Communication method and related device

Abstract

A communication method and a related device, for use in enabling the computing power of communication nodes to be applied to neural network-based artificial intelligence (AI) processing while also enhancing the flexibility of neural network deployment. In the method, first data received by a first communication apparatus is data having undergone first processing, and second data sent by the first communication apparatus is gradient data obtained based on data obtained by performing second processing on the first data and label data; or the second data is data having undergone first processing, and the first data is gradient data obtained based on data obtained by performing second processing on the second data and label data. Moreover, the first processing comprises AI processing, and / or the second processing comprises AI processing. In other words, the second data is gradient data corresponding to data obtained by performing AI processing on the basis of the first data, or the first data is gradient data corresponding to data obtained by performing AI processing on the basis of the second data.

Description

A communication method and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on November 3, 2023, with application number 202311462850.9 and application name “A Communication Method and Related Equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communications, and in particular to a communication method and related equipment. Background Art

[0003] Wireless communication can be the transmission communication between two or more communication nodes without propagating through conductors or cables. The communication nodes generally include network devices and terminal devices.

[0004] Currently, in wireless communication systems, communication nodes generally possess both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., processing both sending and receiving signals), enabling communication between the network device and other communication nodes.

[0005] However, in communication networks, communication nodes may have excess computing power beyond just supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

[0006] Summary of the Invention

[0007] The present application provides a communication method and related equipment for enabling the computing power of communication nodes to be applied to artificial intelligence (AI) processing of neural networks while also improving the flexibility of neural network deployment.

[0008] The first aspect of the present application provides a communication method, which is performed by a first communication device, which may be a communication device (such as a terminal device), or the first communication device may be a partial component in the communication device (such as a processor, chip or chip system, etc.), or the first communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the first communication device receives configuration information, which is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the first communication device receives the first data based on the first information; the first communication device sends second information, which is used to schedule the transmission of the second data; the first communication device sends the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0009] Based on the above technical solution, the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and the label data. In addition, the first processing includes AI processing, and / or the second processing includes AI processing. In other words, the second data is gradient data corresponding to data obtained by performing AI processing on the first data, or the first data is gradient data corresponding to data obtained by performing AI processing on the second data. Thus, when the communication device in the communication system serves as an AI participating node, the computing power of the communication device can be applied to AI processing while also improving the flexibility of AI deployment.

[0010] In addition, the configuration information received by the first communication device is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data. Accordingly, the first communication device can receive the first data based on the first information; and after the first communication device sends the second information for scheduling the transmission of the second data, the first communication device can send the second data based on the second information. In other words, when the first data and / or the second data are data associated with AI processing, the first communication device can realize the transmission of the data associated with AI processing based on the scheduling of the first information and the second information. Thus, by scheduling the data associated with AI processing through the first information and the second information, the transmission success rate of the data associated with AI processing can be improved.

[0011] In this application, since the first processing includes AI processing, and / or the second processing includes AI processing, gradient data and / or loss function results can be obtained based on the data after the first or second processing and the label data. Accordingly, in this application, gradient data can be replaced by the result of the loss function, gradient data and loss function results, etc.

[0012] In this application, terms such as AI, neural network, AI neural network, machine learning, AI processing, and AI neural network processing can be used interchangeably.

[0013] In this application, the data involved (such as first data, second data, etc.) can be replaced by information, signals, etc.

[0014] It should be understood that the second data can be data obtained based on the first data. For example, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, the first data is the data sent by the sender of the first data (e.g., the second communication device) after the first processing, and the second data is the data obtained by the first communication device based on the received first data after the second processing. In this case, the first data can be called forward data, and the second data can be called reverse data (e.g., reverse gradient, the result of the loss function, etc.).

[0015] It should be understood that the first data can be data obtained based on the second data. For example, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data, the second data is data sent by the first communication device after the first processing, and the first data is data obtained by the sender of the first data (e.g., the second communication device) performing the second processing based on the received second data. In this case, the second data can be called forward data, and the first data can be called reverse data (e.g., reverse gradient, the result of the loss function, etc.).

[0016] Optionally, in the above process, if the first processing includes AI processing, the AI ​​processing in the first processing may be referred to as encoding neural network processing, AI encoder processing, AI encoding neural network processing, etc. Correspondingly, if the second processing includes AI processing, the AI ​​processing in the second processing may be referred to as decoding neural network processing, AI decoder processing, AI decoding neural network processing, etc.

[0017] It should be noted that, in a wireless communication system, the first information used to schedule the first data and / or the second information used to schedule the second data can be messages / signaling / information of the radio resource control (RRC) layer, the medium access control (MAC) layer, the physical (PHY) layer or other protocol layers.

[0018] In addition, compared to the implementation method in which the communication device processes the received application layer data through the physical layer and then dequantizes the physical layer processing results to obtain the application layer scheduling signaling (the scheduling signaling is used to schedule the transmission of data associated with AI processing), when the first information used to schedule the first data and / or the second information used to schedule the second data is physical layer signaling, the transmission of data associated with AI processing can be quickly scheduled through physical layer signaling, thereby reducing the processing delay.

[0019] For example, the first information for scheduling the first data comes from a second communication device. The second communication device may be a network device, and accordingly, the first information may be downlink control information (DCI) sent by the network device to the terminal device, and the second information may be uplink control information (UCI) sent by the terminal device to the network device. Alternatively, the second communication device may be a terminal device different from the first communication device, and accordingly, the first information and the second information may be sidelink control information (SCI) exchanged between different terminal devices.

[0020] In a possible implementation manner of the first aspect, the transmission resource of the first information includes a time domain resource carrying the first information; wherein, the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

[0021] Based on the above technical solution, the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured. In this way, after receiving the first information, the first communication device can determine the time domain resource carrying the second information based on the preconfigured time interval and the time domain resource carrying the first information, and transmit the second information on the time domain resource carrying the second information. Furthermore, the second communication device can also receive the second information based on the preconfigured time domain resource, thereby reducing the resource configuration overhead of the second information.

[0022] In a possible implementation of the first aspect, the first data is data after first processing and the second data is gradient data obtained based on the data after second processing on the first data and label data; the first processing satisfies any of the following: the first processing is triggered based on the second information, and the first information is triggered based on the first processing; the second processing satisfies any of the following: the second processing is triggered based on the first information, the second information is triggered based on the second processing, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data.

[0023] In a possible implementation of the first aspect, the second data is data after first processing and the first data is gradient data obtained based on the second data after second processing and label data; the first processing satisfies any of the following: the first processing is triggered based on the first information, and the second information is triggered based on the first processing; the second processing satisfies any of the following: the second processing is triggered based on the second information, the first information is triggered based on the second processing, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data.

[0024] Based on the above technical solution, since the first processing includes AI processing, and / or the second processing includes AI processing, the above implementation method can trigger the scheduling of AI data through AI processing, or the above implementation method can trigger AI processing through the scheduling of AI data. Thus, the AI ​​processing and the scheduling of AI data can trigger each other, thereby reducing the interaction of triggering instructions for AI processing or triggering instructions for scheduling of AI data, which can reduce processing latency and reduce overhead. Alternatively, the above implementation method can trigger AI processing through the transmission of AI data. Thus, the AI ​​processing and the transmission of AI data can trigger each other, thereby reducing the interaction of triggering instructions for AI processing, which can reduce processing latency and reduce overhead.

[0025] In a possible implementation of the first aspect, the configuration information includes a first configuration and a second configuration, the first configuration is used to configure the search space of the first information, and the second configuration is used to configure the retransmission timer of the second information; wherein the time length of the period corresponding to the search space is less than or equal to the time length of the retransmission timer.

[0026] Based on the above technical solution, the configuration information for configuring the transmission resources of the first information may include a first configuration and a second configuration, wherein the first configuration is used to configure a search space for the first information, and the second configuration is used to configure a retransmission timer for the second information. In this way, the first communication device can receive the first information and retransmit the second information based on the configuration information, thereby improving the transmission success rate of the first information and the second information.

[0027] Furthermore, since the first information can be mutually triggered with the AI ​​processing, that is, the first information may be executed irregularly. Therefore, by implementing a method in which the duration of the period corresponding to the search space is less than or equal to the duration of the retransmission timer, the first communication device can detect the first information over a shorter duration, so that the first communication device can promptly receive the AI-processed data or trigger the AI ​​processing. Furthermore, a longer timer can reduce the overhead of the first communication device retransmitting the second information.

[0028] In a possible implementation manner of the first aspect, the method further includes: the first communication device sending first indication information, where the first indication information is used to indicate whether the first information is received correctly.

[0029] In this application, whether it is received correctly can be replaced by other terms, including but not limited to: whether it is received incorrectly, whether it is parsed correctly, whether it is parsed incorrectly, etc.

[0030] Based on the above technical solution, the first communication device can also send a first indication message, so that the second communication device can clarify whether the first communication device correctly receives the first information based on the first indication message, and subsequently the second communication device can determine whether to retransmit the first information and / or the first data scheduled by the first information based on the first indication message.

[0031] In addition, the first information is used to schedule the first data. Generally, the receiver of the first information and the first data provides feedback on whether the first data is correctly received, and the receiver does not provide feedback on whether the first information is correctly received. In the above technical solution, if the first information can be used to trigger AI processing (such as the first processing and / or the second processing), the first indication information indicates whether the first information is correctly received, so that the receiver of the first indication information can clearly understand whether the first communication device triggers the corresponding AI processing based on the first information.

[0032] In a possible implementation of the first aspect, the first indication information indicates that the first information is correctly received. When the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the first indication information is also used to trigger the first processing; the first indication information indicates that the first information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data, the first indication information is also used to trigger the second processing.

[0033] Based on the above technical solution, when the first indication information is used to indicate the correct receipt of the first information, the first indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing). Thus, by indicating the correct receipt of the first information through the first indication information, the recipient of the first indication information can trigger the corresponding AI processing based on the first indication information.

[0034] In a possible implementation manner of the first aspect, the first indication information is further used to indicate whether to perform processing based on the first data.

[0035] Optionally, when the first data is data that has undergone a first process and the second data is gradient data obtained based on the first data after undergoing a second process and label data, the first data may be data obtained based on the second data. To this end, the first indication information may further indicate whether processing is performed based on the first data. This can be understood as indicating whether the first indication information may further indicate whether the first communication device performs further processing based on the first data after undergoing a second process and label data to obtain the gradient data.

[0036] Optionally, when the second data is data that has undergone first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second data may be data obtained based on the first data. To this end, the first indication information may further indicate whether processing is performed based on the first data. This can be understood as indicating whether the first indication information may further indicate whether the first communication device performs gradient update processing based on the first data (i.e., gradient data).

[0037] Based on the above technical solution, the first indication information is used not only to indicate whether the first information is correctly received, but also to indicate whether processing is performed based on the first data. In this way, the first indication information can be reused to implement more indications, thereby reducing overhead.

[0038] In a possible implementation manner of the first aspect, after the first communication device sends the second information, the method further includes: the first communication device receives second indication information, where the second indication information is used to indicate whether the second information is received correctly.

[0039] Based on the above technical solution, after the first communication device sends the second information, the first communication device can also receive second indication information, so that the first communication device can determine whether the second communication device correctly receives the second information based on the second indication information. Subsequently, the first communication device can determine whether to retransmit the second information and / or the second data scheduled by the second information based on the first indication information.

