WO2025059908A1 - Communication method and related device - Google Patents

Abstract

The present application provides a communication method and a related device, which are used for enabling the computing power of a communication node to be applied to an AI model processing process in an artificial intelligence (AI) learning system, and achieving AI data feature matching-based AI model processing. In the method, a first node receives first information, the first information being used for determining AI data features corresponding to processing requirements of a first AI model; and when AI data features of local data meet the AI data features corresponding to the processing requirements of the first AI model, the first node processes the first AI model on the basis of the local data, so as to obtain a second AI model.

Description

A communication method and related equipment Technical Field

The present application relates to the field of communications, and in particular to a communication method and related equipment.

Background Art

Wireless communication can be the transmission communication between two or more communication nodes without propagation through conductors or cables. The communication nodes generally include network equipment and terminal equipment.

At present, in wireless communication systems, communication nodes generally have signal transceiver capabilities and computing capabilities. Taking network devices with computing capabilities as an example, the computing capabilities of network devices mainly provide computing support for signal transceiver capabilities (for example, calculating the time domain resources and frequency domain resources that carry the signal) to achieve communication between network devices and other communication nodes.

However, in a communication network, the computing power of communication nodes may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power is a technical problem that needs to be solved urgently.

Summary of the invention

The present application provides a communication method and related equipment for enabling the computing power of a communication node to be applied to an AI model processing process in an artificial intelligence (AI) learning system, and for realizing AI model processing based on AI data feature matching.

In a first aspect, the present application provides a communication method, which is executed by a first node, or the method is executed by some components in the first node (such as a processor, a chip or a chip system, etc.), or the method can also be implemented by a logic module or software that can realize all or part of the functions of the first node. In the first aspect and its possible implementation, the method is described as being executed by the first node. In this method, the first node receives first information, and the first information is used to determine the AI data features corresponding to the processing requirements of the first AI model; when the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, the first node processes the first AI model based on the local data to obtain a second AI model.

Based on the above technical solution, after the first node receives the first information, the first node can determine the AI data features corresponding to the processing requirements of the first AI model based on the first information. Thereafter, when the AI data features of the local data of the first node meet the AI data features corresponding to the processing requirements of the first AI model, the first node processes the first AI model based on the local data to obtain a second AI model. In other words, the first node acts as a communication node in the communication system, and the AI data features corresponding to the processing requirements of the first AI model processed by the first node match the AI data features of the local data of the first node. Thus, when the communication node in the communication system acts as a node participating in the AI model processing, the computing power of the communication node can be applied to the AI model processing process in the AI learning system, in order to realize the AI model processing process in the communication network.

In addition, the AI data features of the local data of the first node meet the AI data features corresponding to the processing requirements of the first AI model processed by the first node, so that the first node can process the AI model that matches the AI data features of the local data. Correspondingly, the AI model can also be processed by the node that meets the processing requirements, thereby realizing AI model processing based on AI data feature matching.

In this application, the AI model can be replaced by a neural network, an AI neural network, a neural network model, an AI neural network model, a machine learning model, etc.

It should be understood that the AI model involved in this application can be applied to AI tasks. In other words, the execution process of the AI task includes the processing of one or more AI models by the communication node. Among them, the AI task can be a task that requires two or more communication nodes to participate in the processing, and the communication node includes a terminal device and/or a network device. For example, the AI task can include a federated learning (FL) task, a distributed training task, a distributed learning task, etc.

Optionally, AI data may refer to data related to AI, and AI data features may be used to indicate features (or characteristics or traits or attributes or properties) of data related to AI. For example, the AI data feature may include at least one of the following: an identifier of an AI task to which the AI data is applied, an object to which the AI data belongs, geographic location information for collecting the AI data, time information for collecting the AI data, and the number of samples of the AI data.

It should be noted that the AI data features of the local data of the first node meet the AI data features corresponding to the processing requirements of the first AI model. It can be understood that the AI data features corresponding to the processing requirements of the first AI model are a subset of the AI data features of the local data of the first node, the AI data features of the local data of the first node are greater than or equal to the AI data features corresponding to the processing requirements of the first AI model, and the AI data features of the local data of the first node at least include the AI data features corresponding to the processing requirements of the first AI model, and the AI data features of the local data of the first node The data feature matches (or conforms to) at least one of the AI data features corresponding to the processing requirements of the first AI model.

In a possible implementation manner of the first aspect, before the first node processes the first AI model based on the local data to obtain the second AI model, the method further includes: the first node receiving second information, where the second information is used to indicate the first AI model.

It should be understood that the second information is used to indicate the first AI model, which can be understood as: the second information includes the index of the first AI model, so that the recipient of the first information can obtain the first AI model based on the index; or, the second information includes the first AI model, so that the recipient of the first information can obtain the first AI model from the second information.

Based on the above technical solution, the first node may also receive second information indicating the first AI model, so that the first node can determine the first AI model based on the second information, and process the first AI model to obtain the second AI model.

In a possible implementation manner of the first aspect, before the first node receives the second information, the method further includes: the first node sends indication information for indicating that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model.

Based on the above technical solution, before the first node receives the second information, the first node may also send indication information for indicating that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, so that the recipient of the indication information (i.e., the sender of the second information) can clearly know that the AI data features of the local data of the first node meet the AI data features corresponding to the processing requirements of the first AI model, and trigger the receiving direction to send the second information for indicating the first AI model to the first node.

Optionally, when the recipient of the indication information is clear that the AI data characteristics of the local data of the first node do not meet the AI data characteristics corresponding to the processing requirements of the first AI model, the sender of the second information may not send the second information for indicating the first AI model, thereby reducing unnecessary overhead.

Optionally, before the first node receives the second information, the first node may not send indication information for indicating that the AI data characteristics of the local data meet the AI data characteristics corresponding to the processing requirements of the first AI model, that is, the sender of the second information does not need to consider whether the AI data characteristics of the local data of the first node meet the AI data characteristics corresponding to the processing requirements of the first AI model. The first node can decide whether to receive the second information based on the first information and perform AI model processing based on the second information to reduce overhead.

In a possible implementation manner of the first aspect, the first information and the second information are different fields of the same data packet.

Based on the above technical solution, the first information and the second information may be different fields of the same data packet, so that after receiving the data packet, the first node can unpack the same data packet to obtain the first information and the second information.

In a possible implementation manner of the first aspect, the first information and the second information are carried on different communication resources.

Optionally, the different communication resources may include one or more of different time domain resources, different frequency domain resources, different spatial domain resources, etc.

Based on the above technical solution, the first information and the second information can be carried on different communication resources, so that after the first node determines the AI data features corresponding to the processing requirements of the first AI model based on the first information, when the first node determines that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, the first node then receives and parses the second information to obtain the first AI model.

In a possible implementation manner of the first aspect, the first information includes third information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information.

Based on the above technical solution, the first information received by the first node may include third information, so that the first node can implicitly determine the AI data characteristics corresponding to the processing requirements of the first AI model through the first processing information, and determine the first AI model through the third information.

Optionally, the first processing information includes at least one of the following:

a first scrambling sequence, where the first scrambling sequence is one of N scrambling sequences, where the N scrambling sequences respectively correspond to AI data features corresponding to N processing requirements of the first AI model, and N is an integer greater than or equal to 1;

The first key is one of M keys, and the M keys respectively correspond to AI data features corresponding to M processing requirements of the first AI model, where M is an integer greater than or equal to 1.

In a possible implementation manner of the first aspect, the first information includes fourth information, where the fourth information is used to indicate information about AI data features corresponding to a processing requirement of the first AI model.

Based on the above technical solution, the first information received by the first node may include fourth information, so that the first node can determine the AI data characteristics corresponding to the processing requirements of the first AI model through the fourth information independent of the indication information of the first AI model, and then determine whether the AI data characteristics of the local data of the first node meet the AI data characteristics corresponding to the processing requirements of the first AI model.

Optionally, the fourth information includes at least one of the following:

an identifier (or index) of the AI data feature corresponding to the processing requirement of the first AI model, that is, the fourth information indicates the AI data feature corresponding to the processing requirement of the first AI model by displaying an indication;

A first orthogonal sequence, where the first orthogonal sequence is one of K orthogonal sequences, and the K orthogonal sequences respectively correspond to AI data features corresponding to K processing requirements of the first AI model, where K is an integer greater than or equal to 1; that is, the fourth information indicates the AI data features corresponding to the processing requirements of the first AI model by implicit indication.

In a possible implementation manner of the first aspect, the method further includes: the first node sending fifth information, where the fifth information is used to determine AI data features corresponding to the processing requirements of the second AI model.

Based on the above technical solution, the first node may also send fifth information so that the receiver of the fifth information can determine the AI data features corresponding to the processing requirements of the second AI model. Thereafter, when the AI data features of the local data of the receiver meet the AI data features corresponding to the processing requirements of the second AI model, the receiver can process the second AI model based on the local data. Thus, the receiver can process the AI model that matches the AI data features of the local data, and accordingly, the AI model can be processed by the node that meets the processing requirements, thereby realizing AI model processing based on AI data feature matching.

In a possible implementation manner of the first aspect, the method further includes: the first node sending sixth information, where the sixth information is used to indicate the second AI model.

Based on the above technical solution, the first node may also send sixth information, so that a recipient of the sixth information can determine the second AI model based on the sixth information and process the second AI model.

In a possible implementation manner of the first aspect, before the first node sends the sixth information, the method further includes: the first node receives indication information for indicating that the AI data feature of the local data meets the AI data feature corresponding to the processing requirement of the second AI model.

Based on the above technical solution, before the first node sends the sixth information, the first node may also receive indication information from other nodes (such as neighboring nodes) indicating that the AI data features of local data meet the AI data features corresponding to the processing requirements of the second AI model, so that the first node can clearly know that the AI data features of the local data of the other nodes meet the AI data features corresponding to the processing requirements of the second AI model, and trigger the first node to send the sixth information indicating the second AI model to the other nodes.

Optionally, when it is clear that the AI data features of the local data of other nodes do not meet the AI data features corresponding to the processing requirements of the first AI model, the first node may not send the second information for indicating the first AI model, thereby reducing unnecessary overhead.

Optionally, before the first node sends the sixth information, the first node may not receive the indication information used to indicate that the AI data characteristics of the local data meet the AI data characteristics corresponding to the processing requirements of the second AI model, that is, the first node does not need to consider whether the AI data characteristics of the local data of the other nodes meet the AI data characteristics corresponding to the processing requirements of the second AI model. The other node can decide whether to receive the sixth information based on the fifth information and perform AI model processing based on the sixth information to reduce overhead.

In a possible implementation manner of the first aspect, the fifth information and the sixth information are different fields of the same data packet.

Based on the above technical solution, the fifth information and the sixth information can be different fields of the same data packet, so that after receiving the data packet, other nodes can unpack the same data packet to obtain the fifth information and the sixth information.

In a possible implementation manner of the first aspect, the fifth information and the sixth information are carried on different communication resources.

Optionally, the different communication resources may include one or more of different time domain resources, different frequency domain resources, different spatial domain resources, etc.

Based on the above technical solution, the fifth information and the sixth information can be carried on different communication resources, so that after the other node determines the AI data features corresponding to the processing requirements of the second AI model based on the fifth information, when the other node determines that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the second AI model, the other node then receives and parses the sixth information to obtain the second AI model.

In a possible implementation manner of the first aspect, the fifth information includes seventh information; wherein the seventh information is a processing result obtained by processing the second AI model based on the second processing information.

Based on the above technical solution, the fifth information sent by the first node may include seventh information, so that the recipient of the fifth information can implicitly determine the AI data characteristics corresponding to the processing requirements of the second AI model through the second processing information, and determine the first AI model through the seventh information.

Optionally, the second processing information includes at least one of the following:

A second scrambling sequence, the second scrambling sequence being one of the X scrambling sequences, the X scrambling sequences corresponding to the first 2. AI data features corresponding to X processing requirements of the AI model, where X is an integer greater than or equal to 1;

The second key is one of Y keys, and the Y keys respectively correspond to AI data features corresponding to Y processing requirements of the second AI model, where Y is an integer greater than or equal to 1.

In a possible implementation manner of the first aspect, the fifth information includes eighth information, where the eighth information is used to indicate information about AI data features corresponding to the processing requirements of the second AI model.

