WO2025227699A1 - Communication method and related apparatus - Google Patents

Abstract

A communication method and a related apparatus. In the method, a first communication apparatus can process, on the basis of first data indicated by a second communication apparatus, one or more AI models of the first communication apparatus to obtain a processing result; and the first communication apparatus can then send second information obtained on the basis of the processing result, such that the second communication apparatus can determine some or all of the AI models among the one or more AI models on the basis of the second information (or determine an AI-enabled function supported by the first communication apparatus). In other words, after the second communication apparatus indicates the first data to the first communication apparatus, the first communication apparatus can indicate to the second communication apparatus an AI model or AI-enabled function corresponding to the first data. In this way, a communication apparatus in a communication system can participate in AI model processing, and provide an AI model processing capability corresponding to specified data or provide an AI-enabled function corresponding to the specified data.

Description

A communication method and related apparatus

This application claims priority to Chinese Patent Application No. 202410529350.0, filed on April 28, 2024, entitled "A Communication Method and Related Device", the entire contents of which are incorporated herein by reference.

Technical Field

This application relates to the field of communications, and more particularly to a communication method and related apparatus.

Background Technology

Wireless communication can be a transmission communication between two or more communication nodes that does not propagate through conductors or cables. These communication nodes generally include network devices and terminal devices.

Currently, in wireless communication systems, communication nodes generally possess signal transmission and reception capabilities as well as computing capabilities. Taking network devices with computing capabilities as an example, the computing capabilities of network devices mainly provide computational support for signal transmission and reception capabilities (e.g., processing signals for transmission and reception) to enable communication between network devices and other communication nodes.

However, in communication networks, communication nodes may possess surplus computing power beyond simply providing computational support for the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical problem that needs to be solved.

Summary of the Invention

This application provides a communication method and related apparatus, which enables communication devices in a communication system to participate in the processing of artificial intelligence (AI) models and provide the processing capability of AI models corresponding to specified data or provide AI enabling functions corresponding to specified data.

This application provides a communication method executed by a first communication device. The first communication device may be a communication equipment (such as a terminal device or network device), or it may be a component of a communication equipment (such as a processor, chip, or chip system), or it may be a logic module or software capable of implementing all or part of the functions of the communication equipment. In this method, the first communication device receives first information, which is used to indicate first data; wherein the first data is used to obtain a processing result through processing by one or more AI models of the first communication device; the first communication device sends second information, which is determined based on the processing result; wherein the second information is used to determine some or all of the AI models in the one or more AI models, or the second information is used to determine the AI-enabled functions supported by the first communication device.

Based on the above scheme, the first communication device can process one or more AI models of the first communication device based on the first data indicated by the second communication device to obtain a processing result. Subsequently, the first communication device can send second information based on the processing result, enabling the second communication device to determine some or all of the AI models among the one or more AI models (or, to determine the AI-enabled functions supported by the first communication device). In other words, after the second communication device indicates the first data to the first communication device, the first communication device can indicate the AI model or AI-enabled function corresponding to the first data to the second communication device. In this way, communication devices in the communication system can participate in the processing of AI models and provide the processing capability of the AI model corresponding to the specified data or provide the AI-enabled function corresponding to the specified data.

In this application, AI model may be replaced with other terms, such as neural network, neural network model, AI neural network model, machine learning model, or AI processing model, etc.

In this application, the term "AI-enabled function" can be replaced with other terms, such as AI-enabled features, AI capabilities, or AI functions.

In one possible implementation of the first aspect, the first data includes first training data used for model training of the one or more AI models; wherein the processing result is obtained by training the model based on the first training data to obtain a trained model, and then testing the trained model with pre-configured test data.

Based on the above scheme, the first data obtained by the first communication device through the first information may include the first training data, enabling the first communication device to instruct the second communication device on the AI model or AI-enabled function corresponding to the model training process implemented by the first training data.

In one possible implementation of the first aspect, the first data includes first test data, which is used for model testing of the one or more AI models; wherein the processing result is obtained by training the model based on pre-configured training data to obtain a trained model, and then testing the trained model using the first test data.

Based on the above scheme, the first data obtained by the first communication device through the first information may include the first test data, enabling the first communication device to instruct the second communication device on the AI model or AI-enabled function corresponding to the model testing process implemented by the first test data.

In one possible implementation of the first aspect, the first data includes second training data and second test data. The second training data is used for model training of the one or more AI models, and the second test data is used for model testing of the one or more AI models. The processing result is obtained by training the model based on the second training data to obtain the trained model, and then testing the trained model using the second test data.

Based on the above scheme, the first data obtained by the first communication device through the first information may include second training data and second test data, so that the first communication device can instruct the second communication device on the AI model or AI-enabled function corresponding to the model training process implemented by the second training data and the model testing process implemented by the second test data.

In one possible implementation of the first aspect, the first information includes configuration information for collecting some or all of the data in the first data, and/or, the first information includes some or all of the data in the first data.

Based on the above scheme, the first information received by the first communication device may include one or more of the above information contents, so that the first communication device can obtain the first data in multiple ways.

In one possible implementation of the first aspect, the first information further includes at least one of the following:

The first indication information is used to indicate the scenario corresponding to the first data;

The second instruction information is used to indicate the preprocessing rules corresponding to the first data;

The third instruction information is used to indicate the area information to which the first data applies.

Based on the above scheme, the first information received by the first communication device may also include at least one of the above items, so that the first communication device obtains the first data based on the at least one of the above items.

In one possible implementation of the first aspect, the second information includes any of the following:

The fourth instruction information is used to indicate the result of the processing;

The fifth instruction information is used to indicate some or all of the AI models in the one or more AI models;

The sixth instruction information is used to indicate the AI-enabled functions supported by the first communication device.

Based on the above scheme, the second information sent by the first communication device may include any of the above, so that the second communication device can determine the AI model or AI-enabled function corresponding to the first data in a variety of ways.

In one possible implementation of the first aspect, the method further includes: the first communication device receiving third information, the third information being used to indicate auxiliary information corresponding to the one or more AI models, the auxiliary information being used to indicate at least one of the following: model function, model structure parameters, data format of model input, and data format of model output.

Optionally, the first and third information can be carried in the same message or in different messages; this is not limited here.

Based on the above scheme, the first communication device can also determine the auxiliary information corresponding to one or more AI models in the first communication device through the received third information, so that the first communication device can obtain an AI model or AI-enabled function that matches the auxiliary information based on the auxiliary information.

A second aspect of this application provides a communication method executed by a second communication device. The second communication device can be a communication equipment (such as a terminal device or network device), or it can be a component of a communication equipment (such as a processor, chip, or chip system), or it can be a logic module or software capable of implementing all or part of the functions of the communication equipment. In this method, the second communication device sends first information to indicate first data; wherein the first data is used to obtain a processing result through processing by one or more AI models of the first communication device; the second communication device receives second information, which is determined based on the processing result; wherein the second information is used to determine some or all of the one or more AI models, or to determine the AI-enabled functions supported by the first communication device.

Based on the above scheme, after the second communication device instructs the first communication device to send first data via first information, the first communication device can process one or more AI models to obtain a processing result. Subsequently, the second communication device can receive second information based on the processing result, enabling it to determine some or all of the AI models (or, determine the AI-enabled functions supported by the first communication device) based on the second information. In other words, after the second communication device instructs the first communication device to send first data, the first communication device can instruct the second communication device to send the AI model or AI-enabled function corresponding to the first data. In this way, communication devices in the communication system can participate in the processing of AI models and provide the processing capability of the AI model corresponding to specified data or provide the AI-enabled function corresponding to specified data.

