Communication method and related apparatus

Abstract

A communication method and a related apparatus. In the method, a first communication apparatus can send second channel information, such that a second communication apparatus can perform communication on the basis of the second channel information, wherein the basis for determining the second channel information comprises first channel information, time information of the first channel information and feature information of historical channel information, that is, the second channel information can not only reflect a time-domain correlation that corresponds to the time information of the first channel information, but can also reflect one or more of a time-domain correlation, a space-domain correlation and a frequency-domain correlation that correspond to the feature information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of second channel information can be improved, thereby improving the performance of transmitting data on the basis of the second channel information. In addition, the solution can be applied to channel information compression and feedback scenarios, such that the overheads of channel information feedback can be reduced.

Description

A communication method and related device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on April 3, 2024, with application number 202410405425.4 and application name “A communication method and related device”, 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 devices. Background Art

[0003] Wireless communication can be a transmission communication between two or more communication devices without propagating through conductors or cables. Generally, the two or more communication devices include network devices and terminal devices, or the two or more communication devices include different terminal devices.

[0004] Currently, multiple-input, multiple-output (MIMO) technology is used in the communication process between different communication devices to meet high-speed transmission requirements. For example, in the communication process between a network device and a terminal device, the network device can use channel measurement (or channel estimation) to obtain channel information. Based on this channel information, it calculates precoding information between the network device and the terminal device. Subsequently, data transmission between the network device and the terminal device can use this precoding information. Therefore, the accuracy of channel information directly affects the performance of data transmission.

[0005] However, in the process of wireless communication, how to improve the accuracy of channel information obtained by communication equipment is a technical problem that needs to be solved urgently. Summary of the Invention

[0006] The present application provides a communication method and related apparatus for improving the accuracy of channel information to improve the performance of data transmission based on the channel information.

[0007] 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 a network device), or the first communication device may be a component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the first communication device receives a first reference signal, which is used to determine first channel information; the first communication device processes the first channel information, the time information corresponding to the first channel information, and the first feature information based on a first artificial intelligence (AI) model to obtain second channel information and second feature information; wherein the second feature information is used to indicate the first historical channel information and the feature information of the first channel information, the first feature information is used to indicate the feature information of the first historical channel information, and the first feature information is the last processed output of the first AI model; the first communication device sends the second channel information.

[0008] Based on the above scheme, the first communication device can send the second channel information obtained based on the first channel information, so that the recipient of the second channel information (such as the second communication device) can communicate based on the second channel information. The second channel information is determined based on the first channel information, the time information of the first channel information, and the characteristic information of the historical channel information. That is, the second channel information can reflect the time domain correlation corresponding to the time information of the first channel information, and can also reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the second channel information can be improved, thereby improving the performance of data transmission based on the second channel information.

[0009] In addition, the first AI model can be used to compress channel information. That is, the channel information output by the first AI model is compressed based on the channel information input to the first AI model. Correspondingly, the second channel information can be compressed based on the first channel information, so that the data volume of the second channel information is smaller than that of the first channel information. In this way, the above solution can be applied to scenarios where channel information compression feedback is required, which can reduce the overhead of channel information feedback.

[0010] In addition, the output of the first AI model may also include second feature information in addition to the second channel information. Similarly, the second feature information is determined based on the first channel information, the time information of the first channel information, and the feature information of the historical channel information, that is, the second feature information can reflect the time domain correlation corresponding to the time information of the first channel information, and can also reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the feature information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the channel information subsequently obtained based on the second feature information can be improved, so as to improve the performance of data transmission based on the channel information.

[0011] In this application, AI model can be replaced by other terms, such as neural network, neural network model, AI neural network model, machine learning model, or AI processing model.

[0012] It should be understood that the first feature information is the output of the first AI model's previous processing. Similarly, the second feature information can be understood as the output of the first AI model's current processing. In other words, for the first AI model, the input for the next processing of the first AI model can include the second feature information.

[0013] Optionally, the first characteristic information is used to indicate characteristic information of the first historical channel information, which can be understood as the first characteristic information being used to indicate one or more of the channel characteristics, channel attributes, time domain characteristics of the channel, frequency domain characteristics of the channel, spatial domain characteristics of the channel, and code domain characteristics of the channel of the first historical channel information. In other words, the characteristic information involved in this application can be replaced by other terms, including one or more of the channel characteristics, channel attributes, time domain characteristics of the channel, frequency domain characteristics of the channel, spatial domain characteristics of the channel, and code domain characteristics of the channel.

[0014] In a possible implementation of the first aspect, the method also includes: the first communication device receives a second reference signal, and the second reference signal is used to determine third channel information; the first communication device processes the third channel information, the time information corresponding to the third channel information, and the second feature information based on the first AI model to obtain fourth channel information and third feature information; wherein the third feature information is used to indicate the feature information of the first historical channel information, the first channel information, and the third channel information; and the first communication device sends the fourth channel information.

[0015] Based on the above solution, the fourth channel information is determined based on the third channel information, the time information of the third channel information, and the characteristic information of the historical channel information. That is, the fourth channel information can reflect both the time domain correlation corresponding to the time information of the third channel information and one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the second channel information can be improved, thereby improving the performance of data transmission based on the second channel information.

[0016] In addition, the first AI model processes different channel information (for example, the first channel information and the third channel information) in sequence, so that the first AI model can accumulate characteristic information of channel information at multiple times as the process progresses, thereby increasing the amount of characteristic information of the historical channel information indicated by the characteristic information (for example, the first characteristic information, the second characteristic information, and the third characteristic information), thereby further improving the accuracy of the channel information obtained based on the characteristic information.

[0017] In a possible implementation of the first aspect, before the first communication device processes the third channel information and the second feature information based on the first AI model to obtain the fourth channel information and the third feature information, the method also includes: the first communication device receives first indication information, and the first indication information is used to indicate that the second channel information is successfully received.

[0018] Based on the above solution, after receiving the first indication information, the first communication device can determine that the second communication device has successfully received the second channel information. Accordingly, the first communication device can determine that the second communication device has successfully performed processing based on the second channel information and that the first communication device can perform processing based on the second characteristic information as input for the next processing to obtain the fourth channel information and the third characteristic information.

[0019] In a possible implementation of the first aspect, the method further includes: when a first condition is met, the first communication device sets the first feature information as a preconfigured parameter; the first condition includes at least one of the following:

[0020] Determining that a number of times the channel information and / or feature information processed by the first AI model reaches or exceeds a threshold;

[0021] Determining whether model performance information of the first AI model reaches or falls below a threshold;

[0022] Second indication information is received, where the second indication information is used to indicate that the first characteristic information is set as a preconfigured parameter.

[0023] Based on the above scheme, when the first condition is met, the first communication device determines that the current processing performance of the first AI model is low. Accordingly, the first communication device can set the first characteristic information as a preconfigured parameter, in order to improve the processing performance of the first AI model through the preconfigured parameter, so as to avoid affecting the accuracy of the channel information obtained by the first AI model.

[0024] Optionally, the first communication device may also process the first characteristic information based on other methods. For example, the first communication device may receive fifth indication information, and the fifth indication information is used to indicate that the first characteristic information is to be reset. In this way, the first communication device can reset the characteristic information accumulated by the first AI model based on the fifth indication information, in order to improve the processing performance of the first AI model through the reset characteristic information. Among them, the fifth indication information can be implemented in a variety of ways. For example, the fifth indication information can also be used to indicate the reset characteristic information (for example, the fifth indication information carries one or more parameters, and the one or more parameters are used to reset the first characteristic information). For another example, the fifth indication information can also include the reset characteristic information. For another example, the fifth indication information can also include the version number of the characteristic information, so that the first communication device resets the first characteristic information through the characteristic information corresponding to the version number.

[0025] Optionally, the fifth indication information may come from the second communication apparatus, or from a network device (the network device is different from the second communication apparatus).

[0026] In a possible implementation of the first aspect, the first historical channel information includes N1 channel information, and the N1 channel information corresponds to N1 time information respectively, where N1 is a positive integer; wherein, among the N1 time information, there exists at least one i-th time information and one j-th time information satisfying: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N1, and i is not equal to j.

[0027] Based on the above solution, the second channel information can be determined based on the first historical channel information, wherein the N1 channel information included in the first historical channel information can correspond to N1 time information respectively, and the N1 time information can be at unequal intervals. In this way, the solution is not limited to scenarios with equal time intervals, thereby improving the flexibility of the solution implementation.

[0028] The second aspect of the present application provides a communication method, which is performed by a second communication device. The second communication device 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, chip or 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 a first reference signal, which is used to determine the first channel information; the second communication device receives second channel information, which is obtained by processing the first channel information, time information corresponding to the first channel information, and first feature information based on the first AI model; wherein the first feature information is used to indicate feature information of the first historical channel information; the second communication device processes the second channel information and fourth feature information based on the second AI model to obtain fifth channel information and fifth feature information; wherein the fifth feature information is used to indicate the second historical channel information and feature information of the second channel information, the fourth feature information is used to indicate the feature information of the second historical channel information, and the fourth feature information is the last processed output of the second AI model; the second AI model is associated with the first AI model, and the second historical channel information is associated with the first historical channel information.