Based on the above technical solution, the fifth information sent by the first node may include eighth information, so that the recipient of the fifth information can determine the AI data characteristics corresponding to the processing requirements of the second AI model through the eighth information independent of the indication information of the second AI model, and then determine whether the AI data characteristics of the local data of the recipient meet the AI data characteristics corresponding to the processing requirements of the second AI model.

Optionally, the eighth information includes at least one of the following:

an identifier (or index) of the AI data feature corresponding to the processing requirement of the second AI model, that is, the eighth information indicates the AI data feature corresponding to the processing requirement of the second AI model by displaying an indication;

A second orthogonal sequence, where the second orthogonal sequence is one of Z orthogonal sequences, and the Z orthogonal sequences respectively correspond to AI data features corresponding to Z processing requirements of the second AI model, where Z is an integer greater than or equal to 1; that is, the eighth information indicates the AI data features corresponding to the processing requirements of the second AI model by implicit indication.

In a possible implementation manner of the first aspect, before the first node receives the first information, the method also includes: the first node receives first indication information, the first indication information being used to indicate that the first information includes third information and/or fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate AI data features corresponding to the processing requirements of the first AI model.

Based on the above technical solution, the first node can also receive first indication information, so that the first node determines that the first information includes third information and/or fourth information based on the first indication information, and receives and/or parses the first information based on the first indication information.

In a possible implementation manner of the first aspect, before the first node receives the first indication information, the method further includes: the first node sends node information, where the node information is used to indicate the AI data feature of the local data; wherein the node information is used to determine the first indication information.

Based on the above technical solution, before the first node receives the first indication information, the first node may also send node information of the first node, where the node information is used to indicate the AI data characteristics of the local data of the first node. The receiver of the node information can subsequently determine the first indication information based on the node information received from one or more nodes, so that the receiver can determine the first indication information that matches the node information of the one or more nodes.

In a possible implementation of the first aspect, the first node processes the first AI model based on local data to obtain the second AI model, including: the first node performs at least one of training processing, distillation processing, and fusion processing on the first AI model based on the local data to obtain the second AI model.

Based on the above technical solution, the first node can perform at least one of the above processing on the first AI model based on local data to improve the flexibility of the solution implementation.

The second aspect of the present application provides a communication method, which is executed by a central node, or the method is executed by some components in the central node (such as a processor, a chip or a chip system, etc.), or the method can also be implemented by a logic module or software that can realize all or part of the functions of the central node. In the second aspect and its possible implementation, the method is described as being executed by a central node. In this method, the central node determines a first indication information, and the first indication information is used to indicate that the first information includes a third information and/or a fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate the AI data features corresponding to the processing requirements of the first AI model; wherein the first information is used to determine the AI data features corresponding to the processing requirements of the first AI model; the central node sends the first indication information.

Based on the above technical solution, the first indication information sent by the central node is used to indicate that the first information includes the third information and/or the fourth information, wherein the first information is used to determine the AI data features corresponding to the processing requirements of the first AI model. This enables the receiver of the first indication information to receive and/or parse the first information based on the first indication information. Subsequently, the receiver can further determine based on the first information that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model. In this case, the receiver can process the AI model that matches the AI data features of the local data. Correspondingly, the AI model can also be processed by the node that meets the processing requirements, thereby realizing AI model processing based on AI data feature matching.

Optionally, the first processing information includes at least one of the following:

a first scrambling sequence, where the first scrambling sequence is one of N scrambling sequences, where the N scrambling sequences respectively correspond to AI data features corresponding to N processing requirements of the first AI model, and N is an integer greater than or equal to 1;

The first key is one of M keys, and the M keys respectively correspond to AI data features corresponding to M processing requirements of the first AI model, where M is an integer greater than or equal to 1.

Optionally, the fourth information includes at least one of the following:

an identifier (or index) of the AI data feature corresponding to the processing requirement of the first AI model, that is, the fourth information indicates the AI data feature corresponding to the processing requirement of the first AI model by displaying an indication;

A first orthogonal sequence, where the first orthogonal sequence is one of K orthogonal sequences, and the K orthogonal sequences respectively correspond to AI data features corresponding to K processing requirements of the first AI model, where K is an integer greater than or equal to 1; that is, the fourth information indicates the AI data features corresponding to the processing requirements of the first AI model by implicit indication.

In a possible implementation manner of the second aspect, the method further includes: the central node receives one or more node information, and the one or more node information is used to indicate AI data features of local data of one or more nodes; wherein the one or more node information is used to determine the first indication information.

Based on the above technical solution, before the central node sends the first indication information, the central node may also receive one or more node information, where the one or more node information is used to indicate the AI data features of the local data of one or more nodes. Subsequently, the central node can determine the first indication information based on the node information received from the one or more nodes, so that the receiving party can determine the first indication information that matches the node information of the one or more nodes.

Optionally, the AI data feature includes at least one of the following: an identifier of the AI task to which the AI data is applied, an object to which the AI data belongs, geographic location information for collecting the AI data, time information for collecting the AI data, and the number of samples of the AI data.

In a third aspect of the present application, a communication device is provided, which is a first node, or the device is a partial component (such as a processor, a chip or a chip system, etc.) in the first node, or the device can also be a logic module or software that can implement all or part of the functions of the first node. In the third aspect and its possible implementation, the communication device is described as an example of the first node, and the first node can be a terminal device or a network device.

The device includes a processing unit and a transceiver unit; the transceiver unit is used to receive first information, and the first information is used to determine AI data features corresponding to the processing requirements of the first AI model; when the AI data features of local data meet the AI data features corresponding to the processing requirements of the first AI model, the processing unit is used to process the first AI model based on the local data to obtain a second AI model.

In a possible implementation manner of the third aspect, the transceiver unit is further used to receive second information, where the second information is used to indicate the first AI model.

In a possible implementation manner of the third aspect, the transceiver unit is further used to send indication information for indicating that the AI data feature of the local data meets the AI data feature corresponding to the processing requirement of the first AI model.

In a possible implementation manner of the third aspect, the first information and the second information are different fields of the same data packet.

In a possible implementation manner of the third aspect, the first information and the second information are carried on different communication resources.

In a possible implementation manner of the third aspect, the first information includes third information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information.

In a possible implementation manner of the third aspect, the first processing information includes at least one of the following:

a first scrambling sequence, where the first scrambling sequence is one of N scrambling sequences, where the N scrambling sequences respectively correspond to AI data features corresponding to N processing requirements of the first AI model, and N is an integer greater than or equal to 1;

The first key is one of M keys, and the M keys respectively correspond to AI data features corresponding to M processing requirements of the first AI model, where M is an integer greater than or equal to 1.

In a possible implementation manner of the third aspect, the first information includes fourth information, where the fourth information is used to indicate AI data features corresponding to the processing requirements of the first AI model.

In a possible implementation manner of the third aspect, the fourth information includes at least one of the following:

An identifier of an AI data feature corresponding to the processing requirement of the first AI model;

A first orthogonal sequence, the first orthogonal sequence is an orthogonal sequence among K orthogonal sequences, the K orthogonal sequences respectively corresponding to the first AI data features corresponding to K processing requirements of an AI model, where K is an integer greater than or equal to 1.

In a possible implementation manner of the third aspect, the transceiver unit is further used to send fifth information, where the fifth information is used to determine AI data features corresponding to the processing requirements of the second AI model.

In a possible implementation manner of the third aspect, the transceiver unit is further used to send sixth information, where the sixth information is used to indicate the second AI model.

In a possible implementation manner of the third aspect, the transceiver unit is further used to receive indication information indicating that the AI data feature of the local data meets the AI data feature corresponding to the processing requirement of the second AI model.

In a possible implementation manner of the third aspect, the fifth information and the sixth information are different fields of the same data packet.

In a possible implementation manner of the third aspect, the fifth information and the sixth information are carried on different communication resources.

In a possible implementation manner of the third aspect, the fifth information includes seventh information; wherein the seventh information is a processing result obtained by processing the second AI model based on the second processing information.

In a possible implementation manner of the third aspect, the second processing information includes at least one of the following:

a second scrambling sequence, where the second scrambling sequence is one of X scrambling sequences, where the X scrambling sequences respectively correspond to AI data features corresponding to X types of processing requirements of the second AI model, where X is an integer greater than or equal to 1;

The second key is one of Y keys, and the Y keys respectively correspond to AI data features corresponding to Y processing requirements of the second AI model, where Y is an integer greater than or equal to 1.

In a possible implementation manner of the third aspect, the fifth information includes eighth information, wherein the eighth information is used to indicate information about AI data features corresponding to the processing requirements of the second AI model.

In a possible implementation manner of the third aspect, the eighth information includes at least one of the following:

An identifier of an AI data feature corresponding to the processing requirement of the second AI model;

A second orthogonal sequence, where the second orthogonal sequence is an orthogonal sequence among Z orthogonal sequences, and the Z orthogonal sequences respectively correspond to AI data features corresponding to Z processing requirements of the second AI model, where Z is an integer greater than or equal to 1.

In a possible implementation of the third aspect, the transceiver unit is also used to receive first indication information, which is used to indicate that the first information includes third information and/or fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate AI data features corresponding to the processing requirements of the first AI model.

In a possible implementation manner of the third aspect, the transceiver unit is further used to send node information, where the node information is used to indicate the AI data feature of the local data; wherein the node information is used to determine the first indication information.

In a possible implementation manner of the third aspect, the AI data feature includes at least one of the following: an identifier of the AI task to which the AI data is applied, an object to which the AI data belongs, geographic location information for collecting the AI data, time information for collecting the AI data, and the number of samples of the AI data.

In a possible implementation of the third aspect, the processing unit is used to process the first AI model based on local data to obtain the second AI model, including: the processing unit is used to perform at least one of training processing, distillation processing and fusion processing on the first AI model based on the local data to obtain the second AI model.

In a fourth aspect of the present application, a communication device is provided, which is a central node, or the device is a partial component in the central node (such as a processor, a chip or a chip system, etc.), or the device can also be a logic module or software that can implement all or part of the central node functions. In the eighth aspect and its possible implementation, the communication device is described as an example of a central node, and the central node can be a terminal device or a network device.

The device includes a processing unit and a transceiver unit; the processing unit is used to determine first indication information, the first indication information is used to indicate that the first information includes third information and/or fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate the AI data features corresponding to the processing requirements of the first AI model. wherein the first information is used to determine the AI data features corresponding to the processing requirements of the first AI model; the transceiver unit is used to send the first indication information.

In a possible implementation manner of the fourth aspect, the transceiver unit is further used to receive one or more node information, where the one or more node information is used to indicate AI data features of local data of one or more nodes; wherein the one or more node information is used to determine the first indication information.

In a possible implementation manner of the fourth aspect, the AI data feature includes at least one of the following: an identifier of the AI task to which the AI data is applied, an object to which the AI data belongs, geographic location information for collecting the AI data, time information for collecting the AI data, and the number of samples of the AI data.

In a fifth aspect, the present application provides a communication device, comprising at least one processor, which is coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements the method of the aforementioned first aspect or any possible implementation method of the first aspect.

In a sixth aspect, the present application provides a communication device, comprising at least one processor, which is coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements the aforementioned second aspect or any possible implementation method of the second aspect.

The seventh aspect of the present application provides a communication device, including at least one logic circuit and an input and output interface; the logic circuit is used to execute the method as described in the first aspect or any possible implementation of the first aspect.

In an eighth aspect, the present application provides a communication device, comprising at least one logic circuit and an input/output interface; the logic circuit is used to execute the method as described in the second aspect or any possible implementation of the second aspect.

In one possible design, the communication device provided in the fifth, sixth, seventh or eighth aspect above may be a chip or a chip system.

In a ninth aspect, the present application provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method in any possible implementation of any aspect of the first to second aspects above.

The tenth aspect of the present application provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes any possible implementation method of any aspect of the first to second aspects above.

In the eleventh aspect of the present application, a chip system is provided, which includes at least one processor for supporting a communication device to implement the functions involved in any possible implementation method of any aspect of the first to second aspects.

In one possible design, the chip system may also include a memory for storing program instructions and data necessary for the first communication device. The chip system may be composed of a chip, or may include a chip and other discrete devices. Optionally, the chip system also includes an interface circuit for providing program instructions and/or data to the at least one processor.