In one possible implementation of the second aspect, the first data includes first training data used for model training of the one or more AI models; wherein the processing result is obtained by training the model based on the first training data to obtain a trained model, and then testing the trained model with pre-configured test data.

Based on the above scheme, the first data indicated by the second communication device through the first information may include the first training data, so that the first communication device can indicate to the second communication device the AI model or AI enabling function corresponding to the model training process implemented by the first training data.

In one possible implementation of the second aspect, the first data includes first test data, which is used for model testing of the one or more AI models; wherein the processing result is obtained by training the model based on pre-configured training data to obtain a trained model, and then testing the trained model using the first test data.

Based on the above scheme, the first data indicated by the second communication device through the first information may include the first test data, so that the first communication device can indicate to the second communication device the AI model or AI-enabled function corresponding to the model testing process implemented by the first test data.

In one possible implementation of the second aspect, the first data includes second training data and second test data. The second training data is used for model training of the one or more AI models, and the second test data is used for model testing of the one or more AI models. The processing result is obtained by training the model based on the second training data to obtain the trained model, and then testing the trained model using the second test data.

Based on the above scheme, the first data indicated by the second communication device through the first information may include second training data and second test data, so that the first communication device can indicate to the second communication device the AI model or AI enabling function corresponding to the model training process implemented by the second training data and the model testing process implemented by the second test data.

In one possible implementation of the second aspect, the first information includes configuration information for collecting some or all of the data in the first data, and/or the first information includes some or all of the data in the first data.

Based on the above scheme, the first information sent by the second communication device to the first communication device may include one or more of the above information contents, so that the first communication device can obtain the first data in multiple ways.

In one possible implementation of the second aspect, the first information further includes at least one of the following:

The first indication information is used to indicate the scenario corresponding to the first data;

The second instruction information is used to indicate the preprocessing rules corresponding to the first data;

The third instruction information is used to indicate the area information to which the first data applies.

Based on the above scheme, the first information sent by the second communication device to the first communication device may also include at least one of the above items, so that the first communication device obtains the first data based on the at least one of the above items.

In one possible implementation of the second aspect, the second information includes any of the following:

The fourth instruction information is used to indicate the result of the processing;

The fifth instruction information is used to indicate some or all of the AI models in the one or more AI models;

The sixth instruction information is used to indicate the AI-enabled functions supported by the first communication device.

Based on the above scheme, the second information sent by the first communication device to the second communication device may include any of the above, so that the second communication device can determine the AI model or AI-enabled function corresponding to the first data in a variety of ways.

In one possible implementation of the second aspect, the method further includes: the second communication device sending third information, the third information being used to indicate auxiliary information corresponding to the one or more AI models, the auxiliary information being used to indicate at least one of the following: model function, model structure parameters, data format of model input, and data format of model output.

Based on the above scheme, the second communication device can also send third information to the first communication device, so that the first communication device can determine the auxiliary information corresponding to one or more AI models in the first communication device through the received third information, so that the first communication device can obtain an AI model or AI-enabled function that matches the auxiliary information based on the auxiliary information.

A third aspect of this application provides a communication device, which is a first communication device, comprising a transceiver unit and a processing unit; the transceiver unit is used to receive first information, which is used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication device; the processing unit determines second information based on the processing result, and the transceiver unit is further used to send the second information; wherein the second information is used to determine some or all of the AI models in the one or more AI models, or the second information is used to determine the AI-enabled functions supported by the first communication device.

In the third aspect of this application, the constituent modules of the communication device can also be used to execute the steps performed in various possible implementations of the first aspect and achieve the corresponding technical effects. For details, please refer to the first aspect, which will not be repeated here.

A fourth aspect of this application provides a communication device, which is a second communication device. The communication device includes a transceiver unit and a processing unit. The processing unit is used to determine first information. The transceiver unit is used to transmit the first information, which is used to indicate first data. The first data is used to obtain a processing result through processing by one or more AI models of the first communication device. The transceiver unit is also used to receive second information, which is determined based on the processing result. The second information is used to determine some or all of the AI models in the one or more AI models, or the second information is used to determine the AI-enabled functions supported by the first communication device.

In the fourth aspect of this application, the constituent modules of the communication device can also be used to perform the steps executed in various possible implementations of the second aspect and achieve the corresponding technical effects. For details, please refer to the second aspect, which will not be repeated here.

A fifth aspect of this application provides a communication device including at least one processor coupled to a memory; the memory is used to store a program or instructions; the at least one processor is used to execute the program or instructions to enable the communication device to implement the method described in any possible implementation of any of the first to second aspects. Optionally, the communication device may include the memory.

The sixth aspect of this application provides a communication device including at least one logic circuit and an input/output interface; the logic circuit is used to perform the method as described in any one of the possible implementations of the first to second aspects described above.

The seventh aspect of this application provides a communication system, which includes the first communication device and the second communication device described above.

An eighth aspect of this application provides a computer-readable storage medium for storing one or more computer-executable instructions, which, when executed by a processor, perform the method as described in any possible implementation of any of the first to second aspects described above.

The ninth aspect of this application provides a computer program product (or computer program) that, when executed by a processor, performs the method described in any possible implementation of any of the first to second aspects described above.

The tenth aspect of this application provides a chip system including at least one processor for supporting a communication device in implementing the method described in any possible implementation of any of the first to second aspects.

In one possible design, the chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of chips or may include chips and other discrete devices. Optionally, the chip system may also include interface circuitry that provides program instructions and/or data to the at least one processor.

The technical effects of any of the design methods in aspects three through ten can be found in the technical effects of the different design methods in aspects one through two above, and will not be repeated here.

Attached Figure Description

Figures 1a to 1c are schematic diagrams of the communication system provided in this application;

Figures 2a to 2e are schematic diagrams of the AI processing involved in this application;

Figure 3 is an interactive schematic diagram of the communication method provided in this application;

Figures 4 to 8 are schematic diagrams of the communication device provided in this application.

Detailed Implementation

First, some terms used in the embodiments of this application will be explained to facilitate understanding by those skilled in the art.

(1) Terminal device: can be a wireless terminal device that can receive network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and/or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.

Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN). Terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and/or data with the RAN. Examples include personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablets, and computers with wireless transceiver capabilities. Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station (MS), remote station, access point (AP), remote terminal, access terminal, user terminal, user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices or smart 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, 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 speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

Terminals can also be drones, robots, devices in device-to-device (D2D) communication, vehicles to everything (V2X) communication, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in telemedicine or telehealth services, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, and wireless terminals in smart homes, etc.

Furthermore, terminal devices can also be terminal devices in communication systems evolved from fifth-generation (5G) communication systems (such as 5G Advanced or sixth-generation (6G) communication systems), or terminal devices in future public land mobile networks (PLMNs). For example, 5G Advanced or 6G networks can further expand the form and function of 5G communication terminals; 6G terminals include, but are not limited to, vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.

In this embodiment, the terminal device can also obtain artificial intelligence (AI) services provided by the network device. Optionally, the terminal device can also have AI processing capabilities.

(2) Network equipment: This can be equipment in a wireless network. For example, network equipment can be a RAN node (or device) that connects terminal devices to the wireless network, and can also be called a base station. Currently, some examples of RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in 5G communication systems, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point (AP), etc. In addition, in a network structure, network equipment can include central unit (CU) nodes, distributed unit (DU) nodes, or RAN equipment including CU nodes and DU nodes.