[0029] Based on the above solution, the second communication device can receive the second channel information, and after obtaining the fifth channel information based on the second channel information, the second communication device can communicate based on the fifth channel information. The fifth channel information is determined based on the second channel information and characteristic information of the historical channel information, that is, the second channel information can reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing the one or more correlations, the accuracy of the second channel information can be improved, thereby improving the performance of data transmission based on the second channel information.

[0030] In addition, the first AI model can be used to compress channel information, and the second AI model can be used to decompress channel information. That is, the second channel information determined by the first AI model is compressed based on the first channel information input by the first AI model, and the fifth channel information output by the second AI model is decompressed based on the second channel information input by the second AI model. Accordingly, the second channel information can be compressed based on the first channel information, so that the amount of data of the second channel information is less than that of the first channel information. In this way, the above solution can be applied to scenarios where channel information compression feedback is required, which can reduce the overhead of channel information feedback.

[0031] In addition, the output of the second AI model may also include fifth characteristic information in addition to the fifth channel information. Similarly, the basis for determining the fifth characteristic information includes the second channel information and the characteristic information of the historical channel information, that is, the second characteristic information can reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing the one or more correlations, the accuracy of the channel information subsequently obtained based on the fifth characteristic information can be improved, thereby improving the performance of data transmission based on the channel information.

[0032] Optionally, the second AI model is associated with the first AI model, including: the input of the second AI model is determined based on the output of the first AI model; and / or, the output of the second AI model is used to reconstruct the input of the first AI model.

[0033] In a possible implementation of the second aspect, the first historical channel information includes N1 channel information, and the second historical channel information includes N2 channel information, where N1=N2, and N1 and N2 are positive integers; the second historical channel information is associated with the first historical channel information, and includes: time information corresponding to the kth channel information in the N1 channel information, corresponding to the time information corresponding to the kth channel information in the N2 channel information; or, the time information corresponding to the kth channel information in the N1 channel information is the same as the time information corresponding to the kth channel information in the N2 channel information, and k ranges from 1 to N1.

[0034] Based on the above solution, the input of the first AI model includes the first historical channel information, the input of the second AI model includes the second historical channel information, and the first historical channel information and the second historical channel information satisfy the above-mentioned association relationship. In other words, the first AI model and the second AI model are interrelated, and the input of the first AI model and the input of the second AI model are matched. In this way, the inputs of the interrelated AI models can be matched, which can reduce or avoid the decline in AI model performance caused by model input mismatch.

[0035] In a possible implementation of the second aspect, the second communication device processes the second channel information and the fourth characteristic information based on the second AI model to obtain fifth channel information and fifth characteristic information, including: the second communication device processes the second channel information, the time information corresponding to the second channel information, and the fourth characteristic information based on the second AI model to obtain the fifth channel information and the fifth characteristic information.

[0036] Based on the above solution, in addition to the second channel information and the fourth characteristic information, the fifth channel information can also be determined based on the time information corresponding to the second channel information. That is, the fifth channel information can reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information, as well as the time domain correlation corresponding to the time information of the second channel information. Thus, by utilizing multiple correlations, the accuracy of the fifth channel information can be improved, thereby enhancing the performance of data transmission based on the fifth channel information.

[0037] In a possible implementation of the second aspect, the method also includes: the second communication device sends a second reference signal, which is used to determine the third channel information; the second communication device receives fourth channel information, which is obtained by processing the third channel information, the time information corresponding to the third channel information, and the second feature information based on the first AI model; wherein the second feature information is used to indicate the first historical channel information and the feature information of the first channel information; the second communication device processes the fourth channel information and the fifth feature information based on the second AI model to obtain sixth channel information and sixth feature information; wherein the sixth feature information is used to indicate the second historical channel information, the second channel information, and the feature information of the fourth channel information.

[0038] Based on the above solution, the second communication device can also receive the fourth channel information, and after obtaining the sixth channel information based on the fourth channel information, the second communication device can communicate based on the sixth channel information. The sixth channel information is determined based on characteristic information including the fourth channel information and the historical channel information, that is, the sixth channel information can reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing the one or more correlations, the accuracy of the sixth channel information can be improved, thereby improving the performance of data transmission based on the sixth channel information.

[0039] In addition, the second AI model processes different channel information (for example, the second channel information and the fourth channel information) in sequence, so that the first AI model can accumulate characteristic information of channel information at multiple times as the process progresses, thereby increasing the amount of characteristic information of the historical channel information indicated by the characteristic information (for example, the fourth characteristic information, the fifth characteristic information, and the sixth characteristic information), thereby further improving the accuracy of the channel information obtained based on the characteristic information.

[0040] Optionally, the second communication device processes the fourth channel information and the fifth characteristic information based on the second AI model to obtain sixth channel information and sixth characteristic information, including: the second communication device processes the fourth channel information, the time information corresponding to the fourth channel information and the fifth characteristic information based on the second AI model to obtain sixth channel information and sixth characteristic information. In this way, in addition to the fourth channel information and the fifth characteristic information, the basis for determining the sixth channel information may also include the time information corresponding to the fourth channel information. That is, the sixth channel information can reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information, and can also reflect the time domain correlation corresponding to the time information of the fourth channel information. Thus, by utilizing multiple correlations, the accuracy of the sixth channel information can be improved to improve the performance of data transmission based on the sixth channel information.

[0041] In a possible implementation manner of the second aspect, before the second communication apparatus receives the fourth channel information, the method further includes: the second communication apparatus sending first indication information, where the first indication information is used to indicate successful reception of the second channel information.

[0042] Based on the above solution, the second communication device may further transmit first indication information, so that after receiving the first indication information, the first communication device may determine that the second communication device has successfully received the second channel information. Accordingly, the first communication device may determine that the second communication device has successfully performed processing based on the second channel information, and may determine that the first communication device may perform processing based on the second channel information as input for the next processing to obtain the fourth channel information and the third characteristic information.

[0043] In a possible implementation manner of the second aspect, the method further includes: when a second condition is met, the second communication device sets the fourth characteristic information as a preconfigured parameter; the second condition includes at least one of the following:

[0044] Determining that a number of times the channel information and / or feature information processed by the second AI model reaches or exceeds a threshold;

[0045] Determining whether model performance information of the first AI model and / or the second AI model reaches or falls below a threshold;

[0046] Third indication information is received, where the third indication information is used to instruct to set the fourth characteristic information as a preconfigured parameter.

[0047] Based on the above scheme, when the second condition is met, the second communication device determines that the current processing performance of the first AI model and / or the second AI model is low. Accordingly, the first communication device can set the first feature information as a preconfigured parameter, in order to improve the processing performance of the second AI model through the preconfigured parameter, so as to avoid affecting the accuracy of the channel information obtained by the second AI model.

[0048] Optionally, the second communication device may also process the fourth characteristic information based on other methods. For example, the second communication device may receive sixth indication information, and the sixth indication information is used to indicate that the fourth characteristic information is to be reset. In this way, the second communication device can reset the characteristic information accumulated by the second AI model based on the sixth indication information, in order to improve the processing performance of the second AI model through the reset characteristic information. Among them, the sixth indication information can be implemented in a variety of ways. For example, the sixth indication information can also be used to indicate the reset characteristic information (for example, the sixth indication information carries one or more parameters, and the one or more parameters are used to reset the fourth characteristic information). For another example, the sixth indication information may also include the reset characteristic information. For another example, the sixth indication information may also include the version number of the characteristic information, so that the second communication device resets the fourth characteristic information through the characteristic information corresponding to the version number.

[0049] Optionally, the sixth indication information may come from the first communication apparatus, or from a network device (the network device is different from the first communication apparatus).

[0050] In a possible implementation of the second aspect, the second historical channel information includes N2 channel information, where the N2 channel information respectively corresponds to N2 time information, where N2 is a positive integer; wherein, among the N2 time information, there is at least one i-th time information and one j-th time information that satisfies: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N2, and i is not equal to j.

[0051] Based on the above solution, the fifth channel information may be determined based on the second historical channel information, wherein the N2 channel information included in the second historical channel information may correspond to N2 time information respectively, and the N2 time information may be at unequal intervals. In this way, the solution is not limited to scenarios with equal time intervals, thereby improving the flexibility of the solution implementation.

[0052] The third 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 a network device), or the first communication device may be a component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the first communication device obtains seventh channel information, which is determined by a third reference signal; the first communication device processes the seventh channel information, the time information corresponding to the seventh channel information, and the seventh feature information based on the third AI model to obtain eighth channel information and eighth feature information; wherein the eighth feature information is used to indicate the feature information of the third historical channel information and the seventh channel information, the seventh feature information is used to indicate the feature information of the third historical channel information, and the seventh feature information is the output of the last processing of the third AI model; the first communication device communicates based on the eighth channel information.

[0053] Based on the above scheme, the first communication device can communicate based on the eighth channel information output by the third AI model. The basis for determining the eighth channel information includes the seventh channel information, the time information of the seventh channel information, and the characteristic information of the historical channel information. That is, the eighth channel information can reflect the time domain correlation corresponding to the time information of the seventh channel information, and can also reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the eighth channel information can be improved, thereby improving the performance of data transmission based on the eighth channel information.