The twelfth aspect of the present application provides a communication system, which includes the communication device of the third aspect and the communication device of the fourth aspect, and/or the communication system includes the communication device of the fifth aspect and the communication device of the sixth aspect, and/or the communication system includes the communication device of the seventh aspect and the communication device of the eighth aspect.

Among them, the technical effects brought about by any design method in the third aspect to the twelfth aspect can refer to the technical effects brought about by the different design methods in the above-mentioned first aspect to the second aspect, and will not be repeated here.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1a is a schematic diagram of a communication system provided by the present application;

FIG1b is another schematic diagram of a communication system provided by the present application;

FIG1c is another schematic diagram of a communication system provided by the present application;

FIG1d is a schematic diagram of the AI processing process involved in this application;

FIG. 1e is another schematic diagram of the AI processing process involved in this application;

FIG2a is another schematic diagram of the AI processing process involved in the present application;

FIG2b is another schematic diagram of the AI processing process involved in the present application;

FIG2c is another schematic diagram of the AI processing process involved in this application;

FIG2d is another schematic diagram of the AI processing process involved in this application;

FIG2e is another schematic diagram of the AI processing process involved in this application;

FIG3 is an interactive schematic diagram of the communication method provided by the present application;

FIG4a is a schematic diagram of the AI processing process provided by the present application;

FIG4b is another schematic diagram of the AI processing process provided by the present application;

FIG5a is another schematic diagram of the AI processing process provided by the present application;

FIG5b is another schematic diagram of the AI processing process provided by the present application;

FIG5c is another schematic diagram of the AI processing process provided by the present application;

FIG5d is another schematic diagram of the AI processing process provided by the present application;

FIG5e is another schematic diagram of the AI processing process provided by the present application;

FIG6 is another schematic diagram of the AI processing process provided by the present application;

FIG7 is another interactive schematic diagram of the communication method provided by the present application;

FIG8 is a schematic diagram of a communication device provided by the present application;

FIG9 is another schematic diagram of a communication device provided by the present application;

FIG10 is another schematic diagram of a communication device provided by the present application;

FIG11 is another schematic diagram of a communication device provided by the present application;

FIG. 12 is another schematic diagram of the communication device provided in the present application.

DETAILED DESCRIPTION

First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

(1) Terminal device: It 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 users, or a handheld device with wireless connection function, or other processing devices connected to a wireless modem.

The terminal equipment can communicate with one or more core networks or the Internet via the radio access network (RAN). The terminal equipment can be a mobile terminal equipment, such as a mobile phone (or "cellular" phone, mobile phone), a computer and a data card. For example, it can be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device that exchanges voice and/or data with the radio access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDA), tablet computers (Pad), computers with wireless transceiver functions and other devices. Wireless terminal equipment can also be called system, subscriber unit, subscriber station, mobile station, mobile station (MS), remote station, access point (AP), remote terminal equipment (remote terminal), access terminal equipment (access terminal), user terminal equipment (user terminal), user agent (user agent), subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

As an example but not limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices or smart wearable devices, etc., which are a general term for the application of wearable technology to intelligently design and develop wearable devices for daily wear, such as glasses, gloves, watches, clothing and shoes, etc. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also powerful functions achieved through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include full-featured, large-size, and independent of smartphones to achieve complete or partial functions, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various types of smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

The terminal can also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle to everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc.

In addition, the terminal device may also be a terminal device in a communication system that evolves after the fifth generation (5th generation, 5G) communication system (e.g., a sixth generation (6th generation, 6G) communication system, etc.) or a terminal device in a public land mobile network (PLMN) that evolves in the future, etc. Exemplarily, the 6G network can further expand the form and function of the 5G communication terminal, and the 6G terminal includes but is not limited to a car, a cellular network terminal (with integrated satellite terminal function), a drone, and an Internet of Things (IoT) device.

In an embodiment of the present application, the terminal device may also obtain AI services provided by the network device. Optionally, the terminal device may also have AI processing capabilities.

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

Optionally, the RAN node may also be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. The RAN node may also be a server, a wearable device, a vehicle or an onboard device, etc. For example, the access network device in the vehicle to everything (V2X) technology may be a road side unit (RSU).

In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, and different RAN nodes implement part of the functions of the base station respectively. For example, the RAN node can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The CU and DU can be set separately, or can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU) or a remote radio head (RRH).

In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open access network (open RAN, O-RAN or ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, CU, CU-CP, CU-UP, DU and RU are used as examples for description in this application. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

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

For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, refer to Table 1 below.

Table 1

The network device may be any other device that provides wireless communication functions for the terminal device. The embodiments of the present application do not limit the specific technology and specific device form used by the network device. For the convenience of description, the embodiments of the present application do not limit.

The network equipment may also include core network equipment, such as mobility management entity (MME), home subscriber server (HSS), serving gateway (S-GW), policy and charging rules function (PCRF), public data network gateway (PDN gateway, P-GW) in the fourth generation (4G) network; access and mobility management function (AMF), user plane function (UPF) or session management function (SMF) and other network elements in the 5G network. In addition, the core network equipment may also include other core network equipment in the 5G network and the next generation network of the 5G network.

In an embodiment of the present application, the above-mentioned network device may also have a network node with AI capabilities, which can provide AI services for terminals or other network devices. For example, it may be an AI node on the network side (access network or core network), a computing node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

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

(3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used at the same time. Configuration refers to the network device/server sending some parameter configuration information or parameter values to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values or information. Pre-configuration is similar to configuration, and can be parameter information or parameter values pre-negotiated between the network device/server and the terminal device, or parameter information or parameter values used by the base station/network device or terminal device specified by the standard protocol, 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 the present application can be used interchangeably. "Multiple" refers to two or more. "And/or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character "/" generally indicates that the objects associated with each other are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items 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 the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.

(5) "Send" and "receive" in the embodiments of the present application indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information is XX, which can include direct sending through the air interface, and also include indirect sending through the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information is YY, which can include direct receiving from YY through the air interface, and also include indirect receiving from YY through the air interface from other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

In other words, sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

It is understandable that information may be processed between the source and destination of information transmission, such as coding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.

(6) In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein the other information is associated with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other part of the information to be indicated is known or agreed in advance. For example, the indication of specific information may be realized by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that, for the sender of the indication information, the indication information may be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information may be used to determine the information to be indicated.

In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments in this application, and the various methods/designs/implementations in each embodiment, if there is no special explanation and logical conflict, the terms and/or descriptions between different embodiments and the various methods/designs/implementations in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various methods/designs/implementations in each embodiment can be combined to form new embodiments, methods, or implementations according to their inherent logical relationships. The implementation methods of this application described below do not constitute a limitation on the scope of protection of this application.

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

Please refer to FIG. 1a, which is a schematic diagram of a communication system in the present application. FIG. 1a shows a network device and six terminal devices, which are terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in FIG. 1a, terminal device 1 is a smart tea cup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas station, terminal device 4 is a means of transportation, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

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

Exemplarily, in FIG. 1a, terminal device 4-terminal device 6 can also form a communication system. Among them, terminal device 5 serves as a network device, that is, an AI configuration information sending entity; terminal device 4 and terminal device 6 serve as terminal devices, that is, AI configuration information receiving entities. For example, in a vehicle networking system, terminal device 5 sends AI configuration information to terminal device 4 and terminal device 6 respectively, and receives data sent by terminal device 4 and terminal device 6; correspondingly, terminal device 4 and terminal device 6 receive AI configuration information sent by terminal device 5, and send data to terminal device 5.

Taking the communication system shown in Figure 1a as an example, in addition to executing communication-related services, different devices (including between network devices and network devices, between network devices and terminal devices, and/or between terminal devices and terminal devices) may also execute AI-related services. For example, as shown in Figure 1b, taking the network device as a base station as an example, the base station can execute communication-related services and AI-related services with one or more terminal devices, and communication-related services and AI-related services can also be executed between different terminal devices. For another example, as shown in Figure 1c, taking the terminal devices including a TV and a mobile phone as an example, communication-related services and AI-related services can also be executed between the TV and the mobile phone.

The technical solution provided in the present application can be applied to a wireless communication system (e.g., the system shown in FIG. 1a, FIG. 1b, or FIG. 1c). For example, an AI network element can be introduced into the communication system provided in the present application to implement some or all AI-related operations. The AI network element may also be referred to as an AI node, an AI device, an AI entity, an AI module, an AI model, or an AI unit, etc. The AI network element may be a network element built into a communication system. For example, the AI network element may be an AI module built into: an access network device, a core network device, a cloud server, or a network management (operation, administration and maintenance, OAM) to implement AI-related functions. The OAM may be a network management device for a core network device and/or a network management device for an access network device. Alternatively, the AI network element may also be a network element independently set up in the communication system. Optionally, the terminal or the chip built into the terminal may also include an AI entity to implement AI-related functions.

The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

Artificial intelligence (AI) can give machines human intelligence, for example, it can allow machines to use computer hardware and software to simulate certain intelligent behaviors of humans. In order to realize artificial intelligence, machine learning methods can be used. In machine learning methods, machines use training data to learn (or train) a model. The model represents the mapping from input to output. The learned model can be used for reasoning (or prediction), that is, the model can be used to predict the output corresponding to a given input. Among them, the output can also be called the reasoning result (or prediction result).

Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

Supervised learning uses machine learning algorithms to learn the mapping relationship from sample values to sample labels based on the collected sample values and sample labels, and uses AI models to express the learned mapping relationship. The process of training a machine learning model is the process of learning this mapping relationship. During the training process, the 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, the learned mapping can be used to predict new sample labels. The mapping relationship learned by supervised learning can include linear mapping or nonlinear mapping. According to the type of label, the learning task can be divided into classification task and regression task.

Unsupervised learning uses algorithms to discover the inherent patterns of samples based on the collected sample values. One type of algorithm in unsupervised learning uses the samples themselves as supervisory signals, that is, the model learns the mapping relationship from sample to sample, which is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predicted value and the sample itself. Self-supervised learning can be used in applications such as signal compression and decompression recovery. Common algorithms include autoencoders and adversarial generative networks.

Reinforcement learning is different from supervised learning. It is a type of algorithm that learns problem-solving strategies by interacting with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have clear "correct" action label data. The algorithm needs to interact with the environment to obtain reward signals from environmental feedback, and then adjust the decision-making actions to obtain larger reward signal values. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput fed back by the wireless network, in the hope of achieving a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between environmental states and better (e.g., optimal) decision actions. 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 through interaction with the environment. This is achieved through iterative interaction of the environment.

Neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, neural network can theoretically approximate any continuous function, so that neural network has the ability to learn any mapping. Traditional communication systems require rich expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover implicit pattern structures from a large number of data sets, establish mapping relationships between data, and obtain performance that is superior to traditional modeling methods.

The idea of neural network comes from the neuron structure of brain tissue. For example, each neuron performs a weighted sum operation on its input value and outputs the operation result through an activation function. As shown in Figure 1d, it is a schematic diagram of the neuron structure. Assume that the input of the neuron is x = [ _x0 , _x1 , ..., _xn ], and the weights corresponding to each input are w = [w, _w1 , ..., _wn ], where n is a positive integer, and w _i and _xi can be various possible types such as decimals, integers (such as 0, positive integers or negative integers, etc.), or complex numbers. _{w i} is used as the weight of _xi to weight _xi . The bias for weighted summation of input values according to the weight is, for example, b. The activation function can take many forms. Assuming that the activation function of a neuron is: y = f(z) = max(0, z), the output of the neuron is: For another example, the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a decimal, an integer (eg, 0, a positive integer or a negative integer), or a complex number, etc. The activation functions of different neurons in a neural network can be the same or different.

In addition, a neural network generally includes multiple layers, each of which may include one or more neurons. By increasing the depth and/or width of a neural network, the expressive power of the neural network can be improved, providing a more powerful information extraction and abstract modeling capability for complex systems. Among them, the depth of a neural network may refer to the number of layers included in the neural network, and the number of neurons included in each layer may be referred to as the width of the layer. In one implementation, the neural network includes an input layer and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the output layer, and the output layer obtains the output result of the neural network. In another implementation, the neural network includes an input layer, a hidden layer, and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the middle hidden layer, the hidden layer calculates the received processing results to obtain the calculation results, the hidden layer passes the calculation results to the output layer or the next adjacent hidden layer, and finally the output layer obtains the output result of the neural network. Among them, a neural network may include one hidden layer, or include multiple hidden layers connected in sequence, without limitation.