Optionally, RAN nodes can also be macro base stations, micro base stations or indoor stations, relay nodes or donor nodes, or radio controllers in cloud radio access network (CRAN) scenarios. RAN nodes can also be servers, wearable devices, vehicles, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

In another possible scenario, multiple RAN nodes collaborate to assist the terminal 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, CUs (control plane, CP), CUs (user plane, UP), or radio units (RUs). CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), radio heads (RHs), or remote radio heads (RRHs).

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 open access network (open RAN, O-RAN, or 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. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. 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.

Communication between access network devices and terminal devices follows a specific protocol layer structure. This protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc. The user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.

The correspondence between network elements and their achievable protocol layer functions in the ORAN system can be found in Table 1 below.

Table 1

Network devices can be other devices that provide wireless communication functions for terminal devices. The embodiments of this application do not limit the specific technology or form of the network device. For ease of description, the embodiments of this application are not limited.

Network equipment may also include core network equipment, such as the Mobility Management Entity (MME), Home Subscriber Server (HSS), Serving Gateway (S-GW), Policy and Charging Rules Function (PCRF), Public Data Network Gateway (PDN Gateway, or P-GW) in 4G networks; and access and mobility management function (AMF), user plane function (UPF), or session management function (SMF) in 5G networks. Furthermore, this core network equipment may also include other core network equipment in 5G networks and next-generation networks of 5G networks.

In this embodiment of the application, the network device may also have network nodes with AI capabilities, which can provide AI services to terminals or other network devices. For example, it may be an AI node, computing node, RAN node with AI capabilities, or core network element with AI capabilities on the network side (access network or core network).

In this application embodiment, the device for implementing the function of the network device can be the network device itself, or it can be a device capable of supporting the network device in implementing that function, such as a chip system, which can be installed in the network device. In the technical solutions provided in this application embodiment, the example of a network device being used to implement the function of the network device is used to describe the technical solutions provided in this application embodiment.

(3) Configuration and Pre-configuration: In this application, both configuration and pre-configuration are used. Configuration refers to the network device/server sending configuration information or parameter values to the terminal via messages or signaling, so that the terminal can determine communication parameters or resources for transmission based on these values or information. Pre-configuration is similar to configuration; it can be parameter information or parameter values pre-negotiated between the network device/server and the terminal device, parameter information or parameter values specified by standard protocols for use by the base station/network device or terminal device, or parameter information or parameter values pre-stored in the base station/server or terminal device. This application does not limit this.

Furthermore, these values and parameters can be changed or updated.

(4) The terms "system" and "network" in the embodiments of this application can be used interchangeably. "Multiple" refers to two or more. "And/or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and/or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character "/" generally indicates that the related objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. And, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of this application are used to distinguish multiple objects and are not used to limit the order, sequence, priority or importance of multiple objects.

(5) In the embodiments of this application, "send" and "receive" indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which may include sending directly through the air interface or sending indirectly through the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which may include receiving directly from YY through the air interface or receiving indirectly from YY through the air interface by 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.

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.

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.

(6) In the embodiments of this application, "instruction" may include direct instruction and indirect instruction, as well as explicit instruction and implicit instruction. The information indicated by a certain piece of information (as described below, the instruction information) is called the information to be instructed. In the specific implementation process, there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is an association between the other information and the information to be instructed; or it can only indicate a part of the information to be instructed, while the other parts of the information to be instructed are known or pre-agreed upon. For example, the instruction can be implemented by using a pre-agreed (e.g., protocol predefined) arrangement order of various information, thereby reducing the instruction overhead to a certain extent. This application does not limit the specific method of instruction. It is understood that for the sender of the instruction information, the instruction information can be used to indicate the information to be instructed, and for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.

In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments of this application, and the various methods/designs/implementations within each embodiment, unless otherwise specified or logically conflicting, the terminology and/or descriptions between different embodiments and between the various methods/designs/implementations within each embodiment are consistent and can be mutually referenced. The technical features in different embodiments and the various methods/designs/implementations within each embodiment can be combined to form new embodiments, methods, or implementations based on their inherent logical relationships. The following descriptions of the embodiments of this application do not constitute a limitation on the scope of protection of this application.

This application can be applied to long-term evolution (LTE) systems, new radio (NR) systems, or communication systems evolving after 5G (such as 6G). The communication system includes at least one network device and/or at least one terminal device.

Please refer to Figure 1a, which is a schematic diagram of a communication system according to this application. Figure 1a exemplarily shows one network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in Figure 1a, terminal device 1 is a smart teacup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas pump, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

As shown in Figure 1a, the entity sending the AI configuration information can be a network device. The entity receiving the AI configuration information can be terminal devices 1-6. In this case, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 can send data to the network device, and the network device needs to receive the data sent by terminal devices 1-6. At the same time, the network device can send configuration information to terminal devices 1-6.

For example, in Figure 1a, terminal devices 4 and 6 can also form a communication system. Terminal device 5 acts as a network device, i.e., the entity sending AI configuration information; terminal devices 4 and 6 act as terminal devices, i.e., the entities receiving AI configuration information. For instance, in a vehicle-to-everything (V2X) system, terminal device 5 sends AI configuration information to terminal devices 4 and 6 respectively, and receives data sent by terminal devices 4 and 6; correspondingly, terminal devices 4 and 6 receive the AI configuration information sent by terminal device 5 and send data back to terminal device 5.

Taking the communication system shown in Figure 1a as an example, in addition to performing communication-related services, different devices (including network devices and network devices, network devices and terminal devices, and/or terminal devices and terminal devices) may also perform AI-related services.

As shown in Figure 1b, taking a network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

As shown in Figure 1c, taking terminal devices including televisions and mobile phones as an example, communication-related services and AI-related services can also be performed between televisions and mobile phones.

The technical solutions provided in this application can be applied to wireless communication systems (such as the systems shown in Figures 1a, 1b, or 1c). For example, AI network elements can be introduced into the communication system provided in this application to implement some or all AI-related operations. AI network elements can also be called AI nodes, AI devices, AI entities, AI modules, AI models, or AI units, etc. The AI network element can be built into a network element within the communication system. For example, the AI network element can be an AI module built into: access network equipment, core network equipment, cloud server, or operation, administration, and maintenance (OAM) to implement AI-related functions. The OAM can act as the network management system for the core network equipment and/or the access network equipment. Alternatively, the AI network element can also be an independently set network element in the communication system. Optionally, the terminal or its built-in chip can also include an AI entity to implement AI-related functions.

The following is a brief introduction to the AI that may be involved in this application.

AI can endow machines with human-like intelligence, for example, allowing them to use computer hardware and software to simulate certain intelligent human behaviors. To achieve artificial intelligence, machine learning methods can be employed. In machine learning, machines learn (or train) a model using training data. This model represents the mapping between inputs and outputs. The learned model can be used for reasoning (or prediction), that is, it can be used to predict the output corresponding to a given input. This output can also be called the reasoning result (or prediction result).

Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning can also be called learning without supervision.

Supervised learning, based on collected sample values and labels, uses machine learning algorithms to learn the mapping relationship between sample values and labels, and then expresses this learned mapping relationship using an AI model. The process of training the machine learning model is the process of learning this mapping relationship. During training, sample values are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values and the sample labels (ideal values). After the mapping relationship is learned, it can be used to predict new sample labels. The mapping relationship learned in supervised learning can include linear or non-linear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

Unsupervised learning relies on collected sample values to discover inherent patterns within the samples themselves. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this 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.