[0054] In addition, the output of the third AI model may also include eighth characteristic information in addition to the eighth channel information. Similarly, the basis for determining the eighth characteristic information includes the seventh channel information, the time information of the seventh channel information, and the characteristic information of the historical channel information, that is, the eighth characteristic information can reflect the time domain correlation corresponding to the time information of the seventh channel information, and can also reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the channel information subsequently obtained based on the eighth characteristic information can be improved, so as to improve the performance of data transmission based on the channel information.

[0055] Optionally, before the first communication device acquires the seventh channel information, the method further includes: the first communication device sending the third reference signal; wherein, the first communication device acquiring the seventh channel information includes: the first communication device receiving the seventh channel information.

[0056] Optionally, the first communication device acquiring the seventh channel information includes: the first communication device receiving the third reference signal, and the first communication device determining the seventh channel information based on the third reference signal.

[0057] In a possible implementation of the third aspect, the method also includes: the first communication device obtains ninth channel information, where the ninth channel information is determined by a fourth reference signal; the first communication device processes the ninth channel information, the time information corresponding to the ninth channel information, and the eighth feature information based on the third AI model to obtain tenth channel information and ninth feature information; wherein the ninth feature information is used to indicate feature information of the first historical channel information, the seventh channel information, and the ninth channel information; and the first communication device communicates based on the tenth channel information.

[0058] Based on the above solution, the tenth channel information is determined based on the ninth channel information, the time information of the ninth channel information, and the characteristic information of the historical channel information. That is, the tenth channel information can reflect both the time domain correlation corresponding to the time information of the ninth channel information and one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the tenth channel information can be improved, thereby improving the performance of data transmission based on the tenth channel information.

[0059] In addition, the third AI model processes different channel information (for example, the seventh channel information and the ninth channel information) in sequence, so that the third AI model can accumulate characteristic information of channel information at multiple times as the process progresses, thereby increasing the amount of information of the characteristic information of the historical channel information indicated by the characteristic information (for example, the seventh characteristic information, the eighth characteristic information, and the ninth characteristic information), thereby further improving the accuracy of the channel information obtained based on the characteristic information.

[0060] In a possible implementation manner of the third aspect, the method further includes: when a third condition is met, the first communication device sets the seventh characteristic information as a preconfigured parameter; the third condition includes at least one of the following:

[0061] Determining that a number of times the channel information and / or feature information processed by the third AI model reaches or exceeds a threshold;

[0062] Determining that model performance information of the third AI model reaches or falls below a threshold;

[0063] Fourth indication information is received, where the fourth indication information is used to instruct to set the seventh characteristic information as a preconfigured parameter.

[0064] Based on the above scheme, when the third condition is met, the first communication device determines that the current processing performance of the third AI model is low. Accordingly, the first communication device can set the seventh characteristic information as a preconfigured parameter, in order to improve the processing performance of the third AI model through the preconfigured parameter, so as to avoid affecting the accuracy of the channel information obtained by the third AI model.

[0065] Optionally, the first communication device may also process the seventh characteristic information based on other methods. For example, the first communication device may receive seventh indication information, and the seventh indication information is used to indicate that the seventh characteristic information is to be reset. In this way, the first communication device can reset the characteristic information accumulated by the third AI model based on the seventh indication information, in order to improve the processing performance of the third AI model through the reset characteristic information. Among them, the seventh indication information can be implemented in a variety of ways. For example, the seventh indication information can also be used to indicate the reset characteristic information (for example, the seventh indication information carries one or more parameters, and the one or more parameters are used to reset the seventh characteristic information). For another example, the seventh indication information can also include the reset characteristic information. For another example, the seventh indication information can also include the version number of the characteristic information, so that the first communication device resets the seventh characteristic information through the characteristic information corresponding to the version number.

[0066] In a possible implementation of the third aspect, the third historical channel information includes N1 channel information, where the N1 channel information respectively corresponds to N1 time information, where N1 is a positive integer; wherein, among the N1 time information, there exists at least one i-th time information and one j-th time information satisfying: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N1, and i is not equal to j.

[0067] Based on the above solution, the basis for determining the eighth channel information (and / or the tenth channel information) may include the third historical channel information, wherein the N1 channel information included in the third historical channel information may correspond to N1 time information respectively, and the N1 time information may be at unequal intervals. In this way, the solution is not limited to scenarios with equal time intervals, thereby improving the flexibility of the solution implementation.

[0068] It can be understood that in the first to third aspects, the use of the above-mentioned "first", "second", "third", etc. is only to avoid confusion and to distinguish the objects described in different aspects in the entire application document, and does not play any restrictive role.

[0069] The fourth 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 a first reference signal, and the first reference signal is used to determine first channel information; the processing unit processes the first channel information, the time information corresponding to the first channel information, and the first feature information based on the first AI model to obtain second channel information and second feature information; wherein the second feature information is used to indicate the first historical channel information and the feature information of the first channel information, the first feature information is used to indicate the feature information of the first historical channel information, and the first feature information is the last processed output of the first AI model; the transceiver unit is also used to send the second channel information.

[0070] 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 first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

[0071] In a fifth aspect, 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 send a first reference signal, which is used to determine first channel information; the transceiver unit is also used to receive second channel information, which is obtained by processing the first channel information, time information corresponding to the first channel information, and first feature information based on a first AI model; wherein the first feature information is used to indicate feature information of the first historical channel information; the processing unit is used to process the second channel information and fourth feature information based on a second AI model to obtain fifth channel information and fifth feature information; wherein the fifth feature information is used to indicate the second historical channel information and feature information of the second channel information, the fourth feature information is used to indicate the feature information of the second historical channel information, and the fourth feature information is the last processed output of the second AI model; the second AI model is associated with the first AI model, and the second historical channel information is associated with the first historical channel information.

[0072] In the fifth aspect of this 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.

[0073] In a sixth aspect, the present application provides a communication device, which is a first communication device and includes a transceiver unit and a processing unit. The processing unit is used to obtain seventh channel information, where the seventh channel information is determined by a third reference signal; the processing unit is also used to process the seventh channel information, the time information corresponding to the seventh channel information, and the seventh feature information based on a third AI model to obtain eighth channel information and eighth feature information; wherein the eighth feature information is used to indicate feature information of the third historical channel information and the seventh channel information, the seventh feature information is used to indicate feature information of the third historical channel information, and the seventh feature information is the last processed output of the third AI model; the transceiver unit is used to communicate based on the eighth channel information.

[0074] In the sixth aspect of this application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the third aspect and achieve corresponding technical effects. For details, please refer to the third aspect and will not be repeated here.

[0075] In a seventh aspect, the present application provides a communication device, comprising at least one processor coupled to a memory; the memory is configured to store programs or instructions; the at least one processor is configured to execute the programs or instructions, so that the communication device implements the method described in any possible implementation of any one of the first to third aspects. Optionally, the communication device may include the memory.

[0076] In an eighth aspect, the present application provides a communication device comprising at least one logic circuit and an input / output interface; the logic circuit is used to execute the method described in any possible implementation of any one of the first to third aspects.

[0077] In a ninth aspect, the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

[0078] In a tenth 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 third aspects above.

[0079] In an eleventh aspect of the present application, a computer program product (or computer program) is provided. 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 third aspects above.

[0080] A twelfth aspect of the present application provides a chip system, which includes 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 third aspects above.

[0081] 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.

[0082] Among them, the technical effects brought about by any design method in the fourth to twelfth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to third aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figures 1a to 1c are schematic diagrams of a communication system provided by this application;

[0084] Figures 1d, 1e, and 2a to 2c are schematic diagrams of the AI ​​processing process involved in this application;

[0085] 3a to 3c are some schematic diagrams of signal processing involved in this application;

[0086] FIG4 is an interactive schematic diagram of the communication method provided by this application;

[0087] Figures 5a and 5b are some schematic diagrams of signal processing provided by this application;

[0088] FIG6 is another interactive schematic diagram of the communication method provided by this application;

[0089] 7 to 11 are schematic diagrams of the communication device provided in this application. DETAILED DESCRIPTION

[0090] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0091] (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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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, the 6G network can further expand the form and function of 5G communication terminals. 6G terminals include but are not limited to vehicles, cellular network terminals (with integrated satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0096] 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.

[0097] (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.

[0098] 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).

[0099] 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).

[0100] 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.

[0101] 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.

[0102] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0103] Table 1

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] (3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used at the same time. Configuration refers to the network device and / or 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 ​​that the network device and / or server have pre-negotiated with 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 and / or server or terminal device. This application does not limit this.

[0109] Furthermore, these values ​​and parameters can be changed or updated.

[0110] (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.

[0111] (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.

[0112] 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.

[0113] 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.

[0114] (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.

[0115] 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 implementation methods of this application does not constitute a limitation on the scope of protection of this application.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0125] 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).

[0126] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The idea of ​​a neural network is derived from the neuron 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.