A neural network is, for example, a deep neural network (DNN). Depending on how the network is constructed, DNNs can include feedforward neural networks (FNN), convolutional neural networks (CNN), and recurrent neural networks (RNN).

The characteristic of FNN network is that neurons in adjacent layers are fully connected to each other. This characteristic makes FNN usually require a lot of storage space and leads to high computational complexity. Figure 1e is a schematic diagram of a FNN network.

CNN is a neural network that is specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling on the time axis) and image data (discrete sampling on two dimensions) can be considered to be data with a grid-like structure. CNN does not use all the input information for calculations at once, but uses a fixed-size window to intercept part of the information for convolution operations, which greatly reduces the amount of calculation of model parameters. In addition, depending on the type of information intercepted by the window (for example, people and objects in a picture are different types of information), each window can use different convolution kernel operations, which enables CNN to better extract the features of the input data.

RNN is a type of DNN network that uses feedback time series information. Its input includes the new input value at the current moment and its own output value at the previous moment. RNN is suitable for obtaining sequence features that are correlated in time, and is particularly suitable for applications such as speech recognition and channel coding.

In the above machine learning model training process, a loss function can be defined. The loss function describes the gap or difference between the output value of the model and the ideal target value. The loss function can be expressed in many forms, and there is no restriction on the specific form of the loss function. The model training process can be regarded as the following process: by adjusting some or all parameters of the model, the value of the loss function is less than the threshold value or meets the target requirements.

Models can also be referred to as AI models, rules or other names. AI models can be considered as specific methods for implementing AI functions. AI models characterize the mapping relationship or function between the input and output of the model. AI functions may include one or more of the following: data collection, model training (or model learning), model information release, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or reasoning result release, etc. AI functions can also be referred to as AI (related) operations, or AI-related functions.

The implementation process of the neural network will be described exemplarily below with reference to the accompanying drawings.

1. Fully connected neural network.

Also called multilayer perceptron (MLP). As shown in Figure 2a, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (middle). Each layer of the MLP contains several nodes, called neurons. The neurons in two adjacent layers are connected to each other.

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

Among them, 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 ).

Among them, n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

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

Optionally, the specific method of training is to use a loss function to evaluate the output of the neural network. As shown in Figure 2b, the error can be back-propagated, and the neural network parameters (including w and b) can be iteratively optimized by the gradient descent method until the loss function reaches a minimum value, that is, the "better point (e.g., optimal point)" in Figure 2b. It can be understood that the neural network parameters corresponding to the "better point (e.g., optimal point)" in Figure 2b can be used as the neural network parameters in the trained AI model information.

Alternatively, the gradient descent process can be expressed as:

Among them, θ is the parameter to be optimized (including w and b), L is the loss function, η is the learning rate, which controls the step size of gradient descent. represents the derivative operation, It means taking the derivative of θ with respect to L.

Optionally, the back-propagation process utilizes the chain rule for partial derivatives. As shown in Figure 2c, the gradient of the previous layer parameters can be recursively calculated from the gradient of the next layer parameters, which can be expressed as:

Among them, w _ij is the weight of node j connecting node i, and _si is the weighted sum of inputs on node i.

2. Federated Learning (FL).

The concept of federated learning effectively solves the current dilemma faced by the development of artificial intelligence. It fully protects the privacy and security of user data by promoting the collaboration of various edge devices and central servers to efficiently complete the learning tasks of the model. As shown in Figure 2d, the FL architecture is the most widely used training architecture in the current FL field, and the FedAvg algorithm is the basic algorithm of FL. Its algorithm flow is as follows:

(1) The center initializes the model to be trained And broadcast it to all client devices.

(2) In the t∈[1,T]th round, client k∈[1,K] based on the local dataset For the received global model Perform E epochs of training to obtain local training results Report it to the central node.

(3) The central node aggregates and collects local training results from all (or some) clients. Assume that the client set that uploads the local model in round t is The center will use the number of samples of the corresponding client as the weight to perform weighted averaging to obtain a new global model. The specific update rule is: The center then sends the latest version of the global model Broadcast to all client devices for a new round of training.

(4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

In addition to reporting local models You can also use the local gradient of training After reporting, the central node averages the local gradients and updates the global model according to the direction of the average gradient.

As you can see, in the FL framework, the data set exists in the distributed nodes, that is, the distributed nodes collect local data sets, perform local training, and report the local results (models or gradients) obtained from the training to the central node. The central node itself does not have a data set, and is only responsible for fusing the training results of the distributed nodes to obtain the global model and send it to the distributed nodes.

3. Decentralized learning.

Different from federated learning, another distributed learning architecture, decentralized learning, is shown in Figure 2e. Consider a fully distributed system without a central node. The design goal f(x) of a decentralized learning system is generally the mean of the goals _fi (x) of each node, that is, Where n is the number of distributed nodes, x is the parameter to be optimized. In machine learning, x is the parameter of the machine learning (such as neural network) model. Each node uses local data and local target _fi (x) to calculate the local gradient Then it is sent to the neighboring nodes that can be communicated with. After any node receives the gradient information sent by its neighbor, it can update the parameter x of the local model according to the following formula:

in, represents the parameters of the local model after the k+1th (k is a natural number) update in the i-th node, represents the parameters of the local model after the kth update in the i-th node (if k is 0, it means is the parameter of the local model of the i-th node that does not participate in the update), α _k represents the tuning coefficient, _Ni is the set of neighbor nodes of node i, and | _Ni | represents the number of elements in the set of neighbor nodes of node i, that is, the number of neighbor nodes of node i. Through information interaction between nodes, the decentralized learning system will eventually learn a unified model.

The technical solution provided by the present application can be applied to a wireless communication system (e.g., the system shown in FIG. 1a or FIG. 1b), in which a communication node generally has signal transceiving capability and computing capability. Taking a network device with computing capability as an example, the computing capability of the network device is mainly to provide computing power support for the signal transceiving capability (e.g., calculating the time domain resources and frequency domain resources of the signal carrier) to realize the communication task between the network device and other communication nodes.

However, in a communication network, the computing power of communication nodes may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power is a technical problem that needs to be solved urgently.

As an implementation example, in order to cope with the vision of intelligent inclusiveness in the future, intelligence may further evolve at the wireless network architecture level. In future communication systems, AI may further integrate with wireless networks to realize network-native intelligence, as well as terminal intelligence. Exemplarily, the integration of AI and wireless networks can be applied to the following new requirements and new scenarios: For example, terminal connections are more flexible and intelligent: including but not limited to diversified terminal types, Super Internet of Things (Supper IOT) (for example, Supper IOT can include Internet of Things, car networking, industry, medical care...), massive connections, more flexible terminal connections, and terminals themselves have certain AI capabilities. Another example: Network-native intelligence: In addition to providing traditional communication connection services, the network will further provide computing and AI services to better support inclusive, real-time and highly secure AI services. These new requirements and new scenarios will bring about changes in wireless network architecture and communication modes.

Generally, the participating nodes of AI learning may include nodes in multiple distributed communication networks, such as terminal devices, network devices, etc. In the current learning architecture (such as the learning architecture shown in Figure 2d or Figure 2e), it is assumed that the construction of the learning system has been completed, that is, all participating nodes are facing the same learning task and jointly train a global machine learning model. However, the current research does not discuss the construction of the learning system, that is, how to gather nodes facing the same learning task together to complete the AI learning task.

In order to solve the above problems, the present application provides a communication method and related equipment, which are used to enable the computing power of communication nodes to be applied to artificial intelligence (AI) learning, and to realize AI model processing based on AI data feature matching.

Please refer to FIG3 , which is a schematic diagram of an implementation of the communication method provided in the present application. The method includes the following steps.

It should be noted that in Figure 3, the method is illustrated by taking the first node and the second node as the execution subject of the interactive schematic as an example, but the present application does not limit the execution subject of the interactive schematic. For example, the execution subject of S301 in Figure 3 and the corresponding implementation is the first node, and the execution subject may also be a chip, a chip system, or a processor that supports the first node to implement the method, or a logic module or software that can implement all or part of the functions of the first node. The second node in S301-S302 in Figure 3 and the corresponding implementation may also be replaced by a chip, a chip system, or a processor that supports the second node to implement the method, or may be replaced by a logic module or software that can implement all or part of the functions of the second node.

S301. The second node sends first information, and correspondingly, the first node receives the first information, wherein the first information is used to determine AI data features corresponding to processing requirements of the first AI model.

In this application, the AI model can be replaced by a neural network, an AI neural network, a neural network model, an AI neural network model, a machine learning model, etc.

It should be understood that the AI model involved in this application can be applied to AI tasks. In other words, the execution process of the AI task includes the processing of one or more AI models by the communication node. Among them, the AI task can be a task that requires two or more communication nodes (such as a first node and a second node, etc.) to participate in the processing. The communication node includes a terminal device and/or a network device. For example, the AI task can include a federated learning (FL) task, a distributed training task, a distributed learning task, etc.

Optionally, the first node and the second node may be communication nodes participating in the AI task, wherein the first node and the second node are neighboring nodes to each other, that is, the first node and the second node are mutually communicable nodes. In step S301, the second node sends the first information to the first node, that is, the second node may be the previous hop node of the first node, in other words, the first node may be the next hop node of the second node.

S302. The first node processes the first AI model based on the local data to obtain a second AI model. When the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, the first node performs processing on the first AI model in step S302.

It should be understood that AI data may refer to data related to AI, and AI data features may be used to indicate features (or characteristics or traits or attributes or properties) of data related to AI, etc. In step S302, the AI data features of the local data of the first node meet the AI data features corresponding to the processing requirements of the first AI model. It can be understood that the AI data features corresponding to the processing requirements of the first AI model are a subset of the AI data features of the local data of the first node, the AI data features of the local data of the first node are more than or equal to the AI data features corresponding to the processing requirements of the first AI model, the AI data features of the local data of the first node at least include the AI data features corresponding to the processing requirements of the first AI model, and the AI data features of the local data of the first node match/conform to at least one of the AI data features corresponding to the processing requirements of the first AI model.

Exemplarily, the AI data feature may include at least one of the following features 1 to 5.

Feature 1. The AI tasks to which the AI data is applied.

For feature 1, different AI tasks can be distinguished by different task identifications (IDs), where the task ID can be pre-configured or configured, which is not limited here.

Exemplarily, when the AI data feature includes feature 1, as shown in FIG4a, the local data of node 0 and the local data of node 2 can both be applied to the AI task with task ID 1 (recorded as "AI data of task 1" in FIG4a), and the local data of node 1 can be applied to the AI task with task ID 2 (recorded as "AI data of task 2" in FIG4a). Moreover, the AI data feature corresponding to the processing requirement of the AI model involved in the model transfer process is the AI data with task ID 1, that is, the AI model involved in the model transfer process can be the AI model involved in the AI task with task ID 1.

It should be understood that in the example shown in FIG. 4 a , the first node may be node 1 in the graph and the second node may be node 0 in the graph, or the first node may be node 2 in the graph and the second node may be node 1 in the graph.

In the example shown in FIG4a, for node 0, when node 0 determines that the AI task of the local data application includes at least the AI task corresponding to the processing requirement of the AI model, node 0 can determine that the AI data feature of the local data of node 0 meets the AI data feature corresponding to the processing requirement of the AI model (represented as "AI data feature matching" in the figure). Thus, node 0 can process the received AI model (i.e., the processing process in step S302), and send the processing result to node 1 through the model transmission process.

In the example shown in FIG4a, for node 1, when node 1 determines that the AI task of the local data application is different from the AI task corresponding to the processing requirement of the AI model, node 1 can determine that the AI data feature of the local data of node 0 does not meet the AI data feature corresponding to the processing requirement of the AI model (represented as "AI data feature mismatch" in the figure). Thus, node 1 can discard or ignore the received AI model or transmit it to the next hop node (such as node 2 in FIG4b).