[0040] In a possible implementation of the first aspect, the second indication information indicates that the second information is correctly received. When the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing and label data of the first data, the second indication information is also used to trigger the second processing; when the second indication information indicates that the second information is correctly received, when the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and label data of the second data, the second indication information is used to trigger the first processing.

[0041] Based on the above technical solution, when the second indication information is used to indicate the correct receipt of the second information, the second indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing). Thus, by indicating the correct receipt of the second information through the second indication information, the recipient of the second indication information can trigger the corresponding AI processing based on the second indication information.

[0042] In a possible implementation manner of the first aspect, the second indication information is further used to indicate whether to perform processing based on the second data.

[0043] Optionally, when the first data is data that has undergone a first process and the second data is gradient data obtained based on the first data after the second process and the label data, the first data may be data obtained based on the second data. To this end, the second indication information may further indicate whether processing is performed based on the second data. This can be understood as indicating whether the first indication information may further indicate whether the second communication device performs gradient update processing based on the second data (i.e., gradient data).

[0044] Optionally, when the second data is data that has undergone the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second data may be data obtained based on the first data. To this end, the second indication information may further indicate whether processing is performed based on the second data. This can be understood as indicating whether the second indication information may further indicate whether the second communication device performs further processing based on the second data after the second processing and label data to obtain the gradient data.

[0045] Based on the above technical solution, the second indication information is used not only to indicate whether the second information is correctly received, but also to indicate whether processing is performed based on the second data. In this way, the second indication information can be reused to implement more indications, thereby reducing overhead.

[0046] In a possible implementation manner of the first aspect, the second information is further used to indicate at least one of the following: a data type of the second data, and whether to send gradient information determined based on the second data.

[0047] Based on the above technical solution, the second information is also used to indicate at least one of the above items, so that the recipient of the second information (i.e., the second communication device) can obtain other information associated with the second data based on the second information, and assist in subsequent processing of the second data based on the other information.

[0048] In a possible implementation manner of the first aspect, the first information is further used to indicate at least one of the following: a data type of the first data, and whether to send gradient information determined based on the first data.

[0049] Based on the above technical solution, the first information is also used to indicate at least one of the above items, so that the recipient of the first information (i.e., the first communication device) can obtain other information associated with the first data based on the first information, and assist in subsequent processing of the first data based on the other information.

[0050] In a possible implementation manner of the first aspect, after the first communication device receives the first information, the method further includes: in a case where a parsing error occurs in the first information, the first communication device determines not to receive the first data.

[0051] Optionally, after the first communication device receives the first information, the method further includes: in case of a parsing error of the first information, the first communication device does not expect to receive the first data.

[0052] Based on the above technical solution, in the case of an error in parsing the first information, the first communication device may determine not to receive the first data to avoid receiving erroneous data.

[0053] In a possible implementation of the first aspect, the method further includes: the first communication device sending capability information of the first communication device, where the capability information of the first communication device is used to determine the configuration information; wherein the AI ​​capability information of the first communication device includes at least one of the following: processing delay information of the first communication device for forward data of the AI ​​network structure to which the first data belongs, processing delay information of the first communication device for reverse data in the AI ​​network structure to which the first data belongs, the batch size of the first data, load information of the processing resources of the first communication device, and computing power resource information of the first communication device.

[0054] Based on the above technical solution, the first communication device can send the capability information of the first communication device, so that the second communication device determines the configuration information adapted to the capability information based on the capability information, so that the first communication device can receive the first information based on the success rate of the configuration information.

[0055] Optionally, the configuration information includes at least one of the following: a period of the first information, a window duration for detecting the first information, and a position of a transmission symbol of the first information in a time slot. Exemplarily, the configuration information may include a first configuration, the at least one item may be included in the first configuration, and the first configuration is used to configure a search space for the first information.

[0056] In a possible implementation of the first aspect, the method further includes: the first communication device receiving third information, where the third information is used to indicate at least one of the following: information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs.

[0057] Based on the above technical solution, the first communication device may also receive third information indicating at least one of the above items, so that the first communication device can perform subsequent AI processing based on the third information.

[0058] The second aspect of the present application provides a communication method, which is performed by a second communication device, which can be a communication device (such as a terminal device or a network device), or the second communication device can be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the second communication device can also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the second communication device sends configuration information, which is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the second communication device sends the first data based on the first information; the second communication device receives the second information, which is used to schedule the transmission of the second data; the second communication device receives the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and the label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0059] Based on the above technical solution, the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and the label data. In addition, the first processing includes AI processing, and / or the second processing includes AI processing. In other words, the second data is gradient data corresponding to data obtained by performing AI processing on the first data, or the first data is gradient data corresponding to data obtained by performing AI processing on the second data. Thus, when the communication device in the communication system serves as an AI participating node, the computing power of the communication device can be applied to AI processing while also improving the flexibility of AI deployment.

[0060] In addition, the configuration information sent by the second communication device is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data. Accordingly, the second communication device can send the first data based on the first information; and after the second communication device receives the second information for scheduling the transmission of the second data, the second communication device can receive the second data based on the second information. In other words, when the first data and / or the second data are data associated with AI processing, the second communication device can realize the transmission of the data associated with AI processing based on the scheduling of the first information and the second information. Thus, by scheduling the data associated with AI processing through the first information and the second information, the transmission success rate of the data associated with AI processing can be improved.

[0061] In a possible implementation manner of the second aspect, the transmission resource of the first information includes a time domain resource carrying the first information; wherein, the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

[0062] Based on the above technical solution, the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured. In this way, after the second communication device sends the first information, the first communication device can determine the time domain resource carrying the second information based on the preconfigured time interval and the time domain resource carrying the first information, and transmit the second information on the time domain resource carrying the second information. Furthermore, the second communication device can also receive the second information based on the preconfigured time domain resource, thereby reducing the resource configuration overhead of the second information.

[0063] In a possible implementation of the second aspect, the first data is data after first processing and the second data is gradient data obtained based on the data after second processing of the first data and label data; the first processing satisfies any of the following: the first processing is triggered based on the second information, and the first information is triggered based on the first processing; the second processing satisfies any of the following: the second processing is triggered based on the first information, the second information is triggered based on the second processing, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data.

[0064] In a possible implementation of the second aspect, the second data is data after first processing and the first data is gradient data obtained based on the second data after second processing and label data; the first processing satisfies any of the following: the first processing is triggered based on the first information, and the second information is triggered based on the first processing; the second processing satisfies any of the following: the second processing is triggered based on the second information, the first information is triggered based on the second processing, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data.

[0065] Based on the above technical solution, since the first processing includes AI processing, and / or the second processing includes AI processing, the above implementation method can trigger the scheduling of AI data through AI processing, or the above implementation method can trigger AI processing through the scheduling of AI data. Thus, the AI ​​processing and the scheduling of AI data can trigger each other, thereby reducing the interaction of triggering instructions for AI processing or triggering instructions for scheduling of AI data, which can reduce processing latency and reduce overhead. Alternatively, the above implementation method can trigger AI processing through the transmission of AI data. Thus, the AI ​​processing and the transmission of AI data can trigger each other, thereby reducing the interaction of triggering instructions for AI processing, which can reduce processing latency and reduce overhead.

[0066] In a possible implementation of the second aspect, the configuration information includes a first configuration and a second configuration, the first configuration is used to configure the search space of the first information, and the second configuration is used to configure the retransmission timer of the second information; wherein the time length of the period corresponding to the search space is less than or equal to the time length of the retransmission timer.

[0067] Based on the above technical solution, the configuration information for configuring the transmission resources of the first information may include a first configuration and a second configuration, wherein the first configuration is used to configure a search space for the first information, and the second configuration is used to configure a retransmission timer for the second information. In this way, the first communication device can receive the first information and retransmit the second information based on the configuration information, thereby improving the transmission success rate of the first information and the second information.

[0068] Furthermore, since the first information can be mutually triggered with the AI ​​processing, that is, the first information may be executed irregularly. Therefore, by implementing a method in which the duration of the period corresponding to the search space is less than or equal to the duration of the retransmission timer, the first communication device can detect the first information over a shorter duration, so that the first communication device can promptly receive the AI-processed data or trigger the AI ​​processing. Furthermore, a longer timer can reduce the overhead of the first communication device retransmitting the second information.

[0069] In a possible implementation manner of the second aspect, the method further includes: the second communication device receiving first indication information, where the first indication information is used to indicate whether the first information is received correctly.

[0070] Based on the above technical solution, the second communication device can also receive the first indication information, so that the second communication device can clarify whether the first communication device correctly receives the first information based on the first indication information. Subsequently, the second communication device can determine whether to retransmit the first information and / or the first data scheduled by the first information based on the first indication information.

[0071] In addition, the first information is used to schedule the first data. Generally, the receiver of the first information and the first data provides feedback on whether the first data is correctly received, and the receiver does not provide feedback on whether the first information is correctly received. In the above technical solution, if the first information can be used to trigger AI processing (such as the first processing and / or the second processing), the first indication information indicates whether the first information is correctly received, so that the receiver of the first indication information can clearly understand whether the first communication device triggers the corresponding AI processing based on the first information.

[0072] In a possible implementation of the second aspect, the first indication information indicates that the first information is correctly received. When the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the first indication information is also used to trigger the first processing; the first indication information indicates that the first information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data, the first indication information is also used to trigger the second processing.

[0073] Based on the above technical solution, when the first indication information is used to indicate the correct receipt of the first information, the first indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing). Thus, by indicating the correct receipt of the first information through the first indication information, the recipient of the first indication information can trigger the corresponding AI processing based on the first indication information.

[0074] In a possible implementation manner of the second aspect, the first indication information is further used to indicate whether to perform processing based on the first data.

[0075] Optionally, when the first data is data that has undergone a first process and the second data is gradient data obtained based on the first data after undergoing a second process and label data, the first data may be data obtained based on the second data. To this end, the first indication information may further indicate whether processing is performed based on the first data. This can be understood as indicating whether the first indication information may further indicate whether the first communication device performs further processing based on the first data after undergoing a second process and label data to obtain the gradient data.

[0076] Optionally, when the second data is data that has undergone first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second data may be data obtained based on the first data. To this end, the first indication information may further indicate whether processing is performed based on the first data. This can be understood as indicating whether the first indication information may further indicate whether the first communication device performs gradient update processing based on the first data (i.e., gradient data).

[0077] Based on the above technical solution, the first indication information is used not only to indicate whether the first information is correctly received, but also to indicate whether processing is performed based on the first data. In this way, the first indication information can be reused to implement more indications, thereby reducing overhead.

[0078] In a possible implementation manner of the second aspect, after the second communication device receives the second information, the method further includes: the second communication device sends second indication information, where the second indication information is used to indicate whether the second information is received correctly.

[0079] Based on the above technical solution, after the second communication device receives the second information, the second communication device may also send second indication information, so that the first communication device can clarify whether the second communication device correctly receives the second information based on the second indication information. Subsequently, the first communication device can determine whether to retransmit the second information and / or the second data scheduled by the second information based on the first indication information.