[0132] 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=[w0,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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The following is an illustrative description of the implementation process of a fully connected neural network, also known as a multilayer perceptron (MLP), with reference to the accompanying figures.

[0141] 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.

[0142] 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).

[0143] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0144] Alternatively, the output of the neural network can be recursively expressed as: y = f n (w n f n -1(…)+b n ).

[0145] Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0146] 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.

[0147] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0148] 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.

[0149] Alternatively, the gradient descent process can be expressed as:

[0150] 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.

[0151] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0152] 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:

[0153] 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.

[0154] The technical solution provided in this application can be applied to wireless communication systems (such as the system shown in Figure 1a or Figure 1b or Figure 1c). In wireless communication systems, MIMO technology is usually used to increase system capacity, that is, multiple antennas are used at the transmitting and receiving ends at the same time. In theory, the use of multiple antennas combined with space division multiplexing can increase system capacity exponentially, but in practice, the use of multiple antennas also brings about the problem of interference enhancement. Therefore, it is often necessary to perform certain processing on the signal to suppress the impact of interference. This method of interference suppression through signal processing can be implemented at the receiving end or at the transmitting end. When implemented at the transmitting end, the signal to be transmitted can be preprocessed and then sent through the MIMO channel. This transmission method is precoding.

[0155] In order to identify useful channels in the MIMO matrix (which can be represented as matrix H, which can also be called channel information H), multiple channels can be converted into a one-to-one mode similar to the single input single output (SISO) technology, so that the transmitted signal S1 corresponds to the received signal R1, and S2 corresponds to the received signal R2. In other words, multiple MIMO cross channels are converted into multiple parallel one-to-one channels. This process is achieved through the singular value decomposition (SVD) of the channel matrix, which satisfies: H = UΣV T ;

[0156] Where H is the channel information, U and V are orthogonal matrices, Σ is a diagonal matrix with singular values ​​as diagonal elements, and the superscript T indicates the transpose operation. For example, r = H * s + n, it can be expressed as r = UΣVT * s + n.

[0157] For example, when the data to be sent at the sending end is x, the information actually sent by the sending end can be expressed as: s = Vx. -1 U T After decoding, multiple one-to-one channels without interference can be obtained. The process of processing x based on "s=Vx" to obtain s at the transmitting end can be understood as a precoding operation, and V is the precoding matrix. In principle, the transmitting end performs singular value decomposition based on the actual channel information H to obtain the corresponding precoding matrix V. However, in actual systems, especially frequency division duplexing (FDD) systems, it is difficult to obtain the channel information H of MIMO signals. At this time, a series of V matrices, namely codebooks, are given by standard / protocol preconfiguration. The transmitting and receiving ends coordinate and select a suitable precoding matrix from them to determine the above-mentioned precoding matrix V. For example, in the 3GPP standard, the UE selects a V that can maximize the capacity of the channel matrix H through the PMI in the CSI feedback.

[0158] Taking the downlink channel measurement process implemented by the network device based on the downlink reference signal as an example, the downlink reference signal sent by the network device may include a channel state information reference signal (CSI-RS), and the network device may receive feedback of the CSI-RS, so that the network device can obtain channel information based on the feedback of the CSI-RS. Among them, the CSI-RS is sent through the port of the network device, that is, the feedback overhead of the CSI-RS is related to the number of ports of the network device. In other words, in the MIMO system, as the frequency band increases and the demand for high-speed communication increases, the number of ports of the communication device may gradually increase, which will lead to an increase in the feedback overhead of the channel information and occupy more transmission resources.

[0159] To solve this problem, the channel information can be compressed and decompressed to reduce the feedback overhead of the channel information. This will be described below with some implementation examples.

[0160] As an implementation example, the example shown in Figure 3a is an implementation example of an autoencoder. Among them, the autoencoder can be an artificial neural network structure. It is generally used to represent (also called encoding) a set of data, usually for dimensionality reduction (and / or compression), and includes an encoder and a decoder. The encoder encodes the input data through one or more layers of neural networks to obtain code words after dimensionality reduction (and / or compression). The decoder reconstructs the code words into output data through one or more layers of neural networks. The output data is required to be as identical as possible to the encoder input data. The encoder and decoder can generally achieve the above requirements through a process of simultaneous training or synchronous training.

[0161] As shown in Figure 3b, the channel information of the MIMO channel can be compressed and fed back from the perspective of space-frequency domain correlation based on the structure of an autoencoder. As an example, the UE estimates the MIMO channel, obtains the CSI matrix, and transforms it into the angle-delay domain using a two-dimensional Fourier transform. The transformed CSI matrix is ​​input into an encoder based on a machine learning model (typically a neural network) to obtain compressed CSI, i.e., a codeword. The resulting codeword undergoes possible quantization and is sent to the base station via a feedback channel. The base station dequantizes the received codeword and sends it to a decoder based on a machine learning model (typically a neural network) to reconstruct the MIMO channel CSI. After obtaining the reconstructed CSI, precoding calculations are performed based on it, and downlink transmission is performed. However, the compression of channel information (e.g., CSI) in the example shown in Figure 3a only utilizes the space-frequency domain correlation of the channel data during the aforementioned domain transformation process. Compared to non-machine learning methods, this solution has a relatively small performance gain.

[0162] As shown in Figure 3c, in order to further improve the performance of the CSI (taking channel information as CSI as an example) compression feedback method, in addition to considering the spatial-frequency domain correlation of CSI, the time-domain correlation of CSI is also taken into account. In Figure 3c, the input of the encoder, in addition to the channel information to be sent, may also include the accumulated CSI information output by the encoder last time. Similarly, the input of the decoder, in addition to the received channel information, may also include the accumulated CSI information output by the decoder last time. Optionally, in Figure 3c, the transmitter of the channel information may also quantize the channel information output by the encoder before sending it. Correspondingly, the receiver of the channel information may also dequantize the received channel information. The quantization and dequantization processes are optional steps.

[0163] Taking the feedback process at the third moment (t=3) as an example, in FIG3c, the input of the encoder includes the channel information to be compressed at this moment (denoted as V c , V c It can be the channel information H described above, or the matrix V obtained by SVD decomposition described above), and also includes the cumulative CSI information W output by the encoder at the previous moment (t=2) b Similarly, the output of the encoder includes not only the compressed CSI at this moment, but also the cumulative CSI information W output by the encoder at this moment c , where the compressed CSI at this moment is obtained after possible quantization operation to obtain V Qc , is sent to the other side (such as BS side). BS receives V Qc After possible dequantization operation, it is sent to the decoder. At the same time, the decoder input also includes the cumulative CSI information of the decoder at the previous moment. The decoder will output the reconstructed CSI at this moment And the cumulative CSI information of the decoder at this moment

[0164] As can be seen from the above process, in the process shown in Figure 3c, the encoder (or decoder) operations at each time (t = 1, 2, 3, ...) influence each other, similar to a one-way sequence processing neural network. That is, the input of the encoder (or decoder) at the current time includes the output of the encoder (or decoder) at the previous time, and the output of the encoder (or decoder) at the current time serves as the input of the encoder (or decoder) at the next time. Using this interface, the encoder and decoder can effectively exploit the temporal correlation of CSI at different times, further improving the performance of CSI compression feedback.

[0165] However, the implementation process shown in Figure 3c has many limitations. First, as shown in Figure 3c, the intervals between different time points (t = 1, 2, 3, ...) are equal, that is, the intervals are all k time slots (slots); second, when carrying the compressed (and possibly quantized) CSI information V Qc When the feedback information (usually uplink control information (UCI)) fails to be transmitted, the accumulated CSI information of the encoder and decoder will be mismatched, affecting the CSI compression feedback performance. Taking time t = 2 as an example, after the encoder side (UE side) completes CSI compression, the accumulated CSI information of the encoder will become W b , and if V Qb The transmission fails, and the decoder side (BS side) does not know V Qb has been sent, the decoder's accumulated CSI information will also remain At time t=3, when CSI compression feedback is continued, the encoder side will be based on the accumulated CSI information W b Perform CSI compression to obtain V Qc , and the decoder side receives V Qc After that, based on the accumulated CSI information Perform CSI decompression and reconstruction. At this time, W on the encoder side b and decoder side If a mismatch occurs, the performance of CSI compression feedback will be significantly affected. This requires both the encoder and decoder to reset their respective accumulated CSI information. These processes will affect the accuracy of the channel information obtained by the communication device, which directly affects the performance of subsequent data transmission.

[0166] Therefore, during the communication process, how to improve the accuracy of the channel information obtained by the communication equipment to improve the performance of data transmission is a technical problem that needs to be solved urgently.

[0167] In order to solve the above problems, the present application provides a communication method and related devices, which will be described in detail below with reference to the accompanying drawings.

[0168] Please refer to FIG4 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0169] It should be noted that in Figure 4, the method is illustrated by taking the first communication device and the second communication device as the execution entities of the interaction diagram as an example, but this application does not limit the execution entities of the interaction diagram. For example, in Figure 4, the execution entity of the method can be replaced by a chip, chip system, processor, logic module, or software in the communication device.

[0170] As an example, the first communication device may be a terminal device and the second communication device may be a network device.