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 a better (e.g., 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.

Neural networks (NNs) are a specific model in machine learning techniques. 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 deep learning communication systems based on neural networks can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

The idea behind neural networks comes from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values and outputs the result through an activation function.

Figure 2a shows a schematic diagram of a neuron structure. Assume the neuron's input is x = [ _x0 , _x1 , ..., _xn ], and the corresponding weights for each input are w = [ _w0 , _w1 , ..., _wn ], where n is a positive integer, and w _{_i} and _xi can be decimals, integers (e.g., 0, positive integers, or negative integers), or complex numbers, etc. _{w_i} is used as the weight for _xi , and is used to weight _xi . The bias for the weighted summation of the input values based on the weights is, for example, b. The activation function can take many forms. Assuming a neuron's activation function is y = f(z) = max(0, z), then the neuron's output is: For example, if the activation function of a neuron is y = f(z) = z, then the output of that neuron is: Here, b can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

Furthermore, neural networks generally consist of multiple layers, each of which may include one or more neurons. Increasing the depth and/or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it includes, and the number of neurons in each layer can be called the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing result to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, hidden layers, and an output layer. The input layer processes the received input information through neurons and passes the processing result to the hidden layer. The hidden layer calculates the received processing result and passes the calculation result to the output layer or the next adjacent hidden layer, ultimately obtaining the output of the neural network. A neural network may include one hidden layer or multiple sequentially connected hidden layers, without limitation.

Neural networks, for example, are deep neural networks (DNNs). Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

Figure 2b is a schematic diagram of an FNN network. A characteristic of FNN networks is that neurons in adjacent layers are completely connected pairwise. This characteristic makes FNNs typically require a large amount of storage space, leading to high computational complexity.

CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (e.g., discrete sampling along a time axis) and image data (e.g., 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 (e.g., people and objects in an image represent different types of information), each window can use different convolution kernels, allowing CNNs to better extract features from the input data.

Recurrent Neural Networks (RNNs) are a type of distributed neural network (DNN) that utilizes feedback time-series information. The input to an RNN includes the current input value and its 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.

In the model training process described above, a loss function can be defined. The loss function describes the difference between the model's output value and the ideal target value. The loss function can be expressed in various forms, and there are no restrictions on its specific form. The model training process can be viewed as follows: by adjusting some or all of the model's parameters, the value of the loss function is made to be less than a threshold or to meet the target requirement.

A model can also be called an AI model, a rule, or other names. An AI model can be considered a specific method for implementing AI functions. An AI model represents the mapping relationship or function between the model's input and output. AI functions can include one or more of the following: data collection, model training (or model learning), model information dissemination, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model validation, or inference result publication, etc. AI functions can also be called AI (related) operations or AI-related functions.

The implementation process of a fully connected neural network will be described below with reference to the accompanying drawings. A fully connected neural network is also called a multilayer perceptron (MLP).

As shown in Figure 2c, an MLP consists of an input layer (left side), an output layer (right side), and multiple hidden layers (middle). Each layer of an MLP contains several nodes, called neurons. Neurons in adjacent layers are connected pairwise.

Optionally, considering neurons in two adjacent layers, the output h of a neuron in the next layer is the weighted sum of all neurons x in the previous layer connected to it, after passing through an activation function, and can be expressed as:
h = f(wx + b).

Where w is the weight matrix, b is the bias vector, and f is the activation function.

Alternatively, the output of the neural network can be recursively expressed as:
y=f _n (w _n f _n-1 (…)+b _n ).

Where n is the index of the neural network layer, n is greater than or equal to 1 and less than or equal to N, where N is the total number of layers in the neural network.

In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly; the process of obtaining this mapping from random values w and b using existing data is called training the neural network.

Optionally, the training method involves using a loss function to evaluate the output of the neural network.

As shown in Figure 2d, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the loss function reaches its minimum, which is the "better point (e.g., the optimal point)" in Figure 2d. It can be understood that the neural network parameters corresponding to the "better point (e.g., the optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.

Alternatively, the gradient descent process can be represented as:

Where θ represents the parameters to be optimized (including w and b), L is the loss function, and η is the learning rate, controlling the step size of gradient descent. This represents the differentiation operation. This indicates taking the derivative of θ with respect to L.

Alternatively, the backpropagation process can utilize the chain rule for partial derivatives.

As shown in Figure 2e, the gradient of the parameters in the previous layer can be recursively calculated from the gradient of the parameters in the next layer, and can be expressed as:

Where w _{_ij} is the weight connecting node j to node i, and s _{_i} is the weighted sum of the inputs at node i.

The technical solution provided in this application can be applied to wireless communication systems (such as the systems shown in Figure 1a, 1b, or 1c). In wireless communication systems, communication nodes generally possess signal transmission and reception capabilities as well as computing capabilities. Taking a network device with computing capabilities as an example, the computing capabilities of the network device mainly provide computational support for the signal transmission and reception capabilities (e.g., processing the transmission and reception of signals) to realize the communication tasks between the network device and other communication nodes.

The technical solution provided in this application can be applied to wireless communication systems (such as the systems shown in Figure 1a or Figure 1b). In wireless communication systems, communication nodes generally possess signal transmission and reception capabilities as well as computing capabilities. Taking a network device with computing capabilities as an example, the computing capabilities of the network device mainly provide computational support for signal transmission and reception capabilities (e.g., processing signals for transmission and reception) to realize the communication tasks between the network device and other communication nodes. Furthermore, in addition to processing communication signals in the communication network, the communication device may also handle other communication tasks (such as channel prediction, beam management, resource scheduling, etc.).

However, in communication networks, communication nodes may possess surplus computing power beyond simply providing computational support for the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical problem that needs to be solved.

In one possible implementation, communication devices can act as participating nodes in an AI learning system, applying their computing power to a specific stage of the learning process. Generally, AI functionalities introduced into wireless networks rely on AI models. Taking communication devices, including terminal and network devices, as an example, consensus needs to be reached between them regarding the models or model pairs required to implement a particular AI-enabled function. In this case, both parties need to pre-identify existing models to facilitate subsequent calling and maintenance.

Furthermore, when communication devices can serve as participating nodes in an AI learning system, a single communication device may store or deploy one or more AI models. The AI models stored (or deployed) by different communication devices may not be entirely identical (or, a single communication device may provide one or more AI-enabled functions, and the AI-enabled functions provided by different communication devices may not be entirely identical). Therefore, how to implement instructions for AI models (or AI-enabled functions) across different communication devices remains a problem that no solution has yet been found.

To address the aforementioned problems, this application provides a communication method and related apparatus, which will be described in detail below with reference to the accompanying drawings.

Please refer to Figure 3, which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

It should be noted that, in the following text, Figure 3 uses the first communication device and other communication devices (such as the second communication device) as examples to illustrate the method in this interactive illustration, but this application does not limit the execution subject of this interactive illustration. For example, the communication device can be a communication device (such as a terminal device or a network device), or a chip, baseband chip, modem chip, system-on-chip (SoC) chip containing a modem core, system-in-package (SIP) chip, communication module, chip system, processor, logic module, or software in the communication device.

S301. The second communication device sends first information, and correspondingly, the first communication device receives the first information. The first information is used to indicate first data; the first data is used to obtain a processing result through processing by one or more AI models of the first communication device.

S302. The first communication device sends second information, and correspondingly, the second communication device receives the second information. The second information is determined based on the processing result, and is used to determine some or all of the AI models in the one or more AI models, or to determine the AI-enabled functions supported by the first communication device.