[0080] In a possible implementation of the second aspect, the second indication information indicates that the second information is correctly received. When the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing and label data of the first data, the second indication information is also used to trigger the second processing; when the second indication information indicates that the second information is correctly received, when the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and label data of the second data, the second indication information is used to trigger the first processing.

[0081] Based on the above technical solution, when the second indication information is used to indicate the correct receipt of the second information, the second indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing). Thus, by indicating the correct receipt of the second information through the second indication information, the recipient of the second indication information can trigger the corresponding AI processing based on the second indication information.

[0082] In a possible implementation manner of the second aspect, the second indication information is further used to indicate whether to perform processing based on the second data.

[0083] Optionally, when the first data is data that has undergone a first process and the second data is gradient data obtained based on the first data after the second process and the label data, the first data may be data obtained based on the second data. To this end, the second indication information may further indicate whether processing is performed based on the second data. This can be understood as indicating whether the first indication information may further indicate whether the second communication device performs gradient update processing based on the second data (i.e., gradient data).

[0084] Optionally, when the second data is data that has undergone the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second data may be data obtained based on the first data. To this end, the second indication information may further indicate whether processing is performed based on the second data. This can be understood as indicating whether the second indication information may further indicate whether the second communication device performs further processing based on the second data after the second processing and label data to obtain the gradient data.

[0085] Based on the above technical solution, the second indication information is used not only to indicate whether the second information is correctly received, but also to indicate whether processing is performed based on the second data. In this way, the second indication information can be reused to implement more indications, thereby reducing overhead.

[0086] In a possible implementation manner of the second aspect, the second information is further used to indicate at least one of the following: a data type of the second data, and whether to send gradient information determined based on the second data.

[0087] Based on the above technical solution, the second information is also used to indicate at least one of the above items, so that the second communication device can obtain other information associated with the second data based on the second information, and assist in subsequent processing of the second data based on the other information.

[0088] In a possible implementation manner of the second aspect, the first information is further used to indicate at least one of the following: a data type of the first data, and whether to send gradient information determined based on the first data.

[0089] Based on the above technical solution, the first information is also used to indicate at least one of the above items, so that the recipient of the first information (i.e., the first communication device) can obtain other information associated with the first data based on the first information, and assist in subsequent processing of the first data based on the other information.

[0090] In a possible implementation of the second aspect, the method further includes: the second communication device receiving capability information of the first communication device, where the capability information of the first communication device is used to determine the configuration information; wherein the AI ​​capability information of the first communication device includes at least one of the following: processing delay information of the first communication device for forward data of the AI ​​network structure to which the first data belongs, processing delay information of the first communication device for reverse data in the AI ​​network structure to which the first data belongs, the batch size of the first data, load information of the processing resources of the first communication device, and computing power resource information of the first communication device.

[0091] Based on the above technical solution, the second communication device can receive the capability information of the first communication device, so that the second communication device determines the configuration information adapted to the capability information based on the capability information, so that the first communication device can receive the first information based on the success rate of the configuration information.

[0092] Optionally, the configuration information includes at least one of the following: a period of the first information, a window length for detecting the first information, and a position of a transmission symbol of the first information in a time slot.

[0093] In a possible implementation of the second aspect, the method further includes: the second communication device sending third information, where the third information is used to indicate at least one of the following: information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs.

[0094] Based on the above technical solution, the second communication device may further send third information for indicating at least one of the above items, so that the first communication device can perform subsequent AI processing based on the third information.

[0095] The third aspect of the present application provides a communication device, which is a first communication device, and includes a transceiver unit and a processing unit; the transceiver unit is used to receive configuration information, the configuration information is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the processing unit is used to receive the first data based on the first information; the transceiver unit is also used to send second information, and the second information is used to schedule the transmission of the second data; the processing unit is also used to send the second data based on the second information; wherein, the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or, the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes AI processing, and / or, the second processing includes AI processing.

[0096] In the third aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

[0097] The fourth aspect of the present application provides a communication device, which is a second communication device, and includes a transceiver unit and a processing unit. The transceiver unit is used to send configuration information, and the configuration information is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the processing unit is used to send the first data based on the first information; the transceiver unit is also used to receive second information, and the second information is used to schedule the transmission of the second data; the processing unit is also used to receive the second data based on the second information; wherein, the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or, the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes AI processing, and / or, the second processing includes AI processing.

[0098] In the fourth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects. For details, please refer to the second aspect and will not be repeated here.

[0099] In a fifth aspect, the present application provides a communication device, comprising at least one processor, wherein the at least one processor 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 described in any possible implementation method of any one of the first to second aspects.

[0100] In a sixth 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 described in any possible implementation of any one of the first to second aspects.

[0101] In a seventh aspect, the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

[0102] In an eighth aspect, the present application provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in any possible implementation of any one of the first to second aspects above.

[0103] In a ninth aspect, the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any one of the first to second aspects above.

[0104] In a tenth aspect, the present application provides a chip system comprising at least one processor for supporting a communication device to implement the method described in any possible implementation of any one of the first to second aspects.

[0105] In one possible design, the chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of a chip or may include a chip and other discrete components. Optionally, the chip system may further include an interface circuit for providing program instructions and / or data to the at least one processor.

[0106] Among them, the technical effects brought about by any design method in the third to tenth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to second aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figures 1a to 1c are schematic diagrams of a communication system provided by this application;

[0108] Figures 1d, 1e, and 2a to 2f are schematic diagrams of the AI ​​processing process involved in this application;

[0109] FIG3 is an interactive schematic diagram of the communication method provided by this application;

[0110] Figures 4a, 5, and 6 are schematic diagrams of the AI ​​processing process provided by this application;

[0111] Figures 4b to 4g are interactive schematic diagrams of the communication method provided by this application;

[0112] Figures 7 to 11 are schematic diagrams of the AI ​​processing process provided by this application. DETAILED DESCRIPTION

[0113] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0114] (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 the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.

[0115] Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN). Terminal devices can be mobile terminal devices, such as mobile phones (also known as "cellular" phones, mobile phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile devices that exchange voice and / or data with the radio access network. Examples include personal communication service (PCS) phones, cordless phones, Session Initiation Protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablet computers, and computers with wireless transceiver capabilities. 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, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

[0116] As an example and not a 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 wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. 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 achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, 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 smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0117] The terminal may 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 smart city, a wireless terminal in smart home, etc.

[0118] In addition, the terminal device may also be a terminal device in a communication system that has evolved after the fifth generation (5G) communication system (e.g., a sixth generation (6G) communication system) or a terminal device in a future public land mobile network (PLMN). For example, a 6G network may further expand the form and functionality of a 5G communication terminal. 6G terminals include, but are not limited to, vehicles, cellular network terminals (with integrated satellite terminal functionality), drones, and Internet of Things (IoT) devices.

[0119] 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.

[0120] (2) Network equipment: It can be a device in a wireless network. For example, the network equipment can be a RAN node (or device) that connects a terminal device to a wireless network, which can also be called a base station. Currently, some examples of RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point AP, etc. In addition, in a network structure, the network equipment can include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0121] Alternatively, a RAN node can be a macro base station, micro base station, indoor base station, relay node, donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. A RAN node can also be a server, wearable device, vehicle, or vehicle-mounted device. For example, the access network device in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

[0122] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a centralized 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 they can be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0123] 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, this application takes CU, CU-CP, CU-UP, DU and RU as examples for description. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0124] The communication between the access network device and the terminal device 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.

[0125] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0126] Table 1

[0127] The network device may be any other device that provides wireless communication functionality to the terminal device. The embodiments of this application do not limit the specific technology and device form used by the network device. For ease of description, the embodiments of this application do not limit this.

[0128] The network equipment may also include core network equipment, which may include, for example, a mobility management entity (MME), a home subscriber server (HSS), a serving gateway (S-GW), a policy and charging rules function (PCRF), and a public data network gateway (PDN gateway, P-GW) in a fourth generation (4G) network; and network elements such as an access and mobility management function (AMF), a user plane function (UPF), or a session management function (SMF) in a 5G network. In addition, the core network equipment may also include other core network equipment in a 5G network and a next generation network of a 5G network.

[0129] 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 can be an AI node on the network side (access network or core network), a computing power node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

[0130] In the embodiments of the present application, the apparatus for implementing the function of the network device may be the network device, or may be a device capable of supporting the network device in implementing the function, such as a chip system, which may be installed in the network device. In the technical solutions provided in the embodiments of the present application, the technical solutions provided in the embodiments of the present application are described by taking the network device as an example.

[0131] (3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used simultaneously. 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 as specified in 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.

[0132] Furthermore, these values ​​and parameters can be changed or updated.

[0133] (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 can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. In addition, 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.

[0134] (5) “Sending” and “receiving” in the embodiments of the present application indicate the direction of signal transmission. For example, “sending information to XX” can be understood as the destination of the information being XX, which can include direct sending through the air interface, as well as indirect sending through the air interface by other units or modules. “Receiving information from YY” can be understood as the source of the information being YY, which can include direct receiving from YY through the air interface, as well as indirect receiving from YY through the air interface from other units or modules. “Sending” can also be understood as the “output” of the chip interface, and “receiving” can also be understood as the “input” of the chip interface.

[0135] 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.

[0136] 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.

[0137] (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 achieved 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 can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

[0138] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments of 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 following description of the embodiments of this application does not constitute a limitation on the scope of protection of this application.

[0139] 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.

[0140] Please refer to Figure 1a, which is a schematic diagram of a communication system in this application. Figure 1a exemplarily illustrates a network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in Figure 1a, terminal device 1 is a smart teacup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas pump, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

[0141] As shown in Figure 1a, the AI ​​configuration information sending entity can be a network device. The AI ​​configuration information receiving entity can be terminal devices 1-6. In this case, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 can send data to the network device, and the network device needs to receive data sent by terminal devices 1-6. At the same time, the network device can send configuration information to terminal devices 1-6.

[0142] For example, in Figure 1a, terminal devices 4 and 6 can also form a communication system. Terminal device 5 serves as a network device, i.e., the AI ​​configuration information sending entity; terminal devices 4 and 6 serve as terminal devices, i.e., the AI ​​configuration information receiving entities. For example, in a connected vehicle system, terminal device 5 sends AI configuration information to terminal devices 4 and 6, respectively, and receives data from them. Correspondingly, terminal devices 4 and 6 receive AI configuration information from terminal device 5 and send data to terminal device 5.

[0143] Taking the communication system shown in Figure 1a as an example, in addition to executing communication-related services, different devices (including between network devices, between network devices and terminal devices, and / or between terminal devices) may also execute AI-related services.

[0144] As shown in Figure 1b, taking the network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

[0145] 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 performed between the TV and the mobile phone.

[0146] The technical solution provided in this 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 this application to implement some or all AI-related operations. The AI ​​network element can also be referred to as an AI node, AI device, AI entity, AI module, AI model, or AI unit, etc. The AI ​​network element can be a network element built into the communication system. For example, the AI ​​network element can be an AI module built into: an access network device, a core network device, a cloud server, or a network management (OAM) to implement AI-related functions. The OAM can 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 can also be an independently set network element in the communication system. Optionally, the terminal or the chip built into the terminal can also include an AI entity to implement AI-related functions.