[0171] As another example, the first communication device may be a network device, and the second communication device may be a terminal device.

[0172] As another example, the first communication device and the second communication device are both terminal devices, that is, the solution shown in Figure 4 can be applied to the sidelink communication scenario.

[0173] S401. A second communication device sends a first reference signal, and a first communication device receives the first reference signal. The first reference signal is used to determine first channel information, that is, after receiving the first reference signal, the first communication device can determine the first channel information based on the first reference signal.

[0174] S402. The first communication device processes the first channel information, the time information corresponding to the first channel information, and the first feature information based on the first AI model to obtain second channel information and second feature information. The second feature information is used to indicate the first historical channel information and the feature information of the first channel information, the first feature information is used to indicate the feature information of the first historical channel information, and the first feature information is the last processed output of the first AI model.

[0175] It should be noted that the channel information involved in the processing process of the AI ​​model involved in this application (for example, the first channel information and third channel information input by the first AI model, the second channel information and fourth channel information output by the first AI model, the second channel information and fourth channel information input by the second AI model, the fifth channel information and sixth channel information output by the second AI model, etc.) can be the channel information H described above, or it can be one or more of the matrix V obtained by the SVD decomposition described above or other forms of channel information.

[0176] S403. The first communication device sends second channel information, and correspondingly, the second communication device receives the second channel information.

[0177] It should be noted that the channel information involved in the transmission process of this application (e.g., the second channel information, fourth channel information, etc. sent by the first communication device) can be the channel information itself, or the quantization result of the channel information, or other forms of channel information. Similarly, the channel information involved in the reception process of this application (e.g., the second channel information, fourth channel information, etc. received by the second communication device) can be the channel information itself, or the quantization result of the channel information, or other forms of channel information.

[0178] S404. The second communication device processes the second channel information and the fourth characteristic information based on the second AI model to obtain fifth channel information and fifth characteristic information. The fifth characteristic information is used to indicate the second historical channel information and characteristic information of the second channel information, and the fourth characteristic information is used to indicate characteristic information of the second historical channel information. The fourth characteristic information is the last processed output of the second AI model. The second AI model is associated with the first AI model, and the second historical channel information is associated with the first historical channel information.

[0179] In this application, AI model can be replaced by other terms, such as neural network, neural network model, AI neural network model, machine learning model, or AI processing model.

[0180] It should be understood that the first feature information is the output of the first AI model's previous processing. Similarly, the second feature information can be understood as the output of the first AI model's current processing. In other words, for the first AI model, the input for the next processing of the first AI model can include the second feature information.

[0181] Optionally, the first characteristic information is used to indicate characteristic information of the first historical channel information, which can be understood as the first characteristic information being used to indicate one or more of the channel characteristics, channel attributes, time domain characteristics of the channel, frequency domain characteristics of the channel, spatial domain characteristics of the channel, and code domain characteristics of the channel of the first historical channel information. In other words, the characteristic information involved in this application can be replaced by other terms, including one or more of the channel characteristics, channel attributes, time domain characteristics of the channel, frequency domain characteristics of the channel, spatial domain characteristics of the channel, and code domain characteristics of the channel.

[0182] Optionally, the second AI model is associated with the first AI model, including: the input of the second AI model is determined based on the output of the first AI model; and / or, the output of the second AI model is used to reconstruct the input of the first AI model.

[0183] In one possible implementation, the first historical channel information includes N1 channel information, and the second historical channel information includes N2 channel information, where N1=N2, and N1 and N2 are positive integers; accordingly, the second historical channel information is associated with the first historical channel information, including: the time information corresponding to the kth channel information in the N1 channel information corresponds to the time information corresponding to the kth channel information in the N2 channel information; or, the time information corresponding to the kth channel information in the N1 channel information is the same as the time information corresponding to the kth channel information in the N2 channel information, and k ranges from 1 to N1. Specifically, the input of the first AI model includes the first historical channel information, the input of the second AI model includes the second historical channel information, and the first historical channel information and the second historical channel information satisfy the above-mentioned association relationship. In other words, the first AI model and the second AI model are mutually associated, and the input of the first AI model and the input of the second AI model match each other. In this way, the inputs of interconnected AI models can be matched with each other, which can reduce or avoid the degradation of AI model performance due to model input mismatch.

[0184] In one possible implementation, the first historical channel information includes N1 channel information, and the N1 channel information corresponds to N1 time information respectively, where N1 is a positive integer; wherein, among the N1 time information, there is at least one i-th time information and one j-th time information that satisfies: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N1, and i is not equal to j. Specifically, the basis for determining the second channel information may include the first historical channel information, wherein the N1 channel information contained in the first historical channel information may correspond to N1 time information respectively, and the N1 time information may be unequally spaced. In this way, the solution is not limited to scenarios with equal time intervals (such as the scenario shown in Figure 3c), thereby improving the flexibility of the solution implementation.

[0185] Similarly, the second historical channel information includes N2 channel information, each of which corresponds to N2 time information, where N2 is a positive integer; wherein, among the N2 time information, there is at least one i-th time information and one j-th time information such that: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N2, and i is not equal to j. In this way, the solution is not limited to scenarios with equal time intervals (such as the scenario shown in Figure 3c), thereby improving the flexibility of the solution implementation.

[0186] Based on the scheme shown in Figure 4, the first communication device can send second channel information obtained based on the first channel information, so that the recipient of the second channel information (e.g., the second communication device) can communicate based on the second channel information. The second channel information is determined based on the first channel information, the time information of the first channel information, and the characteristic information of the historical channel information. That is, the second channel information can reflect the time domain correlation corresponding to the time information of the first channel information, and can also reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the second channel information can be improved, thereby improving the performance of data transmission based on the second channel information.

[0187] In addition, the output of the first AI model may also include second feature information in addition to the second channel information. Similarly, the second feature information is determined based on the first channel information, the time information of the first channel information, and the feature information of the historical channel information, that is, the second feature information can reflect the time domain correlation corresponding to the time information of the first channel information, and can also reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the feature information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the channel information subsequently obtained based on the second feature information can be improved, so as to improve the performance of data transmission based on the channel information.

[0188] In a possible implementation of the method shown in Figure 4, step S404 may include: the second communication device processes the second channel information, the time information corresponding to the second channel information, and the fourth characteristic information based on the second AI model to obtain the fifth channel information and the fifth characteristic information. In other words, in addition to the second channel information and the fourth characteristic information, the basis for determining the fifth channel information may also include the time information corresponding to the second channel information. That is, the fifth channel information can reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information, and can also reflect the time domain correlation corresponding to the time information of the second channel information. Thus, by utilizing multiple correlations, the accuracy of the fifth channel information can be improved to improve the performance of data transmission based on the fifth channel information.

[0189] Optionally, the time information corresponding to the first channel information may indicate the time when the first channel information is generated, the time when the first reference signal for determining the first channel information is received, the time when the first reference signal for determining the first channel information is received, or other time associated with the first channel information. Similarly, the time information corresponding to the second channel information may indicate the time when the second channel information is received, the time when the reference signal corresponding to the second channel information (ie, the first reference signal) is received, the time when the reference signal corresponding to the second channel information (ie, the first reference signal) is sent, or other time associated with the second channel information. In addition, the time information corresponding to the first channel information used by the first AI model may be the same as or different from the time information corresponding to the second channel information used by the second AI model, and is not limited here.

[0190] In a possible implementation of the method shown in Figure 4, the method further includes: the first communication device receives a second reference signal, the second reference signal is used to determine third channel information; the first communication device processes the third channel information, the time information corresponding to the third channel information, and the second feature information based on the first AI model to obtain fourth channel information and third feature information; wherein the third feature information is used to indicate the feature information of the first historical channel information, the first channel information, and the third channel information; and the first communication device sends the fourth channel information. Specifically, the fourth channel information is determined based on the third channel information, the time information of the third channel information, and the feature information of the historical channel information, that is, the fourth channel information can reflect both the time domain correlation corresponding to the time information of the third channel information and one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the feature information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the second channel information can be improved, thereby improving the performance of data transmission based on the second channel information.

[0191] In addition, the first AI model processes different channel information (for example, the first channel information and the third channel information) in sequence, so that the first AI model can accumulate characteristic information of channel information at multiple times as the process progresses, thereby increasing the amount of characteristic information of the historical channel information indicated by the characteristic information (for example, the first characteristic information, the second characteristic information, and the third characteristic information), thereby further improving the accuracy of the channel information obtained based on the characteristic information.

[0192] Accordingly, the second communication device can be used to send the above-mentioned second reference signal and receive the above-mentioned fourth channel information. Moreover, after receiving the fourth channel information, the second communication device can process the fourth channel information and the fifth characteristic information based on the second AI model to obtain sixth channel information and sixth characteristic information; wherein the sixth characteristic information is used to indicate the characteristic information of the second historical channel information, the second channel information, and the fourth channel information. Thus, after the second communication device receives the fourth channel information and obtains the sixth channel information based on the fourth channel information, the second communication device can communicate based on the sixth channel information. The sixth channel information is determined based on the characteristic information of the fourth channel information and the historical channel information, that is, the sixth channel information can reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing one or more correlations, the accuracy of the sixth channel information can be improved, thereby improving the performance of data transmission based on the sixth channel information.

