part or all of the configuration information indicated by the second indication message is unavailable.

9. A model processing method, characterized in that, include: Receive first indication information, the first indication information being used to indicate a first association identifier corresponding to the first intelligent model information, the first intelligent model information being used to indicate at least one configuration information related to the first intelligent model; Send a second instruction message, which is used to indicate part or all of the first association identifier and at least one configuration message under the first association identifier.

10. The method according to claim 9, characterized in that, The first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, and the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model; wherein, the first association identifier and the second association identifier are ordered according to priority, and / or the first intelligent model information and the second intelligent model information are ordered according to priority.

11. The method according to claim 9, characterized in that, The first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information being used to indicate at least one configuration information related to the second intelligent model, and the method further includes: Receive third indication information, which is used to indicate the priority order of the first association identifier and the second association identifier, and / or the priority order of the first intelligent model information and the second intelligent model information.

12. The method according to any one of claims 9-11, characterized in that, At least one configuration information related to the first intelligent model is sorted by priority.

13. The method according to claim 12, characterized in that, The method further includes: Receive a fourth indication message, the fourth indication message being used to notify the update of at least one of the following information, the at least one of the following information including: a first priority sort, a second priority sort, or a third priority sort, wherein the first priority sort is the priority sort of the first association identifier and the second association identifier, the second priority sort is the priority sort of the first intelligent model information and the second intelligent model information, and the third priority sort is the priority sort of at least one configuration information related to the first intelligent model.

14. The method according to claim 9, characterized in that, The first indication information is further used to indicate the second association identifier corresponding to the second intelligent model information, the second intelligent model information is used to indicate at least one configuration information related to the second intelligent model, and the second indication information is further used to indicate part or all of the second association identifier and at least one configuration information under the second association identifier, the method further includes: Receive a first inference result and / or a second inference result, wherein the first inference result is the inference result of a first intelligent model and the second inference result is the inference result of a second intelligent model.

15. The method according to claim 9, characterized in that, The second indication information is used to indicate part or all of the first association identifier and at least one configuration information under the first association identifier, including: the second indication information is used to indicate the first association identifier and the first configuration information and the second configuration information under the first association identifier, and the method further includes: Receive a third inference result and / or a fourth inference result, wherein the third inference result is the inference result corresponding to the first configuration information under the first intelligent model, and the fourth inference result is the inference result corresponding to the second configuration information under the first intelligent model.

16. The method according to any one of claims 9-13, characterized in that, The method further includes: Receive a fifth indication message, which is used to notify the first intelligent model that it is unavailable, or that the configuration information in the part or all of the configuration information indicated by the second indication message is unavailable.

17. A model processing device, characterized in that, The apparatus includes at least one processor, which is configured to invoke a computer program or instructions stored in a memory to perform the method as described in any one of claims 1-8.

18. A model processing device, characterized in that, The apparatus includes at least one processor, which is configured to invoke a computer program or instructions stored in a memory to perform the method as described in any one of claims 9-16.

19. A communication system, characterized in that, The communication system includes: the apparatus as described in claim 17 and the apparatus as described in claim 18.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or instructions that, when executed on a processor, implement the method as described in any one of claims 1-16.

21. A computer program product, characterized in that, The computer program product includes a computer program or instructions that, when run on a processor, implement the method as described in any one of claims 1-16.

Images