[0147] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0148] Artificial intelligence (AI) can imbue machines with human intelligence. For example, it can enable machines to simulate certain intelligent human behaviors using computer hardware and software. Machine learning methods can be used to achieve AI. In machine learning, a machine uses training data to learn (or train) a model. This model represents the mapping from input to output. The learned model can be used for inference (or prediction), meaning that the model can be used to predict the output corresponding to a given input. This output can also be called an inference result (or prediction result).

[0149] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0150] Supervised learning uses machine learning algorithms to learn the mapping relationship between sample values ​​and sample labels based on collected sample values ​​and sample labels, and then expresses this learned mapping relationship using an AI model. The process of training a machine learning model is the process of learning this mapping relationship. During training, sample values ​​are input into the model to obtain the model's predicted values. The model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). Once 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 mappings or nonlinear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0151] Unsupervised learning uses algorithms to discover inherent patterns in collected sample values. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping from one sample to another. This is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used in signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0152] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems lack explicit label data for "correct" actions. Instead, the algorithm must interact with the environment to obtain reward signals from the environment, and then adjust its decision-making actions to maximize the reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmit power of each user based on the overall system throughput fed back by the wireless network, hoping to achieve higher system throughput. The goal of reinforcement learning is also to learn the mapping between environmental states and optimal (e.g., optimal) decision-making actions. However, because the labels for "correct actions" cannot be obtained in advance, network optimization cannot be achieved by calculating the error between actions and "correct actions." Reinforcement learning training is achieved through iterative interaction with the environment.

[0153] A neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, NNs can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Traditional communication systems require extensive expert knowledge to design communication modules. However, deep learning communication systems based on neural networks can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods.

[0154] The idea of ​​a neural network is derived from the neuronal structure of the brain. For example, each neuron performs a weighted sum operation on its input values ​​and outputs the result through an activation function.

[0155] 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,…,x n ], and the weights corresponding to each input are w=[w,w1,…,w n ], where n is a positive integer, w i and x i It can be a decimal, an integer (such as 0, a positive integer or a negative integer, etc.), or a complex number. i As x i The weight of x i Weighted. The bias of the weighted sum of the 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), then the output of the neuron is: For another example, if 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.

[0156] Furthermore, neural networks generally include multiple layers, each of which may include one or more neurons. Increasing the depth and / or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it comprises, and the number of neurons in each layer can be referred to as the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, a hidden layer, and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to an intermediate hidden layer. The hidden layer performs calculations on the received processing results to obtain a calculation result, which is then passed to the output layer or the next adjacent hidden layer, which ultimately obtains the output of the neural network. A neural network can include one hidden layer or multiple hidden layers connected in sequence, without limitation.

[0157] A neural network is, for example, a deep neural network (DNN). Depending on how the network is constructed, a DNN can include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0158] Figure 1e is a schematic diagram of a FNN network. A characteristic of FNN networks is that neurons in adjacent layers are fully connected. This characteristic typically requires a large amount of storage space and results in high computational complexity.

[0159] CNN is a neural network specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling along the time axis) and image data (discrete sampling along two dimensions) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

[0160] RNNs are a type of DNN that utilizes feedback time series information. Their input consists of a new input value at the current moment and their own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

[0161] During the machine learning model training process, a loss function can be defined. This function describes the gap or discrepancy between the model's output and the ideal target value. Loss functions can be expressed in various forms, and there are no restrictions on their specific form. The model training process can be viewed as adjusting some or all of the model's parameters to keep the loss function below a threshold or meet the target.

[0162] A model may also be referred to as an AI model, rule, or other name. An AI model can be considered a specific method for implementing an AI function. An AI model represents a mapping relationship or function between the input and output of a 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 inference result release, etc. AI functions may also be referred to as AI (related) operations, or AI-related functions.

[0163] The following is an exemplary description of the implementation process of the neural network with reference to the accompanying drawings.

[0164] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0165] As shown in Figure 2a, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (center). Each layer of the MLP contains several nodes, called neurons. Neurons in adjacent layers are connected to each other.

[0166] Optionally, considering neurons in two adjacent layers, the output h of a neuron in the next layer is the weighted sum of all neurons x connected to it in the previous layer and passes through an activation function, which can be expressed as: h=f(wx+b).

[0167] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0168] Alternatively, the output of the neural network can be recursively expressed as: y = f n (w n f n-1 (…)+b n ).

[0169] Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0170] In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly, and the process of obtaining this mapping from random w and b using existing data is called neural network training.

[0171] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0172] As shown in Figure 2b, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the loss function reaches a minimum, which is the "better point (e.g., optimal point)" in Figure 2b. It is 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.

[0173] Alternatively, the gradient descent process can be expressed as:

[0174] Among them, θ is the parameter to be optimized (including w and b), L is the loss function, and η is the learning rate, which controls the step size of gradient descent. represents the derivative operation, represents the derivative of θ with respect to L.

[0175] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0176] 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:

[0177] Among them, w ij is the weight of node j connecting to node i, s i is the weighted sum of the inputs to node i.

[0178] 2. Federated Learning (FL)

[0179] The concept of federated learning effectively solves the current difficulties faced by the development of artificial intelligence. On the premise of fully protecting user data privacy and security, it efficiently completes the model learning task by promoting the collaboration between various edge devices and central servers.

[0180] As shown in Figure 2d, the FL architecture is the most widely used training architecture in the current FL field. The FedAvg algorithm is the basic algorithm of FL. Its algorithm flow is roughly as follows:

[0181] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0182] (2) In the round t∈[1,T], 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.

[0183] (3) The central node aggregates and collects the 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.

[0184] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0185] 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.

[0186] As you can see, in the FL framework, datasets exist on distributed nodes. Distributed nodes collect local datasets, perform local training, and report the local training results (models or gradients) to the central node. The central node itself does not have a dataset; it is only responsible for fusing the training results of distributed nodes to obtain a global model and send it to the distributed nodes.

[0187] 3. Decentralized learning: Different from federated learning, decentralized learning is another distributed learning architecture.

[0188] As 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 goal f of each node. i The mean of (x), 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 f i (x) Calculate 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:

[0189] 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, N i is the set of neighbor nodes of node i, |N i | represents the number of elements in the neighbor node set 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.

[0190] The technical solutions provided in this application can be applied to wireless communication systems (e.g., the systems shown in Figures 1a and 1b). In wireless communication systems, communication nodes generally have both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., performing signal transmission and reception processing) to enable communication between the network device and other communication nodes.

[0191] In communication networks, communication nodes may have excess computing power beyond supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

[0192] In one possible implementation, a communication node can act as a participating node in an AI learning system, applying its computing power to a specific component of the system. With the advent of the era of large models, deep learning models with massive parameters, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT-2), can accomplish increasingly complex tasks and achieve superior performance. However, for large models, even the inference process is limited by device capacity, so large models are typically stored on central cloud servers. Furthermore, each device in the network generates a massive amount of raw data daily, which requires multiple inference calls on the large model. Typically, a device (such as a communication node) sends data to a central server, which then performs inference using the data and returns the inference results to the device. This process consumes significant communication resources for data transmission and also risks the privacy of device data.

[0193] To better save communication costs and protect user data privacy, scholars have proposed distributed inference technology for deep neural networks. This approach distributes models to devices and uses the local computing power of the devices to infer the models, thereby reducing communication costs and ensuring data privacy.

[0194] For example, in the example shown in FIG2f , two communication nodes, Node 1 and Node 2, are used as an example to participate in the AI ​​learning system. Node 1 and Node 2 can both be communication nodes, such as terminal devices or network devices. The neural network used by the AI ​​learning system can include at least a sub-neural network deployed at Node 1 for AI encoding, and / or a sub-neural network deployed at Node 2 for AI decoding.

[0195] As an implementation example in Figure 2f, node 1 processes the encoded signal using the AI ​​encoding sub-neural network. This encoded signal undergoes quantization and physical layer processing to produce a wireless signal. Correspondingly, node 2 receives this signal via a wireless channel. Node 2 then processes the signal at the physical layer and dequantizes it, using it as input for AI decoding. This AI decoding process yields a decoded signal. Node 2 can also determine gradient data based on this decoded signal and the label data.

[0196] After that, after node 2 obtains gradient data based on the sub-neural network processing of AI decoding, the gradient data is quantized and processed at the physical layer to obtain a wireless signal; correspondingly, after node 1 receives the wireless signal through transmission through the wireless channel, node 1 obtains gradient data after physical layer processing and dequantization processing. Subsequently, node 1 can optimize the sub-neural network for AI encoding deployed in node 1 based on the gradient data (such as training / updating / iteration, etc.).

[0197] Optionally, after node 2 obtains the gradient data, node 2 can also optimize the neural network (such as training / updating / iteration, etc.) of the sub-neural network for AI encoding deployed in node 2 based on the gradient data.

[0198] It should be noted that the node 2 can also calculate the result of the loss function based on the decoding result and the label data, and the result of the loss function can also be used to optimize the neural network. The above implementation is only explained by taking the example of node 2 determining the gradient data.

[0199] However, in the above implementation process, the processing of the neural network (for example, the processing process of the sub-neural network for AI encoding deployed in node 1, the sub-neural network for AI decoding deployed in node 2, etc.) and communication (for example, physical layer processing) are two independent operations, which are completed at different protocol layers, requiring more steps and higher latency.

[0200] To address the above issues, this application provides a communication method and related equipment for enabling the computing power of communication nodes to be applied to artificial intelligence (AI) processing of neural networks while also improving the flexibility of neural network deployment. This will be described in detail below with reference to the accompanying drawings.

[0201] Please refer to FIG3 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0202] It should be noted that, in Figure 3, the method is illustrated by taking the first communication device and the second communication device as the execution subjects of the interaction diagram as an example, but the present application does not limit the execution subjects of the interaction diagram. For example, in Figure 3 and Figure 6 below, the execution subject of the method can be replaced by a chip, a chip system, a processor, a logic module or software in the communication device. The first communication device can be a terminal device and the second communication device can be a network device, or the first communication device and the second communication device are both terminal devices (for example, the method can be applied to the communication process of different terminal devices in a sidelink communication scenario).

[0203] S301. The second communication device sends configuration information, and the first communication device receives the configuration information accordingly, wherein the configuration information is used to configure transmission resources for first information, and the first information is used to schedule transmission of first data.

[0204] It should be understood that after the second communication device sends the configuration information for configuring the transmission resources of the first information in step S301, the second communication device can send the first information based on the configuration information, and accordingly, the first communication device can receive the first information based on the configuration information (for example, the implementation process of step A in Figure 3).

[0205] S302. The second communication device sends first data, and correspondingly, the first communication device receives the first data.

[0206] S303: The first communication device sends second information, and correspondingly, the first communication device receives the second information, wherein the second information is used to schedule transmission of the second data.

[0207] S304. The first communication device sends the second data, and correspondingly, the first communication device receives the second data.

[0208] In this application, terms such as AI, neural network, AI neural network, machine learning, AI processing, and AI neural network processing can be used interchangeably.

[0209] In this application, the data involved (such as first data, second data, etc.) can be replaced by information, signals, etc.