[0193] Similarly, the second AI model processes different channel information (for example, the second channel information and the fourth channel information) in sequence, so that the second AI model can accumulate characteristic information of channel information at multiple times as the process progresses, thereby increasing the amount of characteristic information of the historical channel information indicated by the characteristic information (for example, the fourth characteristic information, the fifth characteristic information, and the sixth characteristic information), thereby further improving the accuracy of the channel information obtained based on the characteristic information.

[0194] In one possible implementation, before the first communication device processes the third channel information and the second feature information based on the first AI model to obtain the fourth channel information and the third feature information, the method further includes: the first communication device receives first indication information from the second communication device, and the first indication information is used to indicate that the second channel information is successfully received. Specifically, after the first communication device receives the first indication information, the first communication device can determine that the second communication device has successfully received the second channel information. Accordingly, the first communication device can determine that the second communication device has successfully processed based on the second channel information, and determine that the first communication device can process based on the second feature information as input for the next processing to obtain the fourth channel information and the third feature information.

[0195] Optionally, the second communication device processes the fourth channel information and the fifth characteristic information based on the second AI model to obtain the sixth channel information and the sixth characteristic information, including: the second communication device processes the fourth channel information, the time information corresponding to the fourth channel information and the fifth characteristic information based on the second AI model to obtain the sixth channel information and the sixth characteristic information. In this way, in addition to the fourth channel information and the fifth characteristic information, the basis for determining the sixth channel information may also include the time information corresponding to the fourth channel information. That is, the sixth channel information can reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information, and can also reflect the time domain correlation corresponding to the time information of the fourth channel information. Thus, by utilizing multiple correlations, the accuracy of the sixth channel information can be improved to improve the performance of data transmission based on the sixth channel information.

[0196] It should be understood that the time information corresponding to the third channel information can refer to the implementation of the time information corresponding to the first channel information described above; the time information corresponding to the fourth channel information can refer to the implementation of the time information corresponding to the second channel information described above. The time information corresponding to the third channel information used by the first AI model and the time information corresponding to the fourth channel information used by the second AI model can be the same or different, and this is not limited here.

[0197] It can be understood that the first AI model deployed in the first communication device can be used to compress channel information, that is, the channel information output by the first AI model is compressed based on the channel information input by the first AI model; correspondingly, the second AI model deployed in the second communication device can be used to decompress channel information, that is, after the second communication device receives the second channel information, it can perform decompression processing based on the second AI model. For example, the first AI model can be an encoder, and the second AI model is a decoder corresponding to the encoder. In other words, the second channel information can be compressed based on the first channel information, so that the data volume of the second channel information is less than the data volume of the first channel information. In this way, the above scheme can be applied to the scenario of channel information compression feedback, which can reduce the overhead of channel information feedback.

[0198] As an example of the method shown in FIG4 , as shown in FIG5 a , the first AI model is an encoder (Encoder) in FIG5 a , and the second AI model is a decoder (Decoder) in FIG5 a .

[0199] In FIG5a, the input of the encoder includes: the channel information to be compressed (ie, the first channel information, denoted as V c ), the time position embedding information of the channel information to be compressed (ie, the time information corresponding to the first channel information, denoted as P c ), the encoder accumulates the CSI information of the last successful feedback history (i.e., the first feature information, denoted as W s ).

[0200] In FIG5a, the output of the encoder includes: compressed channel information (wherein the compressed channel information can be quantized to obtain V Qc , thereafter, the first communication device may send V in step S403 Qc , that is, V Qc can be used as an example of the second channel information), the encoder historical accumulated CSI information (ie, the second characteristic information, denoted as W c ).

[0201] Optionally, the quantization process in FIG5a may be an internal module of the encoder, that is, the function of the first AI model may include the quantization process function. In this case, it can be understood that the second channel information output by the first AI model is V Qc .

[0202] Similarly, in Figure 5a, the decoder input includes: QcThe channel information to be decompressed (i.e., the second channel information) is obtained after dequantization processing. The decoder accumulates the CSI information of the last successful feedback (i.e., the fourth characteristic information, recorded as Optionally, the input of the decoder also includes: time position embedded information of the channel information to be decompressed (ie, time information corresponding to the second channel information, denoted as Q c ).

[0203] Optionally, the dequantization process in FIG5b may be an internal module of the decoder, that is, the function of the second AI model may include the dequantization process function. In this case, it can be understood that the second channel information input by the second AI model is V Qc .

[0204] Optionally, the quantization process in FIG5a and the dequantization process in FIG5b are optional processes. For example, after the encoder outputs the compressed channel information, the encoder may transmit the compressed channel information (i.e., the compressed channel information may be used as an example of the second channel information); accordingly, the second channel information received by the decoder is the compressed channel information, and the compressed channel information may be used as part of the decoder's input.

[0205] Similarly, in FIG5a, the output of the decoder includes: the reconstructed channel information (ie, the fifth channel information, denoted as ), the decoder's historical accumulated CSI information (i.e., the fifth characteristic information, recorded as ).

[0206] As can be seen from the process shown in Figure 5a, the operations of the encoder and decoder at each moment are no longer strongly related to the previous moment and the next moment. In this way, the limitation of the feedback method limited to equal time intervals in the scenario shown in Figure 3c above can be lifted. While improving the flexibility of the solution implementation, the method of inputting the time information corresponding to the channel information during the processing of the AI ​​model can also improve the accuracy of the channel information obtained by the communication equipment, thereby improving the performance of subsequent data transmission based on the channel information.

[0207] Optionally, the encoder input P c and the decoder may input Q c They can be the same or different. For example, to determine the encoder input P c (or determine the possible input Q of the decoder c ) can be determined based on one or more of the current frame number k, subframe number s or other time position indication information r, that is, P c =f(k,s,r)(or,Q c =g(k,s,r)).

[0208] For example, as shown in FIG5b, P c (or Q c ) is determined based on the frame number s, and P c (or Q c ) is a vector of length M. An implementation example is given. As shown in Figure 5b, an NxM matrix is ​​determined, where N is the frame number range, ranging from 0 to 1023, and M is the length of the embedded information vector. The elements in this matrix can be obtained through training, preconfiguration, or calculation. For example, the calculation formula can include a periodic function such as sin(x) (or cos(x)), where the (k,m) element in the matrix (k represents the frame number index, and m represents the index of the element in the vector) can be expressed as sin(k / (m / M)), or sin((k / N) / (m / M)). The frame number can be the frame number itself or a representation of the frame number (such as binary). Optionally, since the maximum frame number is 1023, rollover may occur (i.e., frame 0 appears after frame 1023). In this case, relative positions can be used for position embedding. For example, the earliest sample in the data is set to position 0, and the relative positions of other samples are calculated relative to this earliest sample.

[0209] In one possible implementation, the method shown in FIG4 further includes: when a first condition is met, the first communication device sets the first characteristic information as a preconfigured parameter; the first condition includes at least one of the following:

[0210] Determining that a number of times the channel information and / or feature information processed by the first AI model reaches or exceeds a threshold;

[0211] Determining whether model performance information of the first AI model reaches or falls below a threshold;

[0212] Second indication information is received, where the second indication information is used to indicate that the first characteristic information is set as a preconfigured parameter.

[0213] Specifically, when the first condition is met, the first communication device determines that the current processing performance of the first AI model is low. Accordingly, the first communication device can set the first characteristic information as a preconfigured parameter, in order to improve the processing performance of the first AI model through the preconfigured parameter, so as to avoid affecting the accuracy of the channel information obtained by the first AI model.

[0214] Optionally, the first communication device may also process the first characteristic information based on other methods. For example, the first communication device may receive fifth indication information, and the fifth indication information is used to indicate that the first characteristic information is to be reset. In this way, the first communication device can reset the characteristic information accumulated by the first AI model based on the fifth indication information, in order to improve the processing performance of the first AI model through the reset characteristic information. Among them, the fifth indication information can be implemented in a variety of ways. For example, the fifth indication information can also be used to indicate the reset characteristic information (for example, the fifth indication information carries one or more parameters, and the one or more parameters are used to reset the first characteristic information). For another example, the fifth indication information can also include the reset characteristic information. For another example, the fifth indication information can also include the version number of the characteristic information, so that the first communication device resets the first characteristic information through the characteristic information corresponding to the version number.

[0215] Optionally, the fifth indication information may come from the second communication apparatus, or from a network device (the network device is different from the second communication apparatus).

[0216] Similarly, the method shown in FIG4 further includes: when a second condition is met, the second communication device sets the fourth characteristic information as a preconfigured parameter; the second condition includes at least one of the following:

[0217] Determining that a number of times the channel information and / or feature information processed by the second AI model reaches or exceeds a threshold;

[0218] Determining whether model performance information of the first AI model and / or the second AI model reaches or falls below a threshold;

[0219] Third indication information is received, where the third indication information is used to instruct to set the fourth characteristic information as a preconfigured parameter.