In this application, AI model may be replaced with other terms, such as neural network, neural network model, AI neural network model, machine learning model, or AI processing model, etc.

In this application, the term "AI-enabled function" can be replaced with other terms, such as AI-enabled features, AI capabilities, or AI functions.

It should be noted that in step S301, the first data indicated by the first information can be implemented in a variety of ways, which will be explained below with some implementation examples.

In Example 1, the first information indicates that the first data includes the first training data.

In Example 1, the first training data can be used for model training of one or more AI models of the first communication device. Specifically, the second communication device instructs the first communication device to provide specific training data (i.e., the first training data) via first information, enabling the first communication device to train the model based on this specific training data. Correspondingly, the processing result obtained by the first communication device based on the first training data can be obtained by testing the trained model using pre-configured test data after training the model based on the first training data. Thus, in step S302, the first communication device can instruct the second communication device to provide the AI model or AI-enabled function corresponding to the model training process implemented by the first training data.

In Example 2, the first data indicated by the first information includes the first test data.

In Example 2, the first test data is used for model testing of one or more AI models of the first communication device. Specifically, the second communication device instructs the first communication device to provide specific test data (i.e., the first test data) via first information, so that the first communication device can perform model testing based on this specific test data. Correspondingly, the processing result obtained by the first communication device based on the first training data is obtained by training a model using pre-configured training data, followed by model testing of the trained model using the first test data. Thus, in step S302, the first communication device can instruct the second communication device to provide the AI model or AI-enabled function corresponding to the model testing process implemented by the first test data.

In Example 3, the first information indicates that the first data includes the second training data and the second test data.

In Example 3, the second training data is used for model training of one or more AI models of the first communication device, and the second test data is used for model testing of the one or more AI models. The processing result is obtained by training the model using the second training data and then testing the trained model using the second test data. Specifically, the second communication device instructs the first communication device to provide specific training data (i.e., the second training data) and specific test data (i.e., the second test data) via first information, so that the first communication device can perform model training based on the specific training data and model testing based on the specific test data. Thus, in step S302, the first communication device can instruct the second communication device to provide the AI model or AI-enabled function corresponding to the model training process implemented by the second training data and the model testing process implemented by the second test data.

For example, training data and/or test data can be data corresponding to an AI model.

For example, if a certain AI model of the first communication device is an AI model for modulation and/or demodulation (the AI model may be called a smart modulation model, a smart demodulation model, or a smart modulation and demodulation model, etc.), the training data and/or test data for the AI model may include one or more of the following: modulation and coding scheme (MCS), frequency domain resource indicator, transmit power control command (TPC command), and transmitted precoding matrix indicator (TPMI).

For example, if a certain AI model of the first communication device is an AI model for channel prediction (the AI model may be called a channel estimation model, channel prediction model, channel simulation recovery model, channel reconstruction model, channel acquisition model, or channel inference model, etc.), the training data and/or test data for the AI model may include one or more of the following: time domain configuration information, frequency domain configuration information, spatial domain configuration information, port information, periodic information, and codebook configuration information.

For example, if a certain AI model of the first communication device is an AI model for beam management (which may be called a beam management model or a beam optimization model, etc.), the training data and/or test data for the AI model may include: channel characteristic information of the cell and information on the number of beams associated with the AI model for beam management.

It should be understood that after the first communication device determines the first data based on the first information in step S301, the first communication device can obtain a processing result based on the processing of one or more AI models of the first communication device; thereafter, the first communication device can obtain second information based on the processing result, the second information being used to determine some or all of the AI models in the one or more AI models, or the second information being used to determine the AI-enabled functions supported by the first communication device. Based on the above implementation examples one to three, it can be seen that the first data and the AI models or the AI-enabled functions can be correlated.

As an example, in Implementation Example 1, it can be understood that the second communication device instructs the first communication device to provide specific training data, enabling the first communication device to train based on the specific training data. Through this training process, an AI model or AI-enabled function matching the first training data is obtained. The AI model or AI-enabled function matching the first training data can mean that the AI model or AI-enabled function has the ability to process the first training data (or data that is the same as or similar to the first training data).

As another example, in Implementation Example 2, it can be understood that the second communication device instructs the first communication device to provide specific test data, enabling the first communication device to perform tests based on the specific test data. Through this testing process, an AI model or AI-enabled function that matches the first test data is determined (or selected, filtered, etc.). The AI model or AI-enabled function that matches the first test data can be one whose processing performance on the first test data (or data that is the same as or similar to the first training data) is superior (or higher, or exceeds a threshold, etc.).

As another example, in Implementation Example 3, it can be understood that the second communication device instructs the first communication device to provide specific training data and specific test data, enabling the first communication device to train based on the specific training data and test based on the specific test data. Through this training and testing process, an AI model or AI-enabled function corresponding to the second training data and the second test data is obtained and determined (or selected, filtered, etc.). The AI model or AI-enabled function corresponding to the second training data and the second test data can be that the AI model or AI-enabled function has the ability to process the second training data (or data that is the same as or similar to the second training data), and that the processing performance of the AI model or AI-enabled function on the first test data (or data that is the same as or similar to the first training data) is superior (or higher, or above a threshold, etc.).

Based on the scheme shown in Figure 3, the first communication device can process one or more AI models of the first communication device in step S302 based on the first data indicated by the second communication device to obtain a processing result. Subsequently, the first communication device can send second information based on the processing result, enabling the second communication device to determine some or all of the AI models in the one or more AI models (or, determine the AI-enabled functions supported by the first communication device) based on the second information. In other words, after the second communication device indicates the first data to the first communication device, the first communication device can indicate the AI model or AI-enabled function corresponding to the first data to the second communication device. In this way, communication devices in the communication system can participate in the processing of AI models and provide the processing capability of AI models corresponding to specified data or provide AI-enabled functions corresponding to specified data.

In one possible implementation of the method shown in Figure 3, the first information received by the first communication device in step S301 includes configuration information for collecting some or all of the data in the first data, and/or, the first information includes some or all of the data in the first data. The first information received by the first communication device may contain one or more of the above-mentioned information content, allowing the first communication device to obtain the first data in multiple ways.

As an example, when the first information includes configuration information for collecting some or all of the data in the first data, the configuration information may include configuration information for the resources (e.g., time-domain resources, frequency-domain resources, etc.) for collecting that part or all of the data. Accordingly, the first communication device can collect the data based on the configuration information to obtain that part or all of the data.

As another example, when the first information includes part or all of the first data, the first communication device can obtain part or all of the first data based on the first information.

Optionally, when the first information includes part or all of the first data, the first information may also include data composition information of the part or all of the data (e.g., one or more of data size, data format, and data type), in such a way that the first communication device can obtain the part or all of the data from the first information based on the data composition information.

Optionally, the first information may also include at least one of the following:

The first indication information is used to indicate the scenario corresponding to the first data;

The second instruction information is used to indicate the preprocessing rules corresponding to the first data;

The third instruction information is used to indicate the area information to which the first data applies.

Therefore, the first information received by the first communication device may also include at least one of the above-mentioned items, enabling the first communication device to obtain first data based on at least one of the above-mentioned items. The following will provide exemplary descriptions of the various indications included in the first information through some examples.

Example A: When the first information includes first indication information, the first communication device may, based on the first indication information, use the data corresponding to the scenario indicated by the first indication information as the first data.

For example, in Example A, the scenario indicated by the first indication information may include one or more of the following: indoor scenario, outdoor scenario, line of sight (LOS) scenario, non-line of sight (NLOS) scenario, terrestrial network (TN) scenario, and non-terrestrial network (NTN) scenario.