Optionally, in the example shown in FIG4a, node 0 and node 1 are neighbor nodes to each other (i.e., nodes that can communicate with each other), node 1 and node 2 are neighbor nodes to each other (i.e., nodes that can communicate with each other), and node 0 and node 2 are not neighbor nodes to each other (i.e., node 0 and node 2 are not reachable to each other). In other words, node 0 and node 2 can communicate through node 1, and node 1 can be regarded as a relay node for node 0 and node 2. Among them, when node 1 determines that the AI data features do not match, the node 1 may not perform the model processing process. In addition, the node 1 can act as a relay node to perform the model interaction process, that is, forwarding the AI model from node 0 (or the indication information for indicating the AI model) to node 2, so that node 2 can obtain the AI model corresponding to the AI data of task 1 through the model transmission process and perform subsequent processing.

In the example shown in FIG4a, for node 2, after node 2 determines the AI model through the model transfer process, if node 2 determines that the AI task of the local data application at least includes the AI task corresponding to the processing requirement of the AI model, node 2 can determine that the AI data feature of the local data of node 2 meets the AI data feature corresponding to the processing requirement of the AI model (represented as "AI data feature matching" in the figure). Thus, node 2 can process the received AI model (i.e., the processing process in step S302).

Optionally, the node 2 may also transmit the processed AI model so that the next hop node of the node 2 determines the processed AI model.

Feature 2: The object to which AI data belongs.

For feature 2, different objects of AI data can be distinguished by different object IDs, where the object ID can be pre-configured or configured, which is not limited here. For example, different objects of AI data can be distinguished by the different objects corresponding to the AI data, including AI data of object 1, AI data of object 2, etc. Among them, the AI data of object 1 and the AI data of object 2 can be AI data of different industries. For example, the AI data of object 1 and the AI data of object 2 can be any two different AI data such as AI data of the medical industry, AI data of the education industry, AI data of the financial industry, etc.

Exemplarily, when the AI data feature includes feature 2, as shown in FIG4b, the local data of node 0 and the local data of node 2 may both include data with an object identifier of object 1 (recorded as "AI data of object 1" in FIG4b), and the local data of node 1 may include data with an object identifier of object 2 (recorded as "AI data of object 2" in FIG4b). Moreover, the AI data feature corresponding to the processing requirements of the AI model involved in the model transfer process is the AI data of object 1, that is, the AI model involved in the model transfer process needs to be processed through the AI data of object 1.

It should be understood that in the example shown in Figure 4b, the first node may be node 1 in the graph and the second node may be node 0 in the graph, or the first node may be node 2 in the graph and the second node may be node 1 in the graph.

In the example shown in FIG4b, for node 0, when node 0 determines that the object to which the local data belongs includes at least the object corresponding to the processing requirement of the AI model, node 0 can determine that the AI data feature of the local data of node 0 meets the AI data feature corresponding to the processing requirement of the AI model (represented as "AI data feature matching" in the figure). Thus, node 0 can process the received AI model (i.e., the processing process in step S302), and send the processing result to node 1 through the model transfer process.

In the example shown in FIG4b, for node 1, when node 1 determines that the object to which the local data belongs is different from the object corresponding to the processing requirement of the AI model, node 1 can determine that the AI data feature of the local data of node 0 does not meet the AI data feature corresponding to the processing requirement of the AI model (represented as "AI data feature mismatch" in the figure). Thus, node 1 can discard or ignore the received AI model or transmit it to the next hop node (such as node 2 in FIG4b).

Optionally, in the example shown in FIG4b, node 0 and node 1 are neighbor nodes to each other (i.e., nodes that can communicate with each other), node 1 and node 2 are neighbor nodes to each other (i.e., nodes that can communicate with each other), and node 0 and node 2 are not neighbor nodes to each other (i.e., node 0 and node 2 are not reachable to each other). In other words, node 0 and node 2 can communicate through node 1, and node 1 can be regarded as a relay node for node 0 and node 2. Among them, when node 1 determines that the AI data features do not match, the node 1 may not perform the model processing process. In addition, the node 1 can act as a relay node to perform the model interaction process, that is, forwarding the AI model from node 0 (or the indication information for indicating the AI model) to node 2, so that node 2 can obtain the AI model corresponding to the AI data of type 1 through the model transmission process and perform subsequent processing.

In the example shown in FIG4b, for node 2, after node 2 determines the AI model through the model transfer process, if node 2 determines that the object to which the local data belongs at least includes the object corresponding to the processing requirement of the AI model, node 2 can determine that the AI data feature of the local data of node 2 meets the AI data feature corresponding to the processing requirement of the AI model (represented as "AI data feature matching" in the figure). Thus, node 2 can process the received AI model (i.e., the processing process in step S302).

Optionally, the node 2 may also transmit the processed AI model so that the next hop node of the node 2 determines the processed AI model.

Feature 3. Geographic location information of collected AI data.

For feature 3, different geographic location information for collecting AI data can be distinguished by different geographic location identifiers, where different geographic location identifiers can be pre-configured or configured, which is not limited here. For example, different geographic location identifiers can be distinguished by different collection locations of AI data, including GPS information, indoor/outdoor information, LOS scene/NLOS scene information, etc.

In an implementation example, a possible requirement is to process the AI model based on data in a certain geographical location area to obtain an AI model suitable for the geographical area. In this case, the geographical area can be set as feature 3.

In another implementation example, a possible requirement is to process the AI model based on data from different geographical regions to obtain an AI model with better generalization and applicable to multiple geographical regions. In this case, multiple geographical regions can be set as feature 3.

Feature 4. Time information of collecting AI data.

For feature 4, different time information of collecting AI data can be distinguished by different time information identifiers, where different time information identifiers can be pre-configured or configured, which is not limited here. For example, different time information identifiers can be distinguished by different collection times of AI data, including a specific time period, daytime/nighttime, weekdays/holidays, etc.

In an implementation example, a possible requirement is that the AI model needs to be processed based on data in a certain time period to obtain an AI model suitable for the time period. In this case, the time period can be set as feature 4.

In another implementation example, a possible requirement is that the AI model needs to be processed based on data from different time periods to obtain an AI model with better generalization and applicable to multiple time periods. In this case, multiple time periods can be set as feature 4.

Feature 5: Number of samples of AI data.

For feature 5, the number of samples of AI data can be distinguished by different sample number identifiers, where the different sample number identifiers can be pre-configured or configured, which is not limited here.

It should be noted that two or more of Features 1 to 5 may be used in combination.

For example, when feature 1 and feature 2 are used in combination, the AI data features of the local data of the first node satisfy the AI data features corresponding to the processing requirements of the first AI model, including: the AI task applied to the local data of the first node and the AI task corresponding to the processing requirements of the AI model are the same AI task, and the object to which the local data of the first node belongs and the object corresponding to the processing requirements of the AI model are the same object.

For another example, when feature 1 and feature 5 are combined, the AI data features of the local data of the first node satisfy the AI data features corresponding to the processing requirements of the first AI model, including: the AI tasks applied by the local data of the first node include at least the AI tasks corresponding to the processing requirements of the AI model, and the number of samples of the local data of the first node is greater than or equal to the number of samples corresponding to the processing requirements of the AI model.

In a possible implementation, the first node processes the first AI model based on local data to obtain the second AI model, including: the first node performs at least one of training processing, distillation processing, and fusion processing on the first AI model based on the local data to obtain the second AI model. Specifically, the first node can perform at least one of the above processing on the first AI model based on local data to improve the flexibility of solution implementation.

In one possible implementation, before the first node processes the first AI model based on the local data in step S302 to obtain the second AI model, the first node may determine the first AI model in a variety of ways. For example, the first AI model may be preconfigured on the first node, or the first AI model may be sent by other nodes (for example, the second node).

The following is an example description of a case where the first AI model is sent through other nodes.

Implementation example 1: before step S302, the second node sends second information, and accordingly, the first node receives the second information, where the second information is used to indicate the first AI model.

It should be understood that the second information is used to indicate the first AI model, which can be understood as: the second information includes the index of the first AI model, so that the recipient of the first information can obtain the first AI model based on the index; or, the second information includes the first AI model, so that the recipient of the first information can obtain the first AI model from the second information.

Specifically, the first node may also receive second information indicating the first AI model, so that the first node can determine the first AI model based on the second information, and process the first AI model to obtain the second AI model.

In a possible implementation manner of implementation example 1, before the first node receives the second information, the method further includes: the first node sends indication information for indicating that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model. Specifically, before the first node receives the second information, the first node may also send indication information for indicating that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, so that the receiver of the indication information (i.e., the sender of the second information) It is clear that the AI data characteristics of the local data of the first node meet the AI data characteristics corresponding to the processing requirements of the first AI model, and the receiving direction is triggered to send second information indicating the first AI model to the first node.

Optionally, when the recipient of the indication information is clear that the AI data characteristics of the local data of the first node do not meet the AI data characteristics corresponding to the processing requirements of the first AI model, the sender of the second information may not send the second information for indicating the first AI model, thereby reducing unnecessary overhead.

Optionally, before the first node receives the second information, the first node may not send indication information for indicating that the AI data characteristics of the local data meet the AI data characteristics corresponding to the processing requirements of the first AI model, that is, the sender of the second information does not need to consider whether the AI data characteristics of the local data of the first node meet the AI data characteristics corresponding to the processing requirements of the first AI model. The first node can decide whether to receive the second information based on the first information and perform AI model processing based on the second information to reduce overhead.

In a possible implementation, the first information and the second information are different fields of the same data packet. Specifically, the first information and the second information can be different fields of the same data packet, so that after receiving the data packet, the first node unpacks the same data packet to obtain the first information and the second information.

In one possible implementation, the first information and the second information are carried on different communication resources. Specifically, the first information and the second information can be carried on different communication resources, so that after the first node determines the AI data features corresponding to the processing requirements of the first AI model based on the first information, when the first node determines that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, the first node receives and parses the second information to obtain the first AI model.

Optionally, the different communication resources may include one or more of different time domain resources, different frequency domain resources, different spatial domain resources, etc. Exemplarily, taking the different communication resources as different time domain resources as an example, as shown in FIG5a, the first information for determining the AI data characteristics corresponding to the processing requirements of the first AI model and the second information for indicating the first AI model can be carried in the same frequency domain position and carried in different time domain positions, such as adjacent or non-adjacent time domain positions (taking "adjacent" as an example in FIG5a).

In a possible implementation of Example 1, the first information includes fourth information, and the fourth information is used to indicate the AI data feature corresponding to the processing requirement of the first AI model. Specifically, the first information received by the first node may include the fourth information, so that the first node can determine the AI data feature corresponding to the processing requirement of the first AI model based on the fourth information, and then determine whether the AI data feature of the local data of the first node meets the AI data feature corresponding to the processing requirement of the first AI model.

In an implementation example, the fourth information includes an identifier (or index) of an AI data feature corresponding to the processing requirement of the first AI model, that is, the fourth information indicates the AI data feature corresponding to the processing requirement of the first AI model by displaying an indication. For example, the first information and the second information may be carried in the same data packet, and accordingly, the first information for determining the AI data feature corresponding to the processing requirement of the first AI model may be the "identification information" in the header of the data packet in FIG. 5b, and the second information for indicating the first AI model may be the payload of the data packet in FIG. 5b.

In another implementation example, the fourth information includes a first orthogonal sequence, the first orthogonal sequence is one of K orthogonal sequences, the K orthogonal sequences respectively correspond to the AI data features corresponding to the K processing requirements of the first AI model (for example, the K orthogonal sequences correspond one-to-one to the AI data features corresponding to the K processing requirements, and the AI data features corresponding to each processing requirement may include at least one of features 1 to 5), K is an integer greater than or equal to 1; that is, the fourth information indicates the AI data features corresponding to the processing requirements of the first AI model by implicit indication. For example, the first information and the second information can be carried in the same data packet, and accordingly, the first information for determining the AI data features corresponding to the processing requirements of the first AI model can be the "sequence" in the packet header of the data packet in Figure 5c, and the second information for indicating the first AI model can be the payload of the data packet in Figure 5c.

Implementation example two: before step S302, the first information received by the first node in step S301 includes third information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information.

Specifically, the first information received by the first node may include third information, so that the first node can implicitly determine the AI data characteristics corresponding to the processing requirements of the first AI model through the first processing information, and determine the first AI model through the third information.