[0220] Specifically, when the second condition is met, the second communication device determines that the current processing performance of the first AI model and / or the second AI model is low. Accordingly, the first communication device can set the first characteristic information as a preconfigured parameter, in order to improve the processing performance of the second AI model through the preconfigured parameter to avoid affecting the accuracy of the channel information obtained by the second AI model.

[0221] Optionally, the second communication device may also process the fourth characteristic information based on other methods. For example, the second communication device may receive sixth indication information, and the sixth indication information is used to indicate that the fourth characteristic information is to be reset. In this way, the second communication device can reset the characteristic information accumulated by the second AI model based on the sixth indication information, in order to improve the processing performance of the second AI model through the reset characteristic information. Among them, the sixth indication information can be implemented in a variety of ways. For example, the sixth indication information can also be used to indicate the reset characteristic information (for example, the sixth indication information carries one or more parameters, and the one or more parameters are used to reset the fourth characteristic information). For another example, the sixth indication information may also include the reset characteristic information. For another example, the sixth indication information may also include the version number of the characteristic information, so that the second communication device resets the fourth characteristic information through the characteristic information corresponding to the version number.

[0222] Optionally, the sixth indication information may come from the first communication apparatus, or from a network device (the network device is different from the first communication apparatus).

[0223] Please refer to FIG6 , which is another schematic diagram of the communication method provided in this application. The method includes the following steps.

[0224] It should be noted that, in FIG6 , the method is illustrated by using the first communication device and other communication devices as examples of the execution entities of the interaction diagram, but this application does not limit the execution entities of the interaction diagram. For example, in FIG6 , the execution entity of the method can be replaced by a chip, chip system, processor, logic module, or software in the communication device.

[0225] As an example, the first communication device may be a terminal device and the other communication devices may be network devices.

[0226] As another example, the first communication device may be a network device, and the other communication devices may be terminal devices.

[0227] As another example, the first communication device and other communication devices are all terminal devices, that is, the solution shown in Figure 6 can be applied to the sidelink communication scenario.

[0228] S601. The first communication device obtains seventh channel information, where the seventh channel information is determined by a third reference signal.

[0229] As an example, before the first communications device acquires the seventh channel information in step S601, the method further includes: the first communications device transmitting the third reference signal; wherein the process of the first communications device acquiring the seventh channel information in step S601 includes: the first communications device receiving the seventh channel information. In other words, after the first communications device transmits the third reference signal, the seventh channel information acquired by the first communications device may be a measurement result sent by a receiver of the third reference signal.

[0230] As another example, the first communication device acquiring the seventh channel information includes: the first communication device receiving the third reference signal, and the first communication device determining the seventh channel information based on the third reference signal.

[0231] S602. The first communications device processes the seventh channel information, the time information corresponding to the seventh channel information, and the seventh feature information based on the third AI model to obtain eighth channel information and eighth feature information. The eighth feature information indicates feature information of the third historical channel information and the seventh channel information, and the seventh feature information indicates feature information of the seventh historical channel information, the seventh feature information being the last processed output of the third AI model.

[0232] Optionally, the third historical channel information includes N1 channel information, and the N1 channel information corresponds to N1 time information respectively, where N1 is a positive integer; wherein, among the N1 time information, there is at least one i-th time information and one j-th time information that satisfies: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N1, and i is not equal to j. Specifically, the basis for determining the eighth channel information may include the third historical channel information, wherein the N1 channel information contained in the third historical channel information may correspond to N1 time information respectively, and the N1 time information may be unequally spaced. In this way, the solution is not limited to scenarios with equal time intervals (such as the scenario shown in Figure 3c), thereby improving the flexibility of the solution implementation.

[0233] S603. The first communication device communicates with other communication devices based on the eighth channel information.

[0234] Based on the scheme shown in Figure 6, the first communication device can communicate based on the eighth channel information output by the third AI model. The basis for determining the eighth channel information includes the seventh channel information, the time information of the seventh channel information, and the characteristic information of the historical channel information. That is, the eighth channel information can reflect both the time domain correlation corresponding to the time information of the seventh channel information and one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the eighth channel information can be improved, thereby improving the performance of data transmission based on the eighth channel information.

[0235] In addition, the output of the third AI model may also include eighth characteristic information in addition to the eighth channel information. Similarly, the basis for determining the eighth characteristic information includes the seventh channel information, the time information of the seventh channel information, and the characteristic information of the historical channel information, that is, the eighth characteristic information can reflect the time domain correlation corresponding to the time information of the seventh channel information, and can also reflect one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the characteristic information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the channel information subsequently obtained based on the eighth characteristic information can be improved, so as to improve the performance of data transmission based on the channel information.

[0236] It is understandable that the method shown in Figure 6 is not limited to the scenario where the encoder and decoder are respectively deployed at the signal transmission and reception ends. For example, in step S603, the first communication device can communicate with other communication devices based on the eighth channel information obtained in step S602. Of course, the first communication device can also send the eighth channel information obtained based on step S602 to other communication devices, so that the other communication devices can perform signal transmission and reception processing based on the eighth channel information (or, the other communication devices can perform AI model or AI task processing related to the channel information based on the eighth channel information).

[0237] In one possible implementation of the method shown in FIG6 , after step S602, the method further includes: the first communication device obtaining ninth channel information, the ninth channel information being determined by a fourth reference signal; the first communication device processing the ninth channel information, the time information corresponding to the ninth channel information, and the eighth feature information based on the third AI model to obtain tenth channel information and ninth feature information; wherein the ninth feature information is used to indicate the feature information of the third historical channel information, the seventh channel information, and the ninth channel information; and the first communication device communicating based on the tenth channel information. Specifically, the tenth channel information is determined based on the ninth channel information, the time information of the ninth channel information, and the feature information of the historical channel information, that is, the tenth channel information can reflect both the time domain correlation corresponding to the time information of the ninth channel information and one or more of the time domain correlation, spatial domain correlation, and frequency domain correlation corresponding to the feature information of the historical channel information. Thus, by utilizing multiple correlations, the accuracy of the tenth channel information can be improved, thereby improving the performance of data transmission based on the tenth channel information.

[0238] In addition, the third AI model processes different channel information (for example, the seventh channel information and the ninth channel information) in sequence, so that the third AI model can accumulate characteristic information of channel information at multiple times as the process progresses, thereby increasing the amount of information of the characteristic information of the historical channel information indicated by the characteristic information (for example, the seventh characteristic information, the eighth characteristic information, and the ninth characteristic information), thereby further improving the accuracy of the channel information obtained based on the characteristic information.

[0239] In a possible implementation of the method shown in FIG6 , the method further includes: when a third condition is met, the first communication device sets the seventh characteristic information as a preconfigured parameter; the third condition includes at least one of the following:

[0240] Determining that a number of times the channel information and / or feature information processed by the third AI model reaches or exceeds a threshold;

[0241] Determining that model performance information of the third AI model reaches or falls below a threshold;

[0242] Fourth indication information is received, where the fourth indication information is used to instruct to set the seventh characteristic information as a preconfigured parameter.

[0243] Specifically, when the third condition is met, the first communication device determines that the current processing performance of the third AI model is low. Accordingly, the first communication device can set the seventh characteristic information as a preconfigured parameter, in order to improve the processing performance of the third AI model through the preconfigured parameter to avoid affecting the accuracy of the channel information obtained by the third AI model.

[0244] Optionally, the first communication device may also process the seventh characteristic information based on other methods. For example, the first communication device may receive seventh indication information, and the seventh indication information is used to indicate that the seventh characteristic information is to be reset. In this way, the first communication device can reset the characteristic information accumulated by the third AI model based on the seventh indication information, in order to improve the processing performance of the third AI model through the reset characteristic information. Among them, the seventh indication information can be implemented in a variety of ways. For example, the seventh indication information can also be used to indicate the reset characteristic information (for example, the seventh indication information carries one or more parameters, and the one or more parameters are used to reset the seventh characteristic information). For another example, the seventh indication information can also include the reset characteristic information. For another example, the seventh indication information can also include the version number of the characteristic information, so that the first communication device resets the seventh characteristic information through the characteristic information corresponding to the version number.

[0245] It should be noted that the implementation process of the third AI model can refer to the implementation process of the first AI model mentioned above.

[0246] 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).

[0247] 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.

[0248] 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 a first reference signal, which is used to determine the first channel information; the processing unit 701 processes the first channel information, the time information corresponding to the first channel information, and the first feature information based on the first AI model to obtain second channel information and second feature information; wherein the second feature information is used to indicate the first historical channel information and the feature information of the first channel information, the first feature information is used to indicate the feature information of the first historical channel information, and the first feature information is the last processed output of the first AI model; the transceiver unit 702 is also used to send the second channel information.

[0249] 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 a first reference signal, where the first reference signal is used to determine the first channel information; the transceiver unit 702 is also used to receive second channel information, where the second channel information is obtained by processing the first channel information, the time information corresponding to the first channel information, and the first feature information based on the first AI model; wherein the first feature information is used to indicate feature information of the first historical channel information; the processing unit 701 is used to process the second channel information and the fourth feature information based on the second AI model to obtain fifth channel information and fifth feature information; wherein the fifth feature information is used to indicate the second historical channel information and the feature information of the second channel information, the fourth feature information is used to indicate the feature information of the second historical channel information, and the fourth feature information is the last processed output of the second AI model; the second AI model is associated with the first AI model, and the second historical channel information is associated with the first historical channel information.