[0210] It should be understood that in a wireless communication system, the first information used to schedule the first data and / or the second information used to schedule the second data can be messages / signaling / information of the radio resource control (RRC) layer, the medium access control (MAC) layer, the physical (PHY) layer or other protocol layers.

[0211] In addition, compared to the implementation method in which the communication device processes the received application layer data through the physical layer and then dequantizes the physical layer processing results to obtain the application layer scheduling signaling (the scheduling signaling is used to schedule the transmission of data associated with AI processing), when the first information used to schedule the first data and / or the second information used to schedule the second data is physical layer signaling, the transmission of data associated with AI processing can be quickly scheduled through physical layer signaling, thereby reducing the processing delay.

[0212] For example, the first information for scheduling the first data comes from a second communication device. The second communication device may be a network device, and accordingly, the first information may be downlink control information (DCI) sent by the network device to the terminal device, and the second information may be uplink control information (UCI) sent by the terminal device to the network device. Alternatively, the second communication device may be a terminal device different from the first communication device, and accordingly, the first information and the second information may be sidelink control information (SCI) exchanged between different terminal devices.

[0213] It should be noted that the first data can be data that has undergone the first processing, and the second data can be gradient data derived from the first data after the second processing and the label data, or the second data can be data that has undergone the first processing, and the first data can be gradient data derived from the second data after the second processing and the label data. In other words, the first data can be derived based on the second data, or the second data can be derived based on the first data. The order of executing steps S302 and S304 can be varied, and some implementation examples will be used below to illustrate this.

[0214] In implementation method 1, step S302 is performed first and step S304 is performed later. In this case, the second data may be data obtained based on the first data. For example, if the first data is data that has undergone a first process and the second data is gradient data obtained based on the first data that has undergone a second process and label data, the first data is data sent by the sender of the first data (e.g., a second communication device) after the first process, and the second data is data obtained by the first communication device after performing the second process on the received first data.

[0215] In other words, in implementation 1, the first communication device performs the first processing to obtain the first data, and the second communication device performs the second processing to obtain the second data. In this case, the first data can be referred to as forward data, and the second data can be referred to as reverse data (e.g., reverse gradient, loss function result, etc.).

[0216] It is understood that in implementation method 1, step S302 is executed first and step S304 is executed later, while the first information for scheduling the first data is executed before step S302, and the second information for scheduling the second data (i.e., step S303) is executed before step S304. In addition, the order of executing the first data transmission and reception process in step S302 and the second information transmission and reception process in step S303 is not limited. For example, step S302 may be executed first and step S303 may be executed later; for another example, step S303 may be executed first and step S302 may be executed later.

[0217] In a second implementation, step S304 is performed first and step S302 is performed later. In this case, the first data may be data obtained based on the second data. For example, if the second data is data that has undergone a first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second data is data sent by the first communication device after the first processing, and the first data is data obtained by the sender of the first data (e.g., the second communication device) performing the second processing based on the received second data.

[0218] In other words, in implementation mode 2, the second communication device performs the first processing to obtain the first data, and the first communication device performs the second processing to obtain the second data. In this case, the second data can be referred to as forward data, and the first data can be referred to as reverse data (e.g., reverse gradient, loss function result, etc.).

[0219] It is understood that in implementation method 2, step S304 is executed first and step S302 is executed later, while the first information for scheduling the first data is executed before step S302, and the second information for scheduling the second data (i.e., step S303) is executed before step S304. In addition, the order of executing the second data transmission and reception process and the first information transmission and reception process in step S304 is not limited. For example, step S304 may be executed first and the first information transmission and reception process is executed; for another example, the first information transmission and reception process is executed first and step S304 is executed later.

[0220] Optionally, in the above-described implementations 1 and 2, the order of executing the second information transmission and reception process and the first information transmission and reception process in step S304 is not limited. For example, step S304 may be executed first and the first information transmission and reception process may be executed later; or, for another example, the first information transmission and reception process may be executed first and step S304 may be executed later.

[0221] It should be understood that in the above-mentioned implementation method 1 or implementation method 2, the first processing performed by the first communication device or the second communication device may include AI processing, and / or the second processing performed by the first communication device or the second communication device may include AI processing. Exemplarily, if the first processing includes AI processing, the AI ​​processing in the first processing may be referred to as encoding neural network processing, AI encoder processing, AI encoding neural network processing, etc. Correspondingly, if the second processing includes AI processing, the AI ​​processing in the second processing may be referred to as decoding neural network processing, AI decoder processing, AI decoding neural network processing, etc.

[0222] As can be seen from the implementation processes of Implementation Methods 1 and 2 above, after the first communication device receives the configuration information in step S301, an association relationship may exist between the first information received by the first communication device based on the configuration information and the second information sent by the first communication device in step S303. This association relationship can be implemented in various ways, which will be exemplified below using Implementation Methods A and B.

[0223] Implementation A: The time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

[0224] Specifically, in the configuration information received by the first communication device in step S301, the transmission resources of the first information include the time domain resources carrying the first information; wherein the time interval between the time domain resources carrying the first information and the time domain resources carrying the second information is preconfigured.

[0225] In this way, when the first information transmission and reception process is performed first and the second information transmission and reception process is performed later, after the first communication device determines the time domain resource carrying the first information based on the configuration information in step S301, the first communication device can receive the first information based on the time domain resource carrying the first information, and the first communication device can determine the time domain resource carrying the second information based on the preconfigured time interval and the time domain resource carrying the first information. Thereafter, after the first communication device receives the first information, the first communication device can transmit the second information on the time domain resource carrying the second information. Furthermore, the second communication device can also receive the second information based on the preconfigured time interval, thereby reducing the resource configuration overhead of the second information.

[0226] Similarly, if the second information transmission and reception process is performed first and the first information transmission and reception process is performed later, after the first communication device determines the time domain resource for the first information based on the configuration information in step S301, the first communication device can determine the time domain resource for carrying the second information before the time domain resource for carrying the first information based on a preconfigured time interval. Thereafter, the first communication device can transmit the second information on the time domain resource for carrying the second information, and then receive the first information on the time domain resource for carrying the first information. Furthermore, the second communication device can also receive the second information and transmit the first information based on the preconfigured time interval.

[0227] Optionally, implementation method A can be understood as a real-time data alignment method, where real-time can be understood as a process in which the first communication device receives the first information, and a process in which the first communication device sends the second information, and the time interval between the process in which the first communication device performs processing (for example, the first processing or the second processing) to obtain the second data, and the time interval between the process in which the second communication device performs processing (for example, the first processing or the second processing) to obtain the first data, is relatively fixed.

[0228] Figure 4a illustrates an example implementation of Implementation A (i.e., real-time data alignment). In this example, the first of every six frames (e.g., frames numbered 1, 7, or 13) is used to transmit the first information sent by the second communication device, and the fourth of every six frames (e.g., frames numbered 4, 10, or 16) is used to transmit the second information sent by the first communication device. In other words, the time interval between the time domain resources carrying the first information and the time domain resources carrying the second information can be preconfigured.

[0229] It can be understood that in Figure 4a, in addition to exchanging configuration information, first information, first data, second information and second data, the first communication device and the second communication device can also exchange other data, such as other communication signals shown in Figure 4a, such as system information, reference signals, channel information obtained by measuring based on reference signals, etc.

[0230] In one possible implementation of Implementation A, the first communication device may send (or receive from) indication information to the second communication device, where the indication information indicates the time interval between the time domain resource carrying the first information and the time domain resource carrying the second information. In this manner, the first communication device and the second communication device can align their understanding of the time interval to avoid transmission errors.

[0231] Optionally, when receiving indication information from the second communication device (the indication information is used to indicate the time interval), the indication information may be carried in the configuration information in step S301.

[0232] Implementation B: Any one of the first information and the second information and any one of the first processing and the second processing may trigger each other.

[0233] As an implementation example 1 of implementation manner B, when the first data is data after first processing and the second data is gradient data obtained based on the data after second processing of the first data and label data (i.e., in the case of the above implementation manner 1), the second processing is triggered based on the first information and the first processing is triggered based on the second information, or the first information is triggered based on the first processing and the second information is triggered based on the second processing;

[0234] As shown in FIG4b , in the implementation example 1 of the implementation example B, when the second processing is triggered based on the first information and the first processing is triggered based on the second information, the process of sending and receiving the second information (i.e., step S303) is executed first and the process of sending and receiving the first data obtained through the first processing (i.e., step S302) is executed later, and the process of sending and receiving the first information is executed first and the process of sending and receiving the second data obtained through the second processing (i.e., step S304) is executed later.

[0235] In the implementation process shown in FIG4c , in implementation example 1 of implementation example B, when the first information is triggered based on the first process and the second information is triggered based on the second process, the second communication device triggers the execution of the first information transmission and reception process based on the first data obtained by the first process, and then transmits the first data (i.e., step S302). Furthermore, when the first communication device triggers the execution of the second information transmission and reception process based on the second process (i.e., step S303), it then transmits the second data (i.e., step S304).

[0236] As a second implementation example of implementation method B, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data (that is, in the case of the above-mentioned implementation method two), the second processing is triggered based on the second information and the first processing is triggered based on the first information, or, the first information is triggered based on the second processing and the second information is triggered based on the first processing.

[0237] In the implementation process shown in FIG4d , in implementation example 2 of implementation example B, when the second process is triggered based on the second information and the first process is triggered based on the first information, after receiving the first information, the first communication device triggers execution of the first process to obtain the second data and executes step S304. Correspondingly, after receiving the second information in step S303, the second communication device triggers execution of the second process to obtain the first data and executes step S302.

[0238] As shown in FIG4e , in the implementation example 2 of the implementation example B, when the second information is triggered based on the first processing and the first information is triggered based on the second processing, the first communication device triggers the process of sending the second information in step S303 in the process of obtaining the second data based on the first processing; in addition, the second communication device triggers the process of sending the first information in the process of obtaining the first data based on the second processing.

[0239] In the above-mentioned implementation examples 1 and 2, since the first processing includes AI processing and / or the second processing includes AI processing, the above implementations can trigger the scheduling of AI data through AI processing, or can trigger AI processing through the scheduling of AI data. This allows the AI ​​processing and the scheduling of AI data to trigger each other, thereby reducing the interaction of triggering instructions for AI processing or triggering instructions for scheduling AI data, thereby reducing processing latency and overhead.

[0240] As implementation example three of implementation method B, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data (that is, in the case of the above-mentioned implementation method one), the second processing is triggered based on the first data.

[0241] As shown in FIG4f , in the implementation example three of the implementation example B, when the second processing is triggered based on the first data, after the first communication device receives the first data in step S302, the first communication device triggers execution based on the second processing to obtain the second data.

[0242] Optionally, in implementation example three, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data, that is, after the first communication device confirms receipt of the first information and the first data, the first communication device triggers execution based on the second processing to obtain the second data.

[0243] As implementation example four of implementation method B, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data (that is, in the case of the above-mentioned implementation method two), the second processing is triggered based on the second data.

[0244] As shown in FIG4g , in the implementation example 4 of the implementation example B, when the second processing is triggered based on the second data, after the second communication device receives the second data in step S304, the second communication device triggers the execution of the first data obtained based on the second processing.

[0245] Optionally, in implementation example four, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data, that is, after the second communication device confirms receipt of the second information and the second data, the second communication device triggers execution based on the first processing to obtain the first data.