Optionally, if the first data includes training data (such as the first training data, second training data, etc. described above), the above example A can be understood as the second communication device instructing the first communication device on the training data of a specific scenario through the first information, so that the first communication device obtains an AI model (or AI-enabled function) adapted to the specific scenario based on the training data of the specific scenario.

Optionally, when the first data includes test data (such as the first test data, second test data, etc. described above), the above example A can be understood as the second communication device instructing the first communication device on the test data of a specific scenario through the first information, so that the first communication device determines (or selects, filters, etc.) an AI model (or AI-enabled function) that is suitable for the specific scenario based on the test data of the specific scenario.

Example B: When the first information includes second indication information, the first communication device can perform data processing based on the second indication information and the preprocessing rules indicated by the second indication information to obtain the first data.

For example, in Example B, the preprocessing rules indicated by the second instruction information may include one or more of the following: data augmentation, data filtering, data cleaning, data denoising, and data augmentation based on sample data.

In one implementation example, taking the above preprocessing rule as including data augmentation based on sample data as an example, the sample data can be channel data of one or more time units. The first communication device can augment the sample data based on the preprocessing rule, and the channel data of other time units obtained can be part or all of the first data.

In another implementation example, taking the above preprocessing rule including data noise reduction as an example, the first communication device can add noise to the collected (or configured) channel data based on the preprocessing rule, and the resulting noise-added channel data can be used as part or all of the first data.

Example C: When the first information includes third indication information, the first communication device may use the data corresponding to the area information indicated by the third indication information as the first data.

For example, in Example C, the applicable regional information indicated by the third indication information may include one or more of the following: the location coordinates of the geographical region, latitude and longitude information, and altitude information.

Similarly, when the first data includes training data (such as the first training data, second training data, etc. described above), the above example C can be understood as the second communication device instructing the first communication device on the training data of the region information through the first information, so that the first communication device can obtain an AI model (or AI-enabled function) adapted to the specific region based on the training data of the specific region.

Similarly, when the first data includes test data (such as the first test data, second test data, etc. described above), the above example C can be understood as the second communication device instructing the first communication device on the test data of a specific area through the first information, so that the first communication device determines (or selects, filters, etc.) an AI model (or AI-enabled function) that is suitable for the specific area based on the test data of the specific area.

In one possible implementation of the method shown in Figure 3, the second information sent by the first communication device in step S302 includes any of the following:

The fourth instruction information is used to indicate the result of the processing;

The fifth instruction information is used to indicate some or all of the AI models in the one or more AI models;

The sixth instruction information is used to indicate the AI-enabled functions supported by the first communication device.

Therefore, the second information sent by the first communication device may include any of the above-mentioned items, enabling the second communication device to determine the AI model or AI-enabled function corresponding to the first data in various ways. The following examples will exemplarily describe the various indications included in the second information.

In Method 1, when the second information includes the fourth indication information, the second communication device can determine some or all of the AI models in one or more AI models in the first communication device based on the processing result indicated by the fourth indication information; or, the second communication device can determine the AI-enabled functions supported by the first communication device based on the processing result indicated by the fourth indication information. Since the first communication device can directly indicate the processing result through the second information after obtaining the processing result through processing of one or more AI models, the processing complexity of the first communication device can be reduced.

Method 2: When the second information includes the fifth indication information, the second communication device can, based on the fifth indication information, determine some or all of the AI models among one or more AI models deployed (or stored, existing) by the first communication device that match the first data. Subsequently, when the second communication device executes an AI task associated with the first data, it can schedule some or all of the AI models based on the fifth indication information to improve the performance of the AI task.

Method 3: When the second information includes the sixth indication information, the second communication device can determine, based on the sixth indication information, the AI-enabled function among one or more processing capabilities provided by the first communication device that matches the first data. Subsequently, when the second communication device executes an AI task associated with the first data, it can schedule the AI-enabled function based on the sixth indication information to improve the performance of the AI task.

In one possible implementation of the method shown in Figure 3, the method further includes: the first communication device receiving third information, which indicates auxiliary information corresponding to the one or more AI models. This auxiliary information indicates at least one of the following: model function, model structural parameters, model input data format, and model output data format. In other words, the first communication device can also determine the auxiliary information corresponding to one or more AI models within the first communication device through the received third information, enabling the first communication device to obtain an AI model or AI-enabled function matching the auxiliary information based on the received third information.

It should be understood that when the auxiliary information indicates the model function, it can be understood that the auxiliary information is used to indicate the target model, that is, the first communication device can obtain the target model with the specific model function based on the auxiliary information (or the first communication device can obtain the same or similar AI enabling function as the specific model function based on the auxiliary information).

It should be understood that when the auxiliary information indicates at least one of the following: model structure parameters, model input data format (denoted as Format 1), and model output data format (denoted as Format 2), it can be understood that the auxiliary information is used to indicate the reference model. That is, the first communication device can determine, based on the auxiliary information, at least one of the following: a specific model structure of the reference model, a model input conforming to Format 1, or a model output conforming to Format 2. Accordingly, the first communication device can obtain an AI model that is the same as or similar to the reference model (or, the first communication device can obtain the AI enabling functions provided by an AI model that is the same as or similar to the reference model).

Optionally, the first and third information can be carried in the same message or in different messages, without limitation. For example, when the first communication device is a terminal device and the second communication device is a network device, the message may include an RRC message, downlink control information (DCI), or a media access control element (MAC CE), or other messages.

Referring to Figure 4, this application embodiment provides a communication device 400. This communication device 400 can implement the functions of the first communication device (or the second communication device) in the above method embodiments, and therefore can also achieve the beneficial effects of the above method embodiments. In this application embodiment, the communication device 400 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip, baseband chip, modem chip, SoC chip containing a modem core, system-in-package (SIP) chip, communication module, chip system, processor, etc.

It should be noted that the transceiver unit 402 may include a transmitting unit and a receiving unit, which are used to perform transmitting and receiving respectively.

In one possible implementation, when the device 400 is used to execute the method performed by the first communication device in FIG3 and related embodiments, the device 400 includes a processing unit 401 and a transceiver unit 402; the transceiver unit 402 is used to receive first information, which is used to indicate first data; wherein, the first data is used to obtain a processing result through processing of one or more AI models of the first communication device; the processing unit 401 determines second information based on the processing result, and the transceiver unit 402 is also used to send the second information; wherein, the second information is used to determine some or all of the AI models in the one or more AI models, or, the second information is used to determine the AI-enabled functions supported by the first communication device.

In one possible implementation, when the device 400 is used to execute the method performed by the second communication device in FIG3 and related embodiments, the device 400 includes a processing unit 401 and a transceiver unit 402; the processing unit 401 is used to determine first information; the transceiver unit 402 is used to send the first information, which is used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication device; the transceiver unit 402 is also used to receive second information, which is determined based on the processing result; wherein the second information is used to determine some or all of the AI models in the one or more AI models, or the second information is used to determine the AI-enabled functions supported by the first communication device.

In one possible design, when the communication device 400 is a terminal device or a communication module within a terminal, the function of the processing unit 401 can be implemented by one or more processors. Specifically, the processor may include a modem chip, or a SoC chip or SIP chip containing a modem core. The function of the transceiver unit 402 can be implemented by transceiver circuitry.