In an implementation example, the first processing information includes a first scrambling sequence, the first scrambling sequence is one of N scrambling sequences, the N scrambling sequences respectively correspond to AI data features corresponding to N processing requirements of the first AI model (for example, the N scrambling sequences correspond one-to-one to the AI data features corresponding to the N processing requirements, and the AI data features corresponding to each processing requirement may include at least one of features 1 to 5), and N is an integer greater than or equal to 1. For example, the third information included in the first information may be a payload obtained by scrambling the first scrambling sequence in FIG. 5d, and accordingly, the first node may perform descrambling processing on the received third information through the first scrambling sequence to determine the AI data features corresponding to the processing requirements of the first AI model.

In another implementation example, the first processing information includes a first key, which is one of M keys, and the M keys correspond to the AI data features corresponding to the M processing requirements of the first AI model (for example, the M keys correspond to the AI data features corresponding to the M processing requirements one by one, and the AI data features corresponding to each processing requirement may include at least one of features 1 to 5), and M is an integer greater than or equal to 1. Among them, any key among the M keys can be a symmetric key (that is, the encryption key for encryption processing by the second node and the decryption key for decryption processing by the first node can be the same key), or an asymmetric key (that is, the encryption key for encryption processing by the second node and the decryption key for decryption processing by the first node can be different keys, for example, the encryption key is a public key and the decryption key is a private key). For example, the third information that can be included in the first information can be the payload obtained by encryption processing by the public key in Figure 5e, and accordingly, the first node can decrypt the received third information by the public key to determine the AI data features corresponding to the processing requirements of the first AI model.

In a possible implementation of Example 2, the first information includes fourth information, and the fourth information is used to indicate the AI data feature corresponding to the processing requirement of the first AI model. In other words, the first information received by the first node in step S301 may include third information and fourth information.

In an implementation example, the third information included in the first information may be a payload obtained by scrambling the first scrambling sequence in FIG. 6, and, compared to the implementation process shown in FIG. 5d, the first information may also include fourth information, and the fourth information may be other information carried on different communication resources from the third information. Accordingly, the first node may determine the AI data features corresponding to the processing requirements of the first AI model through the fourth information and the first scrambling sequence. For example, the fourth information and the first scrambling sequence correspond to the same AI data features corresponding to the processing requirements of the AI model, that is, the first node can determine the AI data features corresponding to the processing requirements of the first AI model through the fourth information, and can also determine the AI data features corresponding to the processing requirements of the first AI model through the first scrambling sequence. For another example, when the AI data features corresponding to the processing requirements of the first AI model include two or more features (for example, at least two of features 1 to 5), the fourth information and the first scrambling sequence respectively correspond to the AI data features corresponding to different AI model processing requirements in the two or more features, that is, the first node can determine the complete AI data features corresponding to the processing requirements of the first AI model through the fourth information and the first scrambling sequence.

It should be understood that the implementation process related to the payload in FIG. 6 may refer to the implementation process shown in FIG. 5 d .

In one possible implementation, in the method shown in FIG. 3, after the first node obtains the second AI model in step S302, the method may further include: the first node sends fifth information, and the fifth information is used to determine the AI data features corresponding to the processing requirements of the second AI model. Specifically, the first node may also send the fifth information so that the recipient of the fifth information can determine the AI data features corresponding to the processing requirements of the second AI model. Thereafter, when the AI data features of the local data of the recipient meet the AI data features corresponding to the processing requirements of the second AI model, the recipient can process the second AI model based on the local data. Thus, the recipient can process the AI model that matches the AI data features of the local data, and accordingly, the AI model can be processed by the node that meets the processing requirements, thereby realizing AI model processing based on AI data feature matching.

It should be noted that the fifth information sent by the first node for determining the AI data characteristics corresponding to the processing requirements of the second AI model can refer to the implementation process of the first information received by the first node in step S301 for determining the AI data characteristics corresponding to the processing requirements of the first AI model.

In addition, for the recipient of the fifth information (for example, the next hop node of the first node), similar to the determination process of the first AI model mentioned above, the recipient can determine the second AI model in a preconfigured manner, or obtain the second AI model through other nodes (for example, the first node).

Similarly, the following will be exemplified by the case where the second AI model is sent through other nodes.

Implementation example A: After step S302, in addition to sending the fifth information, the first node also sends sixth information, where the sixth information is used to indicate the second AI model. Specifically, the first node may also send the sixth information so that the recipient of the sixth information can determine the second AI model based on the sixth information and process the second AI model. The recipient of the sixth information and the recipient of the fifth information may be the same node (e.g., the next hop node of the first node).

In a possible implementation manner of implementation example A, before the first node sends the sixth information, the method further includes: the first node receives indication information for indicating that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the second AI model. Specifically, before the first node sends the sixth information, the first node may also receive indication information from other nodes (such as neighboring nodes) for indicating that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the second AI model, so that the first node clearly Confirm that the AI data characteristics of the local data of the other node meet the AI data characteristics corresponding to the processing requirements of the second AI model, and trigger the first node to send sixth information indicating the second AI model to the other node.

Optionally, when it is clear that the AI data features of the local data of other nodes do not meet the AI data features corresponding to the processing requirements of the first AI model, the first node may not send the second information for indicating the first AI model, thereby reducing unnecessary overhead.

Optionally, before the first node sends the sixth information, the first node may not receive the indication information used to indicate that the AI data characteristics of the local data meet the AI data characteristics corresponding to the processing requirements of the second AI model, that is, the first node does not need to consider whether the AI data characteristics of the local data of the other nodes meet the AI data characteristics corresponding to the processing requirements of the second AI model. The other node can decide whether to receive the sixth information based on the fifth information and perform AI model processing based on the sixth information to reduce overhead.

Optionally, the fifth information and the sixth information are different fields of the same data packet. Specifically, the fifth information and the sixth information can be different fields of the same data packet, so that after receiving the data packet, other nodes can unpack the same data packet to obtain the fifth information and the sixth information.

Optionally, the fifth information and the sixth information are carried on different communication resources. Specifically, the fifth information and the sixth information can be carried on different communication resources, so that after the other node determines the AI data features corresponding to the processing requirements of the second AI model based on the fifth information, when the other node determines that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the second AI model, the other node receives and parses the sixth information to obtain the second AI model.

Optionally, the different communication resources may include one or more of different time domain resources, different frequency domain resources, different spatial domain resources, etc.

In implementation example B, after step S302, the fifth information sent by the first node includes the seventh information; wherein the seventh information is a processing result obtained by processing the second AI model based on the second processing information. Specifically, the fifth information sent by the first node may include the seventh information, so that the receiver of the fifth information can implicitly determine the AI data features corresponding to the processing requirements of the second AI model through the second processing information, and determine the first AI model through the seventh information.

Optionally, the second processing information includes at least one of the following:

a second scrambling sequence, where the second scrambling sequence is one of X scrambling sequences, where the X scrambling sequences respectively correspond to AI data features corresponding to X types of processing requirements of the second AI model, where X is an integer greater than or equal to 1;

The second key is one of Y keys, and the Y keys respectively correspond to AI data features corresponding to Y processing requirements of the second AI model, where Y is an integer greater than or equal to 1.

In a possible implementation, the fifth information includes eighth information, and the eighth information is used to indicate the AI data features corresponding to the processing requirements of the second AI model. Specifically, the fifth information sent by the first node may include the eighth information, so that the receiver of the fifth information can determine the AI data features corresponding to the processing requirements of the second AI model based on the eighth information, and then determine whether the AI data features of the local data of the receiver meet the AI data features corresponding to the processing requirements of the second AI model.

Optionally, the eighth information includes at least one of the following:

an identifier (or index) of the AI data feature corresponding to the processing requirement of the second AI model, that is, the eighth information indicates the AI data feature corresponding to the processing requirement of the second AI model by displaying an indication;

A second orthogonal sequence, where the second orthogonal sequence is one of Z orthogonal sequences, and the Z orthogonal sequences respectively correspond to AI data features corresponding to Z processing requirements of the second AI model, where Z is an integer greater than or equal to 1; that is, the eighth information indicates the AI data features corresponding to the processing requirements of the second AI model by implicit indication.

It should be noted that the implementation method of the above-mentioned fifth information can refer to the implementation method of the above-mentioned first information, the implementation method of the above-mentioned sixth information can refer to the implementation method of the above-mentioned second information, the implementation method of the above-mentioned seventh information can refer to the implementation method of the above-mentioned third information, and the implementation method of the above-mentioned eighth information can refer to the implementation method of the above-mentioned fourth information.

Based on the technical solution shown in FIG3 , after the first node receives the first information in step S301, the first node can determine the AI data features corresponding to the processing requirements of the first AI model based on the first information. Thereafter, when the AI data features of the local data of the first node meet the AI data features corresponding to the processing requirements of the first AI model, the first node processes the first AI model based on the local data in step S302 to obtain a second AI model. In other words, the first node acts as a communication node in the communication system, and the AI data features corresponding to the processing requirements of the first AI model processed by the first node match the AI data features of the local data of the first node. Thus, when the communication node in the communication system acts as a node participating in the AI model processing, the computing power of the communication node can be applied to the AI model processing process in the AI learning system, in order to realize the AI model processing process in the communication network.

In addition, the AI data features of the local data of the first node meet the AI data features corresponding to the processing requirements of the first AI model processed by the first node, so that the first node can process the AI model that matches the AI data features of the local data. Correspondingly, the AI model can also be processed by the node that meets the processing requirements, thereby realizing AI model processing based on AI data feature matching.

Please refer to FIG. 7 , which is an application example of the technical solution shown in FIG. 3 . The method shown in FIG. 7 includes the following steps.

S701. The central node sends first indication information, and correspondingly, the first node receives the first indication information.

S702. The second node sends first information, and correspondingly, the first node receives the first information, wherein the first information is used to determine AI data features corresponding to the processing requirements of the first AI model.

S703. The first node processes the first AI model based on local data to obtain a second AI model.

It should be noted that step S702 and step S703 can refer to the implementation process of step S301 and step S302 above and achieve corresponding technical effects, which will not be elaborated here.

Specifically, the first node may receive first indication information in step S701, and the first indication information is used to indicate that the first information includes third information and/or fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate the AI data features corresponding to the processing requirements of the first AI model. Thus, the first node can receive and/or parse the first information based on the first indication information. Moreover, for the first node, the first node can further determine in step S703 based on the first information that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model. In this case, the first node can process the AI model that matches the AI data features of the local data. Correspondingly, the AI model can also be processed by the node that meets the processing requirements, thereby realizing AI model processing based on AI data feature matching.

Optionally, the first processing information includes at least one of the following:

a first scrambling sequence, where the first scrambling sequence is one of N scrambling sequences, where the N scrambling sequences respectively correspond to AI data features corresponding to N processing requirements of the first AI model, and N is an integer greater than or equal to 1;

The first key is one of M keys, and the M keys respectively correspond to AI data features corresponding to M processing requirements of the first AI model, where M is an integer greater than or equal to 1.

Optionally, the fourth information includes at least one of the following:

an identifier (or index) of the AI data feature corresponding to the processing requirement of the first AI model, that is, the fourth information indicates the AI data feature corresponding to the processing requirement of the first AI model by displaying an indication;

A first orthogonal sequence, where the first orthogonal sequence is one of K orthogonal sequences, and the K orthogonal sequences respectively correspond to AI data features corresponding to K processing requirements of the first AI model, where K is an integer greater than or equal to 1; that is, the fourth information indicates the AI data features corresponding to the processing requirements of the first AI model by implicit indication.

In an implementation example, the first indication information is used to indicate that the first information includes the third information and/or the fourth information, and may include: the first information is used to indicate that the first information is an AI data feature corresponding to the processing requirements of the first AI model determined by the method corresponding to the third information and/or the fourth information. For example, the method corresponding to the third information and/or the fourth information may include the explicit indication method based on identification, the implicit indication method based on orthogonal sequence, the processing method for scrambling and/or descrambling based on the scrambling sequence, and the processing method for encryption and/or decryption based on the key.

In another implementation example, the first indication information is used to indicate that the first information includes the third information and/or the fourth information, and may include: the first information is used to indicate that the first information determines the AI data feature corresponding to the processing requirement of the first AI model based on the first processing information and/or the fourth information. For example, the first indication information may carry one or more fields, which are respectively used to carry one or more of the first scrambling sequence, the first key, the identifier (or index) of the AI data feature corresponding to the processing requirement of the first AI model, and the first orthogonal sequence.