[0246] In the above implementation examples 3 and 4, since the first processing includes AI processing and / or the second processing includes AI processing, the above implementations can trigger the AI ​​processing through the transmission of AI data. This allows the AI ​​processing and the transmission of AI data to trigger each other, thereby reducing the interaction of triggering instructions for the AI ​​processing, thereby reducing processing latency and overhead.

[0247] Figure 5 is an example of a scenario for implementation method B, in which the implementation scenarios of implementation examples 1 and 3 are used as examples. That is, the first data is data after the first processing, and the second data is gradient data obtained based on the data after the second processing of the first data and the label data. In addition, in this example, the first communication device is a terminal device for performing the second processing and the second communication device is a network device for performing the first processing, that is, the first information is DCI and the second information is UCI.

[0248] It can be seen from the implementation process of Figure 5 that in the above implementation example one, after the network device sends DCI through the downlink transmission link, the terminal device can receive the DCI through the downlink reception link, and the terminal device can trigger the second processing based on the DCI; similarly, after the terminal device sends UCI through the uplink transmission link, the network device can receive the UCI through the uplink reception link, and the network device can trigger the first processing based on the UCI. In the above implementation example three, after the network device sends first data through the downlink transmission link, the terminal device can receive the first data through the downlink reception link, and the terminal device can trigger the second processing based on the first data; similarly, after the terminal device sends second data through the uplink transmission link, the network device can receive the second data through the uplink reception link, and the network device can trigger the first processing based on the second data.

[0249] Alternatively, implementation B can be understood as a data synchronization method in a non-real-time system. Non-real-time herein can be understood as meaning that the time interval between the first communication device receiving the first information and the first communication device sending the second information is not relatively fixed, and / or the time interval between the first communication device performing a process (e.g., the first process or the second process) to obtain the second data and the second communication device performing a process (e.g., the first process or the second process) to obtain the first data is not relatively fixed.

[0250] Figure 6 is an example implementation of Implementation B (i.e., non-real-time data alignment). In this example, multiple AI tasks can be executed between the first communication device and the second communication device, and the execution cycles of different AI tasks or the triggering of data transmission and reception of different AI tasks may be different. For example, the size of the first data of different AI tasks may be different. For another example, the size of the second data of different AI tasks may be different.

[0251] In the example shown in Figure 6, the scheduling information involved in one AI task may include the first information transmitted on the time resource with a frame number of 1 and the second information transmitted on the time resource with a frame number of 4, that is, the interval between the two is 2 frames (that is, frames with frame numbers 2 and 3); the data involved in another AI task may be the first information transmitted on the time resource with a frame number of 5 and the second information transmitted on the time resource with a frame number of 10, that is, the interval between the two is 4 frames (that is, frames with frame numbers 6, 7, 8 and 9); the data involved in another AI task may include the first information transmitted on the time resource with a frame number of 17 and the second information transmitted on the time resource with a frame number of 18, that is, the interval between the two is 0 frame (that is, the two are two adjacent frames).

[0252] In one possible implementation of Implementation B, the configuration information received by the first communications device in step S301 includes a first configuration and a second configuration, wherein the first configuration is used to configure a search space for the first information, and the second configuration is used to configure a retransmission timer for the second information; wherein the duration of a period corresponding to the search space is less than or equal to the duration of the retransmission timer. In this manner, the first communications device can receive the first information and retransmit the second information based on the configuration information, thereby improving the transmission success rate of the first and second information.

[0253] Furthermore, since the first information can be mutually triggered with the AI ​​processing, that is, the first information may be executed irregularly. Therefore, by implementing a method in which the duration of the period corresponding to the search space is less than or equal to the duration of the retransmission timer, the first communication device can detect the first information over a shorter duration, so that the first communication device can promptly receive the AI-processed data or trigger the AI ​​processing. Furthermore, a longer timer can reduce the overhead of the first communication device retransmitting the second information.

[0254] Implementation C: The first information and the first data may trigger each other, and / or the second information and the second data may trigger each other.

[0255] As an example of implementation of Implementation C, the process of the second communication device sending the first information can be used to trigger the generation or transmission of the first data. Similarly, the process of the first communication device sending the second information can be used to trigger the generation or transmission of the second data.

[0256] As another implementation example of implementation manner C, the process of the second communication device generating or sending the first data can trigger the process of the second communication device sending the first information. Similarly, the process of the first communication device generating or sending the second data can trigger the process of the first communication device sending the second information.

[0257] It should be noted that the triggering process in implementation method C can refer to the description of implementation method B above (such as the implementation examples in Figures 4b to 4g).

[0258] In a possible implementation, after the first communication device receives the first information based on the configuration information in step S301, the method further includes: the first communication device sending first indication information, where the first indication information is used to indicate whether the first information is correctly received.

[0259] In this application, the expression "whether correctly received" can be replaced with other terms, including but not limited to: whether erroneously received, whether correctly parsed, whether erroneously parsed, etc. Specifically, the first communication device may further send first indication information, so that the second communication device can ascertain, based on the first indication information, whether the first communication device correctly received the first information. Subsequently, the second communication device may determine, based on the first indication information, whether to retransmit the first information and / or the first data scheduled by the first information.

[0260] In addition, the first information is used to schedule the first data. Generally, the receiver of the first information and the first data provides feedback on whether the first data is correctly received, and the receiver does not provide feedback on whether the first information is correctly received. In the above technical solution, if the first information can be used to trigger AI processing (such as the first processing and / or the second processing), the first indication information indicates whether the first information is correctly received, so that the receiver of the first indication information can clearly understand whether the first communication device triggers the corresponding AI processing based on the first information.

[0261] Optionally, it can be seen from the above implementation process that there may be an association relationship between the first information received by the first communication device based on the configuration information and the second information sent by the first communication device in step S303. In addition to the above implementation methods A and B, there may be other implementation methods for this association relationship.

[0262] For example, if the first indication information is used to indicate the correct receipt of the first information, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data (that is, in the case of the above-mentioned implementation method one), the first indication information is also used to trigger the first processing.

[0263] For another example, if the first indication information is used to indicate correct receipt of the first information, and the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data (i.e., in the case of the above-mentioned implementation method 2), the first indication information is also used to trigger the second processing. Specifically, when the first indication information is used to indicate correct receipt of the first information, the first indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing).

[0264] It should be noted that the implementation process of triggering the first processing or the second processing through the first indication information can refer to the process of triggering the first processing or the second processing through the first information or the second information in the previous text (for example, the implementation process shown in Figures 4b to 4d).

[0265] Therefore, by indicating the correct reception of the first information through the first indication information, the recipient of the first indication information can trigger corresponding AI processing based on the first indication information.

[0266] In a possible implementation, the first indication information is further used to indicate whether to perform processing based on the first data.

[0267] Optionally, when the first data is data that has undergone a first process and the second data is gradient data obtained based on the first data after undergoing a second process and label data (i.e., in the case of the above-described implementation method one), the first data may be data obtained based on the second data. To this end, the first indication information may further indicate whether processing is performed based on the first data. This can be understood as indicating whether the first indication information may further indicate whether the first communication device performs further processing based on the first data after undergoing a second process and label data to obtain gradient data.

[0268] Optionally, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data (that is, in the case of the above-mentioned implementation method two), the second data can be data obtained based on the first data. To this end, the first indication information can also indicate whether to process based on the first data. It can be understood that the first indication information can also indicate whether the first communication device performs gradient update processing based on the first data (that is, gradient data). Specifically, in addition to indicating whether the first information is correctly received, the first indication information is also used to indicate whether to process based on the first data. In this way, the first indication information can be reused to implement more indications to reduce overhead.

[0269] In one possible implementation, after the first communication device sends the second information in step S303, the method further includes: the first communication device receiving second indication information, where the second indication information is used to indicate whether the second information was correctly received. Specifically, after the first communication device sends the second information, the first communication device may also receive the second indication information, so that the first communication device determines whether the second communication device correctly received the second information based on the second indication information. Subsequently, the first communication device may determine whether to retransmit the second information and / or the second data scheduled by the second information based on the first indication information.

[0270] Optionally, it can be seen from the above implementation process that there may be an association relationship between the first information received by the first communication device based on the configuration information and the second information sent by the first communication device in step S303. In addition to the above implementation methods A and B, there may be other implementation methods for this association relationship.

[0271] For example, if the second indication information is used to indicate the correct receipt of the second information, when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data (that is, in the case of the above-mentioned implementation method one), the second indication information is also used to trigger the second processing.

[0272] For another example, if the second indication information is used to indicate that the second information is correctly received, when the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and the label data (that is, in the case of the above-mentioned implementation method two), the second indication information is used to trigger the first processing.

[0273] It should be noted that the implementation process of triggering the first processing or the second processing through the second indication information can refer to the process of triggering the first processing or the second processing through the first information or the second information mentioned above (for example, the implementation process shown in Figures 4b to 4d).

[0274] Thus, when the second indication information is used to indicate the correct receipt of the second information, the second indication information can also be used to trigger AI processing (e.g., the first processing and / or the second processing). Thus, by indicating the correct receipt of the second information through the second indication information, the recipient of the second indication information can trigger corresponding AI processing based on the second indication information.

[0275] In a possible implementation, if the first communication device receives the second indication information, the second indication information is used not only to indicate whether the second information is correctly received, but also to indicate whether to perform processing based on the second data.

[0276] In one implementation example, when the first data is data that has undergone a first process, and the second data is gradient data derived from the first data after the second process and label data (i.e., in the aforementioned implementation method one), the first data may be data derived based on the second data. To this end, the second indication information may further indicate whether processing is to be performed based on the second data. This can be understood as indicating whether the first indication information may further indicate whether the second communication device performs gradient update processing based on the second data (i.e., gradient data).

[0277] In another implementation example, when the second data is data that has undergone first processing and the first data is gradient data obtained based on the second data after the second processing and label data (i.e., in the second implementation method described above), the second data may be data obtained based on the first data. To this end, the second indication information may further indicate whether processing is performed based on the second data. This can be understood as indicating whether the second indication information may further indicate to the second communication device whether to further process the second data based on the second data after the second processing and label data to obtain gradient data.

[0278] Thus, in addition to indicating whether the second information is correctly received, the second indication information is also used to indicate whether processing is performed based on the second data. In this way, the second indication information can be reused to implement more indications, thereby reducing overhead.

[0279] In one possible implementation, the second information sent by the first communication device in step S303 further indicates at least one of the following: the data type of the second data, and whether to send gradient information determined based on the second data. Specifically, the second information further indicates at least one of the above items, enabling the recipient of the second information (i.e., the second communication device) to obtain other information associated with the second data based on the second information, and to assist in subsequent processing of the second data based on the other information.

[0280] In one possible implementation, the first information configured in the configuration information received by the first communication device in step S301 further indicates at least one of the following: the data type of the first data and whether to transmit gradient information determined based on the first data. Specifically, the first information further indicates at least one of the above items, enabling the recipient of the first information (i.e., the first communication device) to obtain other information associated with the first data based on the first information, and to assist in subsequent processing of the first data based on the other information.