In one possible design, when the communication device 400 is a circuit or chip in a terminal responsible for communication functions, such as a modem chip or a SoC chip or SIP chip containing a modem core, the function of the processing unit 401 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processor cores. The function of the transceiver unit 402 can be implemented by the interface circuit or data transceiver circuit on the aforementioned chip.

It should be noted that the information execution process of the unit of the above-mentioned communication device 400 can be specifically described in the method embodiment shown above in this application, and will not be repeated here.

Please refer to Figure 5, which is another schematic structural diagram of the communication device 500 provided in this application. The communication device 500 includes a logic circuit 501 and an input/output interface 502. The communication device 500 can be a chip or an integrated circuit.

In Figure 4, the transceiver unit 402 can be a communication interface, which can be the input/output interface 502 in Figure 5, and the input/output interface 502 can include an input interface and an output interface. Alternatively, the communication interface can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

In one possible implementation, when the device 500 is used to execute the method performed by the first communication device in FIG3 and related embodiments, the input/output interface 502 is used to receive first information, which is used to indicate first data; wherein, the first data is used to obtain a processing result through processing of one or more AI models of the first communication device; the logic circuit 501 determines second information based on the processing result, and the input/output interface 502 is also used to send the second information; wherein, the second information is used to determine some or all of the AI models in the one or more AI models, or, the second information is used to determine the AI-enabled functions supported by the first communication device.

In one possible implementation, when the device 500 is used to execute the method performed by the second communication device in FIG3 and related embodiments, the logic circuit 501 is used to determine first information; the input/output interface 502 is used to send the first information, which is used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication device; the input/output interface 502 is also used to receive second information, which is determined based on the processing result; wherein the second information is used to determine some or all of the AI models in the one or more AI models, or the second information is used to determine the AI-enabled functions supported by the first communication device.

The logic circuit 501 and the input/output interface 502 can also perform other steps performed by the first or second communication device in any embodiment and achieve corresponding beneficial effects, which will not be elaborated here.

In one possible implementation, the processing unit 401 shown in FIG4 can be the logic circuit 501 in FIG5.

Optionally, the logic circuit 501 can be a processing device, the functions of which can be partially or entirely implemented in software.

Optionally, the processing apparatus may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform the corresponding processing and/or steps in any of the method embodiments.

Optionally, the processing device may consist of only a processor. A memory for storing computer programs is located outside the processing device, and the processor is connected to the memory via circuitry/wires to read and execute the computer programs stored in the memory. The memory and processor may be integrated together or physically independent of each other.

Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic controllers (PLDs), or other integrated chips, or any combination of the above chips or processors.

Please refer to Figure 6, which shows the communication device 600 involved in the above embodiments provided in the embodiments of this application. Specifically, the communication device 600 can be the communication device that serves as a terminal device in the above embodiments.

The present invention provides a possible logical structure diagram of the communication device 600, which may include, but is not limited to, at least one processor 601 and a communication port 602.

In Figure 4, the transceiver unit 402 can be a communication interface, which can be the communication port 602 in Figure 6. The communication port 602 can include an input interface and an output interface. Alternatively, the communication port 602 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

Further optionally, the device may also include at least one of a memory 603 and a bus 604. In the embodiments of this application, the at least one processor 601 is used to control the operation of the communication device 600.

Furthermore, processor 601 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

It should be noted that the communication device 600 shown in Figure 6 can be used to implement the steps implemented by the terminal device in the aforementioned method embodiments and achieve the corresponding technical effects of the terminal device. The specific implementation of the communication device shown in Figure 6 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.

Please refer to Figure 7, which is a schematic diagram of the structure of the communication device 700 involved in the above embodiments provided in the embodiments of this application. Specifically, the communication device 700 can be a communication device as a network device in the above embodiments.

The communication device 700 includes at least one processor 711 and at least one network interface 714. Optionally, the communication device further includes at least one memory 712, at least one transceiver 713, and one or more antennas 714. The processor 711, memory 712, transceiver 713, and network interface 714 are connected, for example, via a bus. In this embodiment, the connection may include various interfaces, transmission lines, or buses, etc., and this embodiment is not limited thereto. The antenna 715 is connected to the transceiver 713. The network interface 714 enables the communication device to communicate with other communication devices through a communication link. For example, the network interface 714 may include a network interface between the communication device and core network equipment, such as an S1 interface, or a network interface between the communication device and other communication devices (e.g., other network devices or core network equipment), such as an X2 or Xn interface.

In Figure 4, the transceiver unit 402 can be a communication interface, which can be the network interface 714 in Figure 7. The network interface 714 can include an input interface and an output interface. Alternatively, the network interface 714 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

The processor 711 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process data from these programs, for example, to support the actions described in the embodiments of the communication device. The communication device may include a baseband processor and a central processing unit (CPU). The baseband processor is primarily used to process communication protocols and communication data, while the CPU is primarily used to control the entire terminal device, execute software programs, and process data from these programs. The processor 711 in Figure 7 can integrate the functions of both a baseband processor and a CPU. Those skilled in the art will understand that the baseband processor and CPU can also be independent processors interconnected via technologies such as buses. Those skilled in the art will understand that a terminal device may include multiple baseband processors to adapt to different network standards, and multiple CPUs to enhance its processing capabilities. The various components of the terminal device can be connected via various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The CPU can also be described as a central processing circuit or a central processing chip. The function of processing communication protocols and communication data can be built into the processor or stored in memory as a software program, which is then executed by the processor to implement the baseband processing function.

The memory is primarily used to store software programs and data. The memory 712 can exist independently or be connected to the processor 711. Optionally, the memory 712 can be integrated with the processor 711, for example, integrated into a single chip. The memory 712 can store program code that executes the technical solutions of the embodiments of this application, and its execution is controlled by the processor 711. The various types of computer program code being executed can also be considered as drivers for the processor 711.

Figure 7 shows only one memory and one processor. In actual terminal devices, there may be multiple processors and multiple memories. Memory can also be called storage medium or storage device, etc. Memory can be a storage element on the same chip as the processor, i.e., an on-chip storage element, or it can be a separate storage element; this application does not limit this.

Transceiver 713 can be used to support the reception or transmission of radio frequency (RF) signals between a communication device and a terminal. Transceiver 713 can be connected to antenna 715. Transceiver 713 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 715 can receive RF signals. The receiver Rx of transceiver 713 receives the RF signals from the antennas, converts the RF signals into digital baseband signals or digital intermediate frequency (IF) signals, and provides the digital baseband signals or IF signals to processor 711 so that processor 711 can perform further processing on the digital baseband signals or IF signals, such as demodulation and decoding. Furthermore, the transmitter Tx in transceiver 713 is also used to receive modulated digital baseband signals or IF signals from processor 711, convert the modulated digital baseband signals or IF signals into RF signals, and transmit the RF signals through one or more antennas 715. Specifically, the receiver Rx can selectively perform one or more stages of downmixing and analog-to-digital conversion on the radio frequency signal to obtain a digital baseband signal or a digital intermediate frequency (IF) signal. The order of these downmixing and IF conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of upmixing and digital-to-analog conversion on the modulated digital baseband signal or digital IF signal to obtain a radio frequency signal. The order of these upmixing and IF conversion processes is also adjustable. The digital baseband signal and the digital IF signal can be collectively referred to as digital signals.

The transceiver 713 can also be called a transceiver unit, transceiver, transceiver device, etc. Optionally, the device in the transceiver unit that performs the receiving function can be regarded as the receiving unit, and the device in the transceiver unit that performs the transmitting function can be regarded as the transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit can also be called a receiver, input port, receiving circuit, etc., and the transmitting unit can be called a transmitter, transmitter, or transmitting circuit, etc.