It should be noted that the first processing information and the fourth information corresponding to the third information can refer to the description of other embodiments above, and will not be repeated here.

In a possible implementation of the technical solution shown in FIG7 , for the central node, the central node may also send second indication information to the next-hop node of the first node, the second indication information being used to indicate that the fifth information includes the seventh information and/or the eighth information; wherein the eighth information is a processing result obtained by processing the second AI model based on the second processing information, and the eighth information is used to indicate the AI data features corresponding to the processing requirements of the second AI model. Accordingly, for the next-hop node of the first node, after step S703, the next-hop node may receive the fifth information from the first node, so that the next-hop node can determine, based on the fifth information, that the AI data features of the local data meet the AI data features corresponding to the processing requirements of the second AI model. In this case, the next-hop node can perform AI data processing on the local data. The AI model that matches the characteristics can be processed, and accordingly, the AI model can also be processed by the nodes that meet the processing requirements, thereby realizing AI model processing based on AI data feature matching.

It should be noted that the second processing information corresponding to the seventh information and the eighth information can refer to the description of other embodiments above and will not be repeated here.

In a possible implementation, before the first node receives the first indication information, the method further includes: the first node sends node information, the node information is used to indicate the AI data characteristics of the local data; wherein the node information is used to determine the first indication information. Correspondingly, for the trust node, the central node receives one or more node information, the one or more node information is used to indicate the AI data characteristics of the local data of one or more nodes (including at least the first node, and optionally including the next hop node of the first node); wherein the one or more node information is used to determine the first indication information (and the second indication information that may exist). Specifically, before the central node sends the first indication information, the central node may also receive one or more node information, the one or more node information is used to indicate the AI data characteristics of the local data of one or more nodes. Subsequently, the central node can determine the first indication information based on the node information received from one or more nodes, so that the recipient can determine the first indication information that is compatible with the node information of the one or more nodes.

It should be noted that the implementation process involved in the method shown in FIG. 7 can refer to the description of other embodiments above, and achieve corresponding technical effects, which will not be described in detail here.

Please refer to Figure 8. An embodiment of the present application provides a communication device 800, which can implement the function of the first node or central node (the first node or central node can be a terminal device or a network device) in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment. In the embodiment of the present application, the communication device 800 can be a first node (or central node), or it can be an integrated circuit or component inside the first node (or central node), such as a chip. The following embodiments are described by taking the communication device 800 as the first node (or central node) as an example.

It should be noted that the transceiver unit 802 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

In a possible implementation, when the device 800 is used to execute the method executed by the first node in any of the aforementioned embodiments, the device 800 includes a processing unit 801 and a transceiver unit 802; the transceiver unit 802 is used to receive first information, and the first information is used to determine the AI data features corresponding to the processing requirements of the first AI model; when the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, the processing unit 801 is used to process the first AI model based on the local data to obtain a second AI model.

In a possible implementation, the transceiver unit 802 is further used to receive second information, where the second information is used to indicate the first AI model.

In a possible implementation, the transceiver unit 802 is further used to send indication information indicating that the AI data feature of the local data meets the AI data feature corresponding to the processing requirement of the first AI model.

In a possible implementation manner, the first information and the second information are different fields of the same data packet.

In a possible implementation manner, the first information and the second information are carried on different communication resources.

In a possible implementation, the first information includes third information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information.

In a possible implementation manner, the first processing information includes at least one of the following:

a first scrambling sequence, where the first scrambling sequence is one of N scrambling sequences, where the N scrambling sequences respectively correspond to AI data features corresponding to N processing requirements of the first AI model, and N is an integer greater than or equal to 1;

The first key is one of M keys, and the M keys respectively correspond to AI data features corresponding to M processing requirements of the first AI model, where M is an integer greater than or equal to 1.

In a possible implementation, the first information includes fourth information, and the fourth information is used to indicate AI data features corresponding to the processing requirements of the first AI model.

In a possible implementation manner, the fourth information includes at least one of the following:

An identifier of an AI data feature corresponding to the processing requirement of the first AI model;

A first orthogonal sequence, where the first orthogonal sequence is one of K orthogonal sequences, and the K orthogonal sequences respectively correspond to AI data features corresponding to K types of processing requirements of the first AI model, where K is an integer greater than or equal to 1.

In a possible implementation, the transceiver unit 802 is further used to send fifth information, where the fifth information is used to determine AI data features corresponding to the processing requirements of the second AI model.

In a possible implementation, the transceiver unit 802 is further used to send sixth information, where the sixth information is used to indicate the second AI model.

In a possible implementation, the transceiver unit 802 is further used to receive indication information indicating that the AI data feature of the local data meets the AI data feature corresponding to the processing requirement of the second AI model.

In a possible implementation manner, the fifth information and the sixth information are different fields of the same data packet.

In a possible implementation manner, the fifth information and the sixth information are carried on different communication resources.

In a possible implementation, the fifth information includes seventh information; wherein the seventh information is a processing result obtained by processing the second AI model based on the second processing information.

In a possible implementation manner, the second processing information includes at least one of the following:

a second scrambling sequence, where the second scrambling sequence is one of X scrambling sequences, where the X scrambling sequences respectively correspond to AI data features corresponding to X types of processing requirements of the second AI model, where X is an integer greater than or equal to 1;

The second key is one of Y keys, and the Y keys respectively correspond to AI data features corresponding to Y processing requirements of the second AI model, where Y is an integer greater than or equal to 1.

In a possible implementation, the fifth information includes eighth information, and the eighth information is used to indicate AI data features corresponding to the processing requirements of the second AI model.

In a possible implementation manner, the eighth information includes at least one of the following:

An identifier of an AI data feature corresponding to the processing requirement of the second AI model;

A second orthogonal sequence, where the second orthogonal sequence is an orthogonal sequence among Z orthogonal sequences, and the Z orthogonal sequences respectively correspond to AI data features corresponding to Z processing requirements of the second AI model, where Z is an integer greater than or equal to 1.

In one possible implementation, the transceiver unit 802 is also used to receive first indication information, which is used to indicate that the first information includes third information and/or fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate AI data features corresponding to the processing requirements of the first AI model.

In a possible implementation, the transceiver unit 802 is further used to send node information, where the node information is used to indicate the AI data feature of the local data; wherein the node information is used to determine the first indication information.

In one possible implementation, the AI data feature includes at least one of the following: an identifier of the AI task to which the AI data is applied, an object to which the AI data belongs, geographic location information for collecting the AI data, time information for collecting the AI data, and the number of samples of the AI data.

In one possible implementation, the processing unit 801 is used to process the first AI model based on local data to obtain the second AI model, including: the processing unit 801 is used to perform at least one of training processing, distillation processing and fusion processing on the first AI model based on the local data to obtain the second AI model.

In a possible implementation, when the device 800 is used to execute the method executed by the central node in any of the aforementioned embodiments, the device 800 includes a processing unit 801 and a transceiver unit 802; the processing unit 801 is used to determine the first indication information, and the first indication information is used to indicate that the first information includes the third information and/or the fourth information; wherein the third information is the processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate the AI data features corresponding to the processing requirements of the first AI model. wherein the first information is used to determine the AI data features corresponding to the processing requirements of the first AI model; and the transceiver unit 802 is used to send the first indication information.

In a possible implementation, the transceiver unit 802 is further used to receive one or more node information, where the one or more node information is used to indicate AI data features of local data of one or more nodes; wherein the one or more node information is used to determine the first indication information.

In one possible implementation, the AI data feature includes at least one of the following: an identifier of the AI task to which the AI data is applied, an object to which the AI data belongs, geographic location information for collecting the AI data, time information for collecting the AI data, and the number of samples of the AI data.

It should be noted that the information execution process and other contents of the units of the above-mentioned communication device 800 can be specifically referred to the description in the method embodiment shown in the above-mentioned application, and will not be repeated here.

Please refer to Fig. 9, which is another schematic structural diagram of a communication device 900 provided in the present application. The communication device 900 includes a logic circuit 901 and an input/output interface 902. The communication device 900 may be a chip or an integrated circuit.

The transceiver unit 802 shown in Fig. 8 may be a communication interface, which may be the input/output interface 902 in Fig. 9, which may include an input interface and an output interface. Alternatively, the communication interface may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

Optionally, the input-output interface 902 is used to receive first information, and the first information is used to determine AI data features corresponding to the processing requirements of the first AI model; when the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, the logic circuit 901 is used to process the first AI model based on the local data to obtain a second AI model.

Optionally, the logic circuit 901 is used to determine first indication information, the first indication information is used to indicate that the first information includes third information and/or fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate AI data features corresponding to the processing requirements of the first AI model. The first information is used to determine the AI data features corresponding to the processing requirements of the first AI model; the input and output interface 902 is used to send the first indication information.

The logic circuit 901 and the input/output interface 902 may also execute other steps executed by the first node or the central node in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

In a possible implementation, the processing unit 801 shown in FIG. 8 may be the logic circuit 901 in FIG. 9 .

Optionally, the logic circuit 901 may be a processing device, and the functions of the processing device may be partially or completely implemented by software. The functions of the processing device may be partially or completely implemented by software.

Optionally, the processing device 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 corresponding processing and/or steps in any one of the method embodiments.

Alternatively, the processing device may include only a processor. A memory for storing a computer program is located outside the processing device, and the processor is connected to the memory via a circuit/wire to read and execute the computer program stored in the memory. The memory and the processor may be integrated together, or may be 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 (FPGA), application specific integrated circuits (ASIC), system on chip (SoC), central processor unit (CPU), network processor (NP), digital signal processor (DSP), microcontroller unit (MCU), programmable logic device (PLD) or other integrated chips, or any combination of the above chips or processors.

Please refer to Figure 10, which shows a communication device 1000 involved in the above embodiments provided in an embodiment of the present application. The communication device 1000 can specifically be a communication device as a terminal device in the above embodiments. The example shown in Figure 10 is that the terminal device is implemented through the terminal device (or a component in the terminal device).

Among them, a possible logical structure diagram of the communication device 1000 is shown, and the communication device 1000 may include but is not limited to at least one processor 1001 and a communication port 1002.

The transceiver unit 802 shown in Fig. 8 may be a communication interface, which may be the communication port 1002 in Fig. 10, which may include an input interface and an output interface. Alternatively, the communication port 1002 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

Further optionally, the device may also include at least one of a memory 1003 and a bus 1004 . In an embodiment of the present application, the at least one processor 1001 is used to control and process the actions of the communication device 1000 .

In addition, the processor 1001 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 device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute 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 a computing function, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and the like. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

It should be noted that the communication device 1000 shown in Figure 10 can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment, and to achieve the corresponding technical effects of the terminal device. The specific implementation methods of the communication device shown in Figure 10 can refer to the description in the aforementioned method embodiment, and will not be repeated here one by one.

Please refer to Figure 11, which is a structural diagram of the communication device 1100 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 1100 can specifically be a communication device as a network device in the above-mentioned embodiments. The example shown in Figure 11 is that the network device is implemented through the network device (or a component in the network device), wherein the structure of the communication device can refer to the structure shown in Figure 11.

The communication device 1100 includes at least one processor 1111 and at least one network interface 1114. Further optionally, the communication device also includes at least one memory 1112, at least one transceiver 1113 and one or more antennas 1115. The processor 1111, the memory 1112, the transceiver 1113 and the network interface 1114 are connected, for example, through a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 1115 is connected to the transceiver 1113. The network interface 1114 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1114 may include a network interface between the communication device and the core network device, such as an S1 interface, and the network interface may include a network interface between the communication device and other communication devices (such as other network devices or core network devices), such as an X2 or Xn interface.

The transceiver unit 802 shown in Fig. 8 may be a communication interface, which may be the network interface 1114 in Fig. 11, and the network interface 1114 may include an input interface and an output interface. Alternatively, the network interface 1114 may also be a transceiver circuit, and the transceiver circuit may include an input interface circuit and an output interface circuit.