[0281] In one possible implementation, after the first communication device receives the first information based on the configuration information in step S301, the method further includes: if a parsing error occurs in the first information, the first communication device determines not to receive the first data. Optionally, after the first communication device receives the first information, the method further includes: if a parsing error occurs in the first information, the first communication device does not expect to receive the first data. Specifically, if a parsing error occurs in the first information, the first communication device may determine not to receive the first data to avoid receiving erroneous data.

[0282] In one possible implementation, before step S301, the method further includes: the first communication device sending capability information of the first communication device, the capability information of the first communication device being used to determine the configuration information; wherein the AI ​​capability information of the first communication device includes at least one of the following: processing latency information of the first communication device for forward data in the AI ​​network structure to which the first data belongs, processing latency information of the first communication device for reverse data in the AI ​​network structure to which the first data belongs, batch size of the first data, load information of the processing resources of the first communication device, and computing resource information of the first communication device. Specifically, the first communication device may send the capability information of the first communication device, so that the second communication device determines, based on the capability information, configuration information compatible with the capability information, so as to increase the success rate of the first communication device receiving the first information based on the configuration information.

[0283] Optionally, the configuration information includes at least one of the following: a period of the first information, a window duration for detecting the first information, and a position of a transmission symbol of the first information in a time slot. Exemplarily, the configuration information may include a first configuration, the at least one item may be included in the first configuration, and the first configuration is used to configure a search space for the first information.

[0284] As an implementation example, let's assume that the first communication device is a terminal device and the second communication device is a network device. That is, the terminal device may receive configuration information in step S301, and the terminal device may send capability information before step S301, and the capability information may be used to determine the configuration information. The network device may receive capability information of one or more terminal devices and send configuration information to each of the one or more terminal devices.

[0285] Exemplarily, the network device may save the mapping relationship between the terminal device capability and the resource of the first information configured by the configuration information, as shown in Table 2, taking the resource of the first information configured by the configuration information as the search space of DCI as an example.

[0286] Table 2

[0287] The capability index can correspond to different capabilities of the terminal device. For example, different capability indexes can represent the device's computing power level, computational latency, etc. The task identifier can be a task index, which can correspond to different tasks, different neural network structures, or different model complexities. In addition, searchSpaceId x (in the example shown in Table 2, x ranges from 0 to 7) indicates the specific searchSpace configuration. An example searchSpace configuration is shown in Table 3 below.

[0288] Table 3

[0289] It should be understood that in Table 3, the searchSpace configuration may include one or more fields in Table 3. In Table 3, the definition of some information elements is as follows:

[0290] The "monitoringSlotPeriodicityAndOffset" information element indicates the monitoring period (ie, the period of the first information), sl160 indicates 160 slots, and the value indicates the offset within the 160 slots.

[0291] The "Duration" information element indicates the duration of the monitoring (ie, the window duration for detecting the first information).

[0292] The "monitoringSymbolsWithinSlot" information element indicates the symbol number within the monitoring slot where the monitoring starts (ie, the position of the transmission symbol of the first information in the time slot).

[0293] Optionally, for the same terminal device, the network device may configure different searchSpace configurations according to different training tasks / neural network structures, that is, the configuration information received by the terminal device in step S301 may include different searchSpace configurations, and the different searchSpace configurations correspond to different training tasks, or the searchSpace configurations correspond to different neural network structures.

[0294] In one possible implementation, the method shown in FIG3 may further include: the first communication device receiving third information, where the third information is used to indicate at least one of the following: information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information for the AI ​​task to which the first data belongs. Specifically, the first communication device may also receive third information indicating at least one of the above items, so that the first communication device can perform subsequent AI processing based on the third information (for example, when the second processing in the above implementation method one includes AI processing, or when the first processing in the above implementation method two includes AI processing).

[0295] Based on Figure 3 and related technical solutions, the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and the label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and the label data. In addition, the first processing includes AI processing, and / or the second processing includes AI processing. In other words, the second data is gradient data corresponding to data obtained by performing AI processing on the first data, or the first data is gradient data corresponding to data obtained by performing AI processing on the second data. Thus, when the communication device in the communication system serves as an AI participating node, the computing power of the communication device can be applied to AI processing while also improving the flexibility of AI deployment.

[0296] In addition, the configuration information received by the first communication device in step S301 is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data. Accordingly, the first communication device can receive the first data based on the first information in step S302; and after the first communication device sends the second information for scheduling the transmission of the second data in step S303, the first communication device can send the second data based on the second information in step S304. In other words, when the first data and / or the second data are data associated with AI processing, the first communication device can realize the transmission of the data associated with AI processing based on the scheduling of the first information and the second information. Thus, by scheduling the data associated with AI processing through the first information and the second information, the transmission success rate of the data associated with AI processing can be improved.

[0297] Referring to Figure 7, an embodiment of the present application provides a communication device 700. This communication device 700 can implement the functions of the second communication device or the first communication device in the above-mentioned method embodiment, thereby also achieving the beneficial effects of the above-mentioned method embodiment. In this embodiment of the present application, the communication device 700 can be the first communication device (or the second communication device), or it can be an integrated circuit or component, such as a chip, within the first communication device (or the second communication device).

[0298] It should be noted that the transceiver unit 702 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

[0299] In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive configuration information, the configuration information is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the processing unit 701 is used to receive the first data based on the first information; the transceiver unit 702 is also used to send second information, and the second information is used to schedule the transmission of the second data; the processing unit 701 is also used to send the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or, the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes AI processing, and / or, the second processing includes AI processing.

[0300] In one possible implementation, when the device 700 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to send configuration information, the configuration information is used to configure the transmission resources of the first information, and the first information is used to schedule the transmission of the first data; the processing unit 701 is used to send the first data based on the first information; the transceiver unit 702 is also used to receive second information, and the second information is used to schedule the transmission of the second data; the processing unit 701 is also used to receive the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0301] It should be noted that, for details on the information execution process of the units of the above-mentioned communication device 700, please refer to the description in the method embodiment shown above in this application, and no further details will be given here.

[0302] Please refer to Fig. 8, which is another schematic structural diagram of a communication device 800 provided in this application. The communication device 800 includes a logic circuit 801 and an input / output interface 802. The communication device 800 may be a chip or an integrated circuit.

[0303] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the input / output interface 802 in FIG8 , 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.

[0304] Optionally, the input-output interface 802 is used to receive configuration information, which is used to configure transmission resources for the first information, and the first information is used to schedule the transmission of the first data; the logic circuit 801 is used to receive the first data based on the first information; the input-output interface 802 is also used to send second information, and the second information is used to schedule the transmission of the second data; the logic circuit 801 is also used to send the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing and the label data of the first data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and the label data of the second data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0305] Optionally, the input-output interface 802 is used to send configuration information, which is used to configure transmission resources for the first information, and the first information is used to schedule the transmission of the first data; the logic circuit 801 is used to send the first data based on the first information; the input-output interface 802 is also used to receive second information, and the second information is used to schedule the transmission of the second data; the logic circuit 801 is also used to receive the second data based on the second information; wherein the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes AI processing, and / or the second processing includes AI processing.

[0306] The logic circuit 801 and the input / output interface 802 may also execute other steps executed by the first communication device or the second communication device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

[0307] In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

[0308] Optionally, the logic circuit 801 may be a processing device, and the functions of the processing device may be partially or entirely implemented by software. The functions of the processing device may be partially or entirely implemented by software.

[0309] 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.

[0310] Alternatively, the processing device may include only a processor. A memory for storing the computer program is located outside the processing device, and the processor is connected to the memory via circuits / wires to read and execute the computer program stored in the memory. The memory and processor may be integrated or physically separate.

[0311] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processor units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors.

[0312] Please refer to Figure 9, which shows a communication device 900 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 900 can specifically be a communication device serving as a terminal device in the above-mentioned embodiments. The example shown in Figure 9 is that the terminal device is implemented through the terminal device (or a component in the terminal device).

[0313] Herein, a possible logical structure diagram of the communication device 900 is shown. The communication device 900 may include but is not limited to at least one processor 901 and a communication port 902 .

[0314] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the communication port 902 in FIG9 , which may include an input interface and an output interface. Alternatively, the communication port 902 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0315] Further optionally, the device may also include at least one of a memory 903 and a bus 904. In an embodiment of the present application, the at least one processor 901 is used to control and process the actions of the communication device 900.

[0316] In addition, the processor 901 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 the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0317] It should be noted that the communication device 900 shown in Figure 9 can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment and achieve the corresponding technical effects of the terminal device. The specific implementation methods of the communication device shown in Figure 9 can refer to the description in the aforementioned method embodiment and will not be repeated here.

[0318] Please refer to Figure 10, which is a structural diagram of the communication device 1000 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 1000 can specifically be a communication device as a network device in the above-mentioned embodiments. The example shown in Figure 10 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 10.

[0319] The communication device 1000 includes at least one processor 1011 and at least one network interface 1014. Further optionally, the communication device also includes at least one memory 1012, at least one transceiver 1013 and one or more antennas 1015. The processor 1011, the memory 1012, the transceiver 1013 and the network interface 1014 are connected, for example, via 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 1015 is connected to the transceiver 1013. The network interface 1014 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1014 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.

[0320] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the network interface 1014 in FIG10 , which may include an input interface and an output interface. Alternatively, the network interface 1014 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0321] Processor 1011 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process software program data, for example, to support the communication device in performing the actions described in the embodiments. The communication device may include a baseband processor and a central processing unit. The baseband processor is primarily used to process communication protocols and communication data, while the central processing unit is primarily used to control the entire terminal device, execute software programs, and process software program data. Processor 1011 in Figure 10 may integrate the functions of both a baseband processor and a central processing unit. Those skilled in the art will appreciate that the baseband processor and the central processing unit may also be independent processors interconnected via a bus or other technology. Those skilled in the art will appreciate that a terminal device may include multiple baseband processors to accommodate different network standards, multiple central processing units to enhance its processing capabilities, and various components of the terminal device may be connected via various buses. The baseband processor may also be referred to as a baseband processing circuit or a baseband processing chip. The central processing unit may also be referred to as a central processing circuit or a central processing chip. The functionality for processing communication protocols and communication data may be built into the processor or stored in memory as a software program, which is executed by the processor to implement the baseband processing functionality.

[0322] The memory is primarily used to store software programs and data. Memory 1012 can exist independently and be connected to processor 1011. Alternatively, memory 1012 and processor 1011 can be integrated, for example, within a single chip. Memory 1012 can store program code for implementing the technical solutions of the embodiments of this application, and its execution is controlled by processor 1011. The various computer program codes executed can also be considered drivers for processor 1011.

[0323] Figure 10 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. 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 present embodiment.

[0324] The transceiver 1013 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal. The transceiver 1013 can be connected to the antenna 1015. The transceiver 1013 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1015 can receive radio frequency signals. The receiver Rx of the transceiver 1013 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 digital intermediate frequency signal to the processor 1011 so that the processor 1011 can further process the digital baseband signal or digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1013 is also used to receive a modulated digital baseband signal or digital intermediate frequency signal from the processor 1011, convert the modulated digital baseband signal or digital intermediate frequency signal into a radio frequency signal, and transmit the radio frequency signal through one or more antennas 1015. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal. The order of the down-mixing and analog-to-digital conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal. The order of the up-mixing and digital-to-analog conversion processes is adjustable. The digital baseband signal and the digital intermediate frequency signal may be collectively referred to as digital signals.