It should be noted that the communication device 700 shown in Figure 7 can be used to implement the steps implemented by the network device in the aforementioned method embodiments and to achieve the corresponding technical effects of the network device. The specific implementation of the communication device 700 shown in Figure 7 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.

Please refer to Figure 8, which is a schematic diagram of the structure of the communication device involved in the above embodiments provided in the embodiments of this application.

It is understood that the communication device 800 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to execute the technical solutions provided in this application. The communication device 800 may be the terminal device or network device described above, or a component (e.g., a chip) within these devices, used to implement the methods described in the following method embodiments. The communication device 800 includes one or more processors 801. The processor 801 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (e.g., a RAN node, terminal, or chip), execute software programs, and process data from the software programs.

Optionally, in one design, processor 801 may include program 803 (sometimes also referred to as code or instructions), which may be executed on processor 801 to cause communication device 800 to perform the methods described in the embodiments below. In yet another possible design, communication device 800 includes circuitry (not shown in FIG8).

Optionally, the communication device 800 may include one or more memories 802 storing a program 804 (sometimes referred to as code or instructions), which can be run on the processor 801 to cause the communication device 800 to perform the methods described in the above method embodiments.

Optionally, the processor 801 and/or memory 802 may include AI modules 807 and 808, which are used to implement AI-related functions. The AI modules can be implemented through software, hardware, or a combination of both. For example, the AI module may include a radio intelligence control (RIC) module. For example, the AI module may be a near real-time RIC or a non-real-time RIC.

Optionally, the processor 801 and/or memory 802 may also store data. The processor and memory may be configured separately or integrated together.

Optionally, the communication device 800 may further include a transceiver 805 and/or an antenna 806. The processor 801, sometimes referred to as a processing unit, controls the communication device (e.g., a RAN node or terminal). The transceiver 805, sometimes referred to as a transceiver unit, transceiver, transceiver circuit, or transceiver, is used to implement the transmission and reception functions of the communication device through the antenna 806.

In this context, the processing unit 401 shown in Figure 4 can be a processor 801. The transceiver unit 402 shown in Figure 4 can be a communication interface, which can be the transceiver 805 in Figure 8. The transceiver 805 can include an input interface and an output interface. Alternatively, the transceiver 805 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

This application also provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor performs the method described in the possible implementations of the first or second communication device in the foregoing embodiments.

This application also provides a computer program product (or computer program) that, when executed by a processor, executes the method described above for the possible implementation of the first or second communication device.

This application also provides a chip system including at least one processor for supporting a communication device in implementing the functions involved in the possible implementations of the communication device described above. Optionally, the chip system further includes an interface circuit that provides program instructions and/or data to the at least one processor. In one possible design, the chip system may also include a memory for storing the program instructions and data necessary for the communication device. The chip system may be composed of chips or may include chips and other discrete devices, wherein the communication device may specifically be the first communication device or the second communication device in the aforementioned method embodiments.

This application also provides a communication system, the network system architecture of which includes the first communication device and/or the second communication device in any of the above embodiments.

In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms. Whether a function is implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims (21)

A communication method, characterized in that it includes: A first communication device receives first information, which is used to indicate first data; wherein the first data is used to obtain a processing result through processing by one or more artificial intelligence (AI) models of the first communication device. The first communication device sends second information, which is determined based on the processing result; wherein the second information is used to determine some or all of the AI models in the one or more AI models, or the second information is used to determine the AI-enabled functions supported by the first communication device. The method according to claim 1, wherein the first data includes first training data, the first training data being used for model training of the one or more AI models; The processing result is obtained by training the model based on the first training data, and then testing the trained model with pre-configured test data. The method according to claim 1, wherein the first data includes first test data, the first test data being used for model testing of the one or more AI models; The processing result is obtained by training the model based on pre-configured training data, and then testing the trained model using the first test data. The method according to claim 1, wherein the first data includes second training data and second test data, the second training data is used for model training of the one or more AI models, and the second test data is used for model testing of the one or more AI models; The processing result is obtained by training the model based on the second training data, and then testing the trained model using the second test data. The method according to any one of claims 1 to 4, wherein the first information includes configuration information for collecting some or all of the data in the first data, and/or the first information includes some or all of the data in the first data. The method according to claim 5, wherein the first information further includes at least one of the following: The first indication information is used to indicate the scenario corresponding to the first data; The second instruction information is used to indicate the preprocessing rules corresponding to the first data; The third indication information is used to indicate the area information to which the first data applies. The method according to any one of claims 1 to 6, wherein the second information includes any one of the following: The fourth indication information is used to indicate the processing result; The fifth indication information is used to indicate some or all of the AI models in the one or more AI models; The sixth instruction information is used to indicate the AI-enabled functions supported by the first communication device. The method according to any one of claims 1 to 7, characterized in that the method further comprises: The first communication device receives third information, which is used to indicate auxiliary information corresponding to the one or more AI models, and the auxiliary information is used to indicate at least one of the following: Model functionality, model structure parameters, model input data format, and model output data format. A communication method, characterized in that it includes: The second communication device sends first information, which is used to indicate first data; wherein the first data is used to obtain a processing result through processing by one or more artificial intelligence (AI) models of the first communication device. The second communication device receives second information, which is determined based on the processing result; wherein the second information is used to determine some or all of the AI models in the one or more AI models, or the second information is used to determine the AI-enabled functions supported by the first communication device. The method according to claim 9, wherein the first data includes first training data, the first training data being used for model training of the one or more AI models; The processing result is obtained by training the model based on the first training data, and then testing the trained model with pre-configured test data. The method according to claim 9, wherein the first data includes first test data, the first test data being used for model testing of the one or more AI models; The processing result is obtained by training the model based on pre-configured training data, and then testing the trained model using the first test data. The method according to claim 9, wherein the first data includes second training data and second test data, the second training data is used for model training of the one or more AI models, and the second test data is used for model testing of the one or more AI models; The processing result is obtained by training the model based on the second training data, and then testing the trained model using the second test data. The method according to any one of claims 9 to 12, wherein the first information includes configuration information for collecting some or all of the data in the first data, and/or the first information includes some or all of the data in the first data. The method according to claim 13, wherein the first information further comprises at least one of the following: The first indication information is used to indicate the scenario corresponding to the first data; The second instruction information is used to indicate the preprocessing rules corresponding to the first data; The third indication information is used to indicate the area information to which the first data applies. The method according to any one of claims 9 to 14, wherein the second information includes any one of the following: The fourth indication information is used to indicate the processing result; The fifth indication information is used to indicate some or all of the AI models in the one or more AI models; The sixth instruction information is used to indicate the AI-enabled functions supported by the first communication device. The method according to any one of claims 9 to 15, characterized in that the method further comprises: The second communication device sends third information, which is used to indicate auxiliary information corresponding to the one or more AI models, and the auxiliary information is used to indicate at least one of the following: Model functionality, model structure parameters, model input data format, and model output data format. A communication device, characterized in that it includes a module for performing the method as described in any one of claims 1 to 16. A communication device, characterized in that it includes at least one processor coupled to a memory; the at least one processor is configured to perform the method as described in any one of claims 1 to 16. The communication device according to claim 18 is characterized in that the communication device is a chip or a chip system. A readable storage medium, characterized in that the storage medium stores a computer program or instructions that, when executed by a communication device, implement the method as described in any one of claims 1 to 16. A computer program product, characterized in that, when the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1 to 16.