The processor 1111 is mainly used to process the communication protocol and communication data, and to control the entire communication device, execute the software program, and process the data of the software program, for example, to support the communication device to perform the actions described in the embodiment. The communication device may include a baseband processor and a central processing unit. The baseband processor is mainly used to process the communication protocol and communication data, and the central processing unit is mainly used to control the entire terminal device, execute the software program, and process the data of the software program. The processor 1111 in Figure 11 can integrate the functions of the baseband processor and the central processing unit. It can be understood by those skilled in the art that the baseband processor and the central processing unit can also be independent processors, interconnected by technologies such as buses. It can be understood by those skilled in the art that the terminal device can include multiple baseband processors to adapt to different network formats, and the terminal device can include multiple central processing units to enhance its processing capabilities. The various components of the terminal device can be connected through various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The central processing unit can also be described as a central processing circuit or a central processing chip. The function of processing the communication protocol and communication data can be built into the processor, or it can be stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.

The memory is mainly used to store software programs and data. The memory 1112 can be independent and connected to the processor 1111. Optionally, the memory 1112 can be integrated with the processor 1111, for example, integrated into a chip. Among them, the memory 1112 can store program codes for executing the technical solutions of the embodiments of the present application, and the execution is controlled by the processor 1111. The various types of computer program codes executed can also be regarded as drivers of the processor 1111.

FIG11 shows only one memory and one processor. In an actual terminal device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device, etc. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the embodiments of the present application.

The transceiver 1113 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal, and the transceiver 1113 can be connected to the antenna 1115. The transceiver 1113 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1115 can receive radio frequency signals, and the receiver Rx of the transceiver 1113 is used to receive the radio frequency signal from the antenna, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or the digital intermediate frequency signal to the processor 1111, so that the processor 1111 further processes the digital baseband signal or the digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1113 is also used to receive a modulated digital baseband signal or a digital intermediate frequency signal from the processor 1111, and convert the modulated digital baseband signal or the digital intermediate frequency signal into a radio frequency signal, and send the radio frequency signal through one or more antennas 1115. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion processing on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal, and the order of the down-mixing and analog-to-digital conversion processing is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion processing on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal, and the order of the up-mixing and digital-to-analog conversion processing is adjustable. The digital baseband signal and the digital intermediate frequency signal can be collectively referred to as a digital signal.

The transceiver 1113 may also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, a device in the transceiver unit for implementing a receiving function may be regarded as a receiving unit, and a device in the transceiver unit for implementing a sending function may be regarded as a sending unit, that is, the transceiver unit includes a receiving unit and a sending unit, the receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the sending unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

It should be noted that the communication device 1100 shown in Figure 11 can be specifically used to implement the steps implemented by the network equipment in the aforementioned method embodiment, and to achieve the corresponding technical effects of the network equipment. The specific implementation methods of the communication device 1100 shown in Figure 11 can refer to the description in the aforementioned method embodiment, and will not be repeated here.

Please refer to FIG. 12 , which is a schematic diagram of the structure of the communication device involved in the above-mentioned embodiment provided in an embodiment of the present application.

It can be understood that the communication device 120 includes, for example, modules, units, elements, circuits, or interfaces, etc., which are appropriately configured together to perform the technical solutions provided in the present application. The communication device 120 may be the RAN node, terminal, core network device or other network device described above, or a component (such as a chip) in these devices, to implement the method described in the following method embodiment. The communication device 120 includes one or more processors 121. The processor 121 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control the communication device (such as a RAN node, terminal, or chip, etc.), execute software programs, and process data of software programs.

Optionally, in one design, the processor 121 may include a program 123 (sometimes also referred to as code or instruction), and the program 123 may be executed on the processor 121 so that the communication device 120 performs the method described in the following embodiments. In another possible design, the communication device 120 includes a circuit (not shown in FIG. 12 ).

Optionally, the communication device 120 may include one or more memories 122 on which a program 124 (sometimes also referred to as code or instructions) is stored. The program 124 can be run on the processor 121 so that the communication device 120 executes the method described in the above method embodiment.

Optionally, the processor 121 and/or the memory 122 may include an AI module 127, 128, and the AI module is used to implement AI-related functions. The AI module may be implemented by software, hardware, or a combination of software and hardware. For example, the AI module may include a wireless intelligent control (radio intelligence control, RIC) module. For example, the AI module may be a near real-time RIC or a non-real-time RIC.

Optionally, data may also be stored in the processor 121 and/or the memory 122. The processor and the memory may be provided separately or integrated together.

Optionally, the communication device 120 may further include a transceiver 125 and/or an antenna 126. The processor 121 may also be sometimes referred to as a processing unit, which controls the communication device (e.g., a RAN node or a terminal). The transceiver 125 may also be sometimes referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., which is used to implement the transceiver function of the communication device through the antenna 126.

The transceiver unit 802 shown in Fig. 8 may be a communication interface, which may be the transceiver 125 in Fig. 12, and the transceiver 125 may include an input interface and an output interface. Alternatively, the transceiver 125 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

An embodiment of the present application also provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation method of the first node or the central node in the above embodiment.

An embodiment of the present application also provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes the method of the possible implementation mode of the above-mentioned first node or central node.

The embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device. Optionally, the chip system also includes an interface circuit, which provides program instructions and/or data for the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, or it can include chips and other discrete devices, wherein the communication device can specifically be the first node or central node in the aforementioned method embodiment.

An embodiment of the present application also provides a communication system, which includes a first node and a second node in any of the above embodiments, where the first node can be a terminal device or a network device, and the second node can also be a terminal device or a network device.

Optionally, the communication system may further include a central node, which may also be a terminal device or a network device.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of 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 the present application is essentially or the part that contributes or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk.

Claims (32)

A communication method, comprising: Receive first information, where the first information is used to determine AI data features corresponding to a processing requirement of a first AI model; When the AI data features of the local data meet the AI data features corresponding to the processing requirements of the first AI model, the first AI model is processed based on the local data to obtain a second AI model. The method according to claim 1, characterized in that before processing the first AI model based on the local data to obtain the second AI model, the method further comprises: Second information is received, where the second information is used to indicate the first AI model. The method according to claim 2, characterized in that before receiving the second information, the method further comprises: Sending indication information for indicating that the AI data feature of the local data satisfies the AI data feature corresponding to the processing requirement of the first AI model. The method according to claim 2 or 3 is characterized in that the first information and the second information are different fields of the same data packet. The method according to any one of claims 2 to 4 is characterized in that the first information and the second information are carried on different communication resources. The method according to claim 1 is characterized in that the first information includes third information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information. The method according to claim 6, wherein the first processing information includes at least one of the following: A first scrambling sequence, where the first scrambling sequence is one of N scrambling sequences, where the N scrambling sequences respectively correspond to AI data features corresponding to N processing requirements of the first AI model, and N is an integer greater than or equal to 1; A first key, where the first key is one of M keys, and the M keys respectively correspond to AI data features corresponding to M processing requirements of the first AI model, where M is an integer greater than or equal to 1. The method according to any one of claims 1 to 7 is characterized in that the first information includes fourth information, and the fourth information is used to indicate AI data characteristics corresponding to the processing requirements of the first AI model. The method according to claim 8, characterized in that the fourth information includes at least one of the following: an identifier of an AI data feature corresponding to the processing requirement of the first AI model; A first orthogonal sequence, wherein the first orthogonal sequence is one of K orthogonal sequences, and the K orthogonal sequences respectively correspond to AI data features corresponding to K types of processing requirements of the first AI model, where K is an integer greater than or equal to 1. The method according to any one of claims 1 to 9, characterized in that the method further comprises: Send fifth information, where the fifth information is used to determine AI data features corresponding to the processing requirements of the second AI model. The method according to claim 10, characterized in that the method further comprises: Send sixth information, where the sixth information is used to indicate the second AI model. The method according to claim 11, characterized in that before sending the sixth information, the method further comprises: Indication information is received, indicating that the AI data feature of the local data satisfies the AI data feature corresponding to the processing requirement of the second AI model. The method according to claim 11 or 12 is characterized in that the fifth information and the sixth information are different fields of the same data packet. The method according to any one of claims 11 to 13 is characterized in that the fifth information and the sixth information are carried on different communication resources. The method according to claim 10, characterized in that the fifth information includes seventh information; Among them, the seventh information is a processing result obtained by processing the second AI model based on the second processing information. The method according to claim 15, wherein the second processing information includes at least one of the following: a second scrambling sequence, where the second scrambling sequence is one of X scrambling sequences, where the X scrambling sequences respectively correspond to AI data features corresponding to X types of processing requirements of the second AI model, and X is an integer greater than or equal to 1; The second key is one of Y keys, and the Y keys respectively correspond to AI data features corresponding to Y processing requirements of the second AI model, where Y is an integer greater than or equal to 1. The method according to any one of claims 10 to 16 is characterized in that the fifth information includes eighth information, and the eighth information is used to indicate AI data characteristics corresponding to the processing requirements of the second AI model. The method according to claim 17, characterized in that the eighth information includes at least one of the following: an identifier of an AI data feature corresponding to the processing requirement of the second AI model; A second orthogonal sequence, wherein the second orthogonal sequence is one of Z orthogonal sequences, and the Z orthogonal sequences respectively correspond to AI data features corresponding to Z processing requirements of the second AI model, where Z is an integer greater than or equal to 1. The method according to any one of claims 1 to 18, characterized in that before receiving the first information, the method further comprises: Receive first indication information, where the first indication information is used to indicate that the first information includes third information and/or fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate AI data features corresponding to the processing requirements of the first AI model. The method according to claim 19, characterized in that before receiving the first indication information, the method further comprises: Sending node information, where the node information is used to indicate AI data features of the local data; wherein the node information is used to determine the first indication information. The method according to any one of claims 1 to 20, characterized in that the AI data feature includes at least one of the following: The identification of the AI task to which the AI data is applied, the object to which the AI data belongs, the geographical location information of when the AI data was collected, the time information of when the AI data was collected, and the number of samples of the AI data. The method according to any one of claims 1 to 21, characterized in that the processing of the first AI model based on local data to obtain the second AI model comprises: Based on the local data, the first AI model is subjected to at least one of training processing, distillation processing and fusion processing to obtain the second AI model. A communication method, comprising: Determine first indication information, where the first indication information is used to indicate that the first information includes third information and/or fourth information; wherein the third information is a processing result obtained by processing the first AI model based on the first processing information, and the fourth information is used to indicate AI data features corresponding to the processing requirements of the first AI model; wherein the first information is used to determine the AI data features corresponding to the processing requirements of the first AI model; Send the first indication information. The method according to claim 23, characterized in that the method further comprises: Receive one or more node information, where the one or more node information is used to indicate AI data features of local data of one or more nodes; wherein the one or more node information is used to determine the first indication information. The method according to claim 23 or 24, characterized in that the AI data feature includes at least one of the following: The identification of the AI task to which the AI data is applied, the object to which the AI data belongs, the geographical location information of when the AI data was collected, the time information of when the AI data was collected, and the number of samples of the AI data. The method according to any one of claims 23 to 25, characterized in that the first processing information includes at least one of the following: A first scrambling sequence, where the first scrambling sequence is one of N scrambling sequences, where the N scrambling sequences respectively correspond to AI data features corresponding to N processing requirements of the first AI model, and N is an integer greater than or equal to 1; A first key, where the first key is one of M keys, and the M keys respectively correspond to AI data features corresponding to M processing requirements of the first AI model, where M is an integer greater than or equal to 1. The method according to any one of claims 23 to 26, characterized in that the fourth information includes at least one of the following: an identifier of an AI data feature corresponding to the processing requirement of the first AI model; A first orthogonal sequence, wherein the first orthogonal sequence is one of K orthogonal sequences, and the K orthogonal sequences respectively correspond to AI data features corresponding to K types of processing requirements of the first AI model, where K is an integer greater than or equal to 1. A communication device, characterized in that it comprises a module for executing the method according to any one of claims 1 to 27. A communication device, characterized in that it comprises at least one processor, wherein the at least one processor is coupled to a memory; the at least one processor is used to execute the method as described in any one of claims 1 to 27. The communication device according to claim 29 is characterized in that the communication device is a chip or a chip system. A readable storage medium, characterized in that a computer program or instruction is stored in the storage medium, and when the computer program or instruction is executed by a communication device, the method as described in any one of claims 1 to 27 is implemented. A computer program product, characterized in that it comprises instructions, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 27.