[0325] The transceiver 1013 may also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, a device in the transceiver unit that implements a receiving function may be referred to as a receiving unit, and a device in the transceiver unit that implements a transmitting function may be referred to as a transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the transmitting unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0326] It should be noted that the communication device 1000 shown in Figure 10 can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and to achieve the corresponding technical effects of the network device. The specific implementation methods of the communication device 1000 shown in Figure 10 can refer to the description in the aforementioned method embodiment, and will not be repeated here one by one.

[0327] Please refer to FIG11 , which is a schematic structural diagram of the communication device involved in the above-mentioned embodiment provided in an embodiment of the present application.

[0328] It can be understood that the communication device 110 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to implement the technical solutions provided in this application. The communication device 110 can be the terminal device or network device described above, or a component (such as a chip) in these devices, used to implement the method described in the following method embodiment. The communication device 110 includes one or more processors 111. The processor 111 can be a general-purpose processor or a dedicated processor. For example, it can be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a RAN node, terminal, or chip, etc.), execute software programs, and process data of software programs.

[0329] Optionally, in one design, the processor 111 may include a program 113 (sometimes also referred to as code or instructions), which may be executed on the processor 111 to cause the communication device 110 to perform the methods described in the following embodiments. In yet another possible design, the communication device 110 includes circuitry (not shown in FIG11 ).

[0330] Optionally, the communication device 110 may include one or more memories 112 on which a program 114 (sometimes also referred to as code or instructions) is stored. The program 114 can be run on the processor 111, so that the communication device 110 executes the method described in the above method embodiment.

[0331] Optionally, the processor 111 and / or the memory 112 may include AI modules 117 and 118, which are used to implement AI-related functions. The AI ​​module can be implemented through software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a wireless intelligent control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0332] Optionally, data may be stored in the processor 111 and / or the memory 112. The processor and the memory may be provided separately or integrated together.

[0333] Optionally, the communication device 110 may further include a transceiver 115 and / or an antenna 116. The processor 111 may also be referred to as a processing unit, and controls the communication device (e.g., a RAN node or terminal). The transceiver 115 may also be referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, and is configured to implement the transceiver functions of the communication device through the antenna 116.

[0334] The processing unit 701 shown in FIG7 may be the processor 111. The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the transceiver 115 shown in FIG11 . The transceiver 115 may include an input interface and an output interface. Alternatively, the transceiver 115 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0335] An embodiment of the present application further 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 methods of the first communication device or the second communication device in the aforementioned embodiment.

[0336] 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 that may be implemented by the above-mentioned first communication device or second communication device.

[0337] An 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 to 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 communication device or the second communication device in the aforementioned method embodiment.

[0338] An embodiment of the present application further provides a communication system, wherein the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0339] In the several embodiments provided in this 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 merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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.

[0340] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0341] In addition, the functional units in the various embodiments 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 contributing part or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A communication method, characterized in that: include: receiving configuration information, where the configuration information is used to configure transmission resources for first information, where the first information is used to schedule transmission of first data; receiving the first data based on the first information; Sending second information, where the second information is used to schedule transmission of second data; sending the second data based on the second information; Among them, the first data is data after a first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or the second data is data after a first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes artificial intelligence AI processing, and / or the second processing includes AI processing.

2. The method according to claim 1, characterized in that The transmission resource of the first information includes a time domain resource that carries the first information; The time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

3. The method according to claim 1, characterized in that The first data is data after a first processing, and the second data is gradient data obtained based on the data after a second processing of the first data and label data; The first process satisfies any one of the following: the first process is triggered based on the second information, and the first information is triggered based on the first process; The second processing satisfies any of the following: the second processing is triggered based on the first information, the second information is triggered based on the second processing, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data.

4. The method according to claim 1 or 3, characterized in that: The second data is data after the first processing, and the first data is gradient data obtained based on the data after the second processing and label data of the second data; The first process satisfies any one of the following: the first process is triggered based on the first information, and the second information is triggered based on the first process; The second processing satisfies any of the following: the second processing is triggered based on the second information, the first information is triggered based on the second processing, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data.

5. The method according to claim 3 or 4, characterized in that: The configuration information includes a first configuration and a second configuration, the first configuration is used to configure a search space for the first information, and the second configuration is used to configure a retransmission timer for the second information; The time length of the period corresponding to the search space is less than or equal to the time length of the retransmission timer.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Sending first indication information, where the first indication information is used to indicate whether the first information is received correctly.

7. The method according to claim 6, characterized in that The first indication information indicates that the first information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the first indication information is also used to trigger the first processing; The first indication information indicates that the first information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the data after the second processing and label data, the first indication information indicates that the first information is correctly received. The information is also used to trigger the second process.

8. The method according to claim 6 or 7, characterized in that: The first indication information is also used to indicate whether to perform processing based on the first data.

9. The method according to any one of claims 1 to 8, characterized in that: After sending the second information, the method further includes: Second indication information is received, where the second indication information is used to indicate whether the second information is received correctly.

10. The method according to claim 9, characterized in that The second indication information indicates that the second information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the second indication information is also used to trigger the second processing; The second indication information indicates that the second information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second indication information is used to trigger the first processing.

11. The method according to claim 9 or 10, characterized in that: The second indication information is also used to indicate whether to perform processing based on the second data.

12. The method according to any one of claims 1 to 11, characterized in that: The second information is further used to indicate at least one of the following: The data type of the second data, and whether to send gradient information determined based on the second data.

13. The method according to any one of claims 1 to 12, characterized in that: The first information is further used to indicate at least one of the following: The data type of the first data, and whether to send gradient information determined based on the first data.

14. The method according to any one of claims 1 to 13, characterized in that: The method further comprises: sending capability information of a first communication device, where the capability information of the first communication device is used to determine the configuration information; The AI ​​capability information of the first communication device includes at least one of the following: The processing delay information of the first communication device on the forward data of the AI ​​network structure to which the first data belongs, the processing delay information of the first communication device on the reverse data in the AI ​​network structure to which the first data belongs, the batch size of the first data, the load information of the processing resources of the first communication device, and the computing resource information of the first communication device.

15. The method according to any one of claims 1 to 14, characterized in that The configuration information includes at least one of the following: The period of the first information, the window length for detecting the first information, and the position of the transmission symbol of the first information in the time slot.

16. The method according to any one of claims 1 to 15, characterized in that The method further comprises: Receive third information, where the third information is used to indicate at least one of the following: Information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs.

17. A communication method, characterized in that: include: Sending configuration information, where the configuration information is used to configure transmission resources for first information, where the first information is used to schedule transmission of first data; sending the first data based on the first information; receiving second information, where the second information is used to schedule transmission of second data; receiving the second data based on the second information; Among them, the first data is data after a first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, or the second data is data after a first processing and the first data is gradient data obtained based on the data after the second processing of the second data and label data; the first processing includes artificial intelligence AI processing, and / or the second processing includes AI processing.

18. The method according to claim 17, characterized in that The transmission resource of the first information includes a time domain resource that carries the first information; The time interval between the time domain resource carrying the first information and the time domain resource carrying the second information is preconfigured.

19. The method according to claim 17, characterized in that The first data is data after a first processing, and the second data is gradient data obtained based on the data after a second processing of the first data and label data; The first process satisfies any one of the following: the first process is triggered based on the second information, and the first information is triggered based on the first process; The second processing satisfies any of the following: the second processing is triggered based on the first information, the second information is triggered based on the second processing, the second processing is triggered based on the first data, and the second processing is triggered based on the first information and the first data.

20. The method according to claim 17 or 19, characterized in that The second data is data after the first processing, and the first data is gradient data obtained based on the data after the second processing and label data of the second data; The first process satisfies any one of the following: the first process is triggered based on the first information, and the second information is triggered based on the first process; The second processing satisfies any of the following: the second processing is triggered based on the second information, the first information is triggered based on the second processing, the second processing is triggered based on the second data, and the second processing is triggered based on the second information and the second data.

21. The method according to claim 19 or 20, characterized in that The configuration information includes a first configuration and a second configuration, the first configuration is used to configure a search space for the first information, and the second configuration is used to configure a retransmission timer for the second information; The time length of the period corresponding to the search space is less than or equal to the time length of the retransmission timer.

22. The method according to any one of claims 17 to 21, characterized in that The method further comprises: First indication information is received, where the first indication information is used to indicate whether the first information is received correctly.

23. The method according to claim 22, characterized in that The first indication information indicates that the first information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the first indication information is also used to trigger the first processing; The first indication information indicates that the first information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the first indication information is also used to trigger the second processing.

24. The method according to claim 22 or 23, characterized in that The first indication information is also used to indicate whether to perform processing based on the first data.

25. The method according to any one of claims 17 to 24, characterized in that After receiving the second information, the method further includes: Send second indication information, where the second indication information is used to indicate whether the second information is received correctly.

26. The method according to claim 25, characterized in that The second indication information indicates that the second information is correctly received, and when the first data is data after the first processing and the second data is gradient data obtained based on the data after the second processing of the first data and label data, the second indication information is also used to trigger the second processing; The second indication information indicates that the second information is correctly received. When the second data is data after the first processing and the first data is gradient data obtained based on the second data after the second processing and label data, the second indication information is used to trigger the first processing.

27. The method according to claim 25 or 26, characterized in that The second indication information is also used to indicate whether to perform processing based on the second data.

28. The method according to any one of claims 17 to 27, characterized in that The second information is further used to indicate at least one of the following: The data type of the second data, and whether to send gradient information determined based on the second data.

29. The method according to any one of claims 17 to 28, characterized in that The first information is further used to indicate at least one of the following: The data type of the first data, and whether to send gradient information determined based on the first data.

30. The method according to any one of claims 17 to 29, characterized in that The method further comprises: receiving capability information of a first communication device, wherein the capability information of the first communication device is used to determine the configuration information; The AI ​​capability information of the first communication device includes at least one of the following: The processing delay information of the first communication device on the forward data of the AI ​​network structure to which the first data belongs, the processing delay information of the first communication device on the reverse data in the AI ​​network structure to which the first data belongs, the batch size of the first data, the load information of the processing resources of the first communication device, and the computing resource information of the first communication device.

31. The method according to any one of claims 17 to 30, characterized in that The configuration information includes at least one of the following: The period of the first information, the window length for detecting the first information, and the position of the transmission symbol of the first information in the time slot.

32. The method according to any one of claims 17 to 31, characterized in that The method further comprises: Sending third information, where the third information is used to indicate at least one of the following: Information about the AI ​​network structure to which the first data belongs, hyperparameters of the AI ​​network structure, and data set information of the AI ​​task to which the first data belongs.

33. A communication device, characterized in that: Comprising means for performing the method as claimed in any one of claims 1 to 32.

34. A communication device, characterized in that: The method comprises at least one processor coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 32.

35. The communication device according to claim 34, characterized in that The communication device is a chip or a chip system.

36. A readable storage medium, characterized in that: The storage medium stores a computer program or an instruction, and when the computer program or the instruction is executed by the communication device, the method according to any one of claims 1 to 32 is implemented.

37. A computer program product, characterized in that The method comprises instructions which, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 32.

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