[0250] 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 processing unit 701 is used to obtain seventh channel information, where the seventh channel information is determined by a third reference signal; the processing unit 702 is also used to process the seventh channel information, the time information corresponding to the seventh channel information, and the seventh feature information based on the third AI model to obtain eighth channel information and eighth feature information; wherein the eighth feature information is used to indicate feature information of the third historical channel information and the seventh channel information, the seventh feature information is used to indicate feature information of the third historical channel information, and the seventh feature information is the last processed output of the third AI model; the transceiver unit 701 is used to communicate based on the eighth channel information.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] Optionally, the input-output interface 802 is used to receive a first reference signal, which is used to determine the first channel information; the logic circuit 801 processes the first channel information, the time information corresponding to the first channel information, and the first feature information based on the first AI model to obtain second channel information and second feature information; wherein the second feature information is used to indicate the first historical channel information and the feature information of the first channel information, the first feature information is used to indicate the feature information of the first historical channel information, and the first feature information is the last processed output of the first AI model; the input-output interface 802 is also used to send the second channel information.

[0255] Optionally, the input / output interface 802 is used to send a first reference signal, which is used to determine the first channel information; the input / output interface 802 is also used to receive second channel information, which is obtained by processing the first channel information, the time information corresponding to the first channel information, and the first feature information based on the first AI model; wherein the first feature information is used to indicate the feature information of the first historical channel information; the logic circuit 801 is used to process the second channel information and the fourth feature information based on the second AI model to obtain fifth channel information and fifth feature information; wherein the fifth feature information is used to indicate the second historical channel information and the feature information of the second channel information, the fourth feature information is used to indicate the feature information of the second historical channel information, and the fourth feature information is the last processed output of the second AI model; the second AI model is associated with the first AI model, and the second historical channel information is associated with the first historical channel information.

[0256] Optionally, the logic circuit 801 is used to obtain seventh channel information, where the seventh channel information is determined by a third reference signal; the logic circuit 801 is also used to process the seventh channel information, the time information corresponding to the seventh channel information, and the seventh feature information based on the third AI model to obtain eighth channel information and eighth feature information; wherein the eighth feature information is used to indicate the feature information of the third historical channel information and the seventh channel information, the seventh feature information is used to indicate the feature information of the third historical channel information, and the seventh feature information is the last processed output of the third AI model; the input and output interface 802 is used to communicate based on the eighth channel information.

[0257] 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.

[0258] In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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 as a terminal device in the above-mentioned embodiments. The example communication device shown in Figure 9 is implemented through the terminal device (or a component in the terminal device).

[0264] Here, 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 .

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] 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 communication device shown in Figure 10 is implemented by a network device (or a component in a network device), wherein the structure of the communication device can refer to the structure shown in Figure 10.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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 together, for example, within a single chip. Memory 1012 can store program code for executing the technical solutions of the embodiments of the present application, and execution is controlled by processor 1011. The various computer program codes executed can also be considered drivers for processor 1011.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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 ).

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] 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.

[0288] 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.

[0289] 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.

[0290] 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.

[0291] 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.

[0292] 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 a first reference signal, where the first reference signal is used to determine first channel information; Processing the first channel information, time information corresponding to the first channel information, and first feature information based on the first AI model to obtain second channel information and second feature information; wherein the second feature information is used to indicate feature information of the first historical channel information and the first channel information, the first feature information is used to indicate feature information of the first historical channel information, and the first feature information is output by the first AI model during the last processing; The second channel information is sent.

2. The method according to claim 1, characterized in that The method further comprises: receiving a second reference signal, where the second reference signal is used to determine third channel information; Processing the third channel information, the time information corresponding to the third channel information, and the second feature information based on the first AI model to obtain fourth channel information and third feature information; wherein the third feature information is used to indicate feature information of the first historical channel information, the first channel information, and the third channel information; The fourth channel information is sent.

3. The method according to claim 2, characterized in that Before processing the third channel information and the second feature information based on the first AI model to obtain fourth channel information and third feature information, the method further includes: First indication information is received, where the first indication information is used to indicate successful reception of the second channel information.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: When a first condition is met, the first characteristic information is set as a preconfigured parameter; the first condition includes at least one of the following: Determining that a number of times the channel information and / or feature information processed by the first AI model reaches or exceeds a threshold; Determining whether model performance information of the first AI model reaches or falls below a threshold; Second indication information is received, where the second indication information is used to instruct that the first characteristic information be set as a preconfigured parameter.

5. The method according to any one of claims 1 to 4, characterized in that The first historical channel information includes N1 channel information, the N1 channel information respectively corresponds to N1 time information, and N1 is a positive integer; Among them, among the N1 time information, there exists at least one i-th time information and one j-th time information that satisfies: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N1, and i is not equal to j.

6. A communication method, characterized in that: include: sending a first reference signal, where the first reference signal is used to determine first channel information; receiving second channel information, where the second channel information is obtained by processing the first channel information, time information corresponding to the first channel information, and first feature information based on the first AI model; wherein the first feature information is used to indicate feature information of the first historical channel information; The second channel information and the fourth characteristic information are processed based on the second AI model to obtain fifth channel information and fifth characteristic information; wherein the fifth characteristic information is used to indicate the second historical channel information and the characteristic information of the second channel information, the fourth characteristic information is used to indicate the characteristic information of the second historical channel information, and the fourth characteristic information is the last processed output of the second AI model; the second AI model is associated with the first AI model, and the second historical channel information is associated with the first historical channel information.

7. The method according to claim 6, characterized in that The second AI model is associated with the first AI model and includes: The input of the second AI model is determined based on the output of the first AI model; and / or, The output of the second AI model is used to reconstruct the input of the first AI model.

8. The method according to claim 6 or 7, characterized in that The first historical channel information includes N1 channel information, and the second historical channel information includes N2 channel information, where N1=N2, and N1 and N2 are positive integers; The second historical channel information is associated with the first historical channel information, including: The time information corresponding to the k-th channel information in the N1 channel information corresponds to the time information corresponding to the k-th channel information in the N2 channel information; or, The time information corresponding to the k-th channel information in the N1 channel information is the same as the time information corresponding to the k-th channel information in the N2 channel information, and the value of k ranges from 1 to N1.

9. The method according to any one of claims 6 to 8, characterized in that The processing of the second channel information and the fourth feature information based on the second AI model to obtain the fifth channel information and the fifth feature information includes: The second channel information, the time information corresponding to the second channel information, and the fourth feature information are processed based on the second AI model to obtain the fifth channel information and the fifth feature information.

10. The method according to any one of claims 6 to 9, characterized in that The method further comprises: sending a second reference signal, where the second reference signal is used to determine third channel information; receiving fourth channel information, where the fourth channel information is obtained by processing the third channel information, time information corresponding to the third channel information, and the second feature information based on the first AI model; wherein the second feature information is used to indicate feature information of the first historical channel information and the first channel information; The fourth channel information and the fifth characteristic information are processed based on the second AI model to obtain sixth channel information and sixth characteristic information; wherein the sixth characteristic information is used to indicate characteristic information of the second historical channel information, the second channel information, and the fourth channel information.

11. The method according to claim 10, characterized in that Before receiving the fourth channel information, the method further includes: First indication information is sent, where the first indication information is used to indicate successful reception of the second channel information.

12. The method according to any one of claims 6 to 11, characterized in that The method further comprises: When the second condition is met, the fourth characteristic information is set as a preconfigured parameter; the second condition includes at least one of the following: Determining that a number of times the channel information and / or feature information processed by the second AI model reaches or exceeds a threshold; Determining whether model performance information of the first AI model and / or the second AI model reaches or falls below a threshold; Third indication information is received, where the third indication information is used to instruct that the fourth characteristic information be set as a preconfigured parameter.

13. The method according to any one of claims 6 to 12, characterized in that The second historical channel information includes N2 channel information, the N2 channel information respectively corresponding to N2 time information, and N2 is a positive integer; Among them, among the N2 time information, there exists at least one i-th time information and one j-th time information that satisfies: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N2, and i is not equal to j.

14. A communication method, characterized in that: include: acquiring seventh channel information, where the seventh channel information is determined by a third reference signal; processing the seventh channel information, the time information corresponding to the seventh channel information, and the seventh feature information based on the third AI model to obtain eighth channel information and eighth feature information; wherein the eighth feature information is used to indicate feature information of the third historical channel information and the seventh channel information, the seventh feature information is used to indicate feature information of the third historical channel information, and the seventh feature information is the last processed output of the third AI model; Communication is performed based on the eighth channel information.

15. The method according to claim 14, characterized in that The method further comprises: acquiring ninth channel information, where the ninth channel information is determined by a fourth reference signal; processing the ninth channel information, the time information corresponding to the ninth channel information, and the eighth feature information based on the third AI model to obtain tenth channel information and ninth feature information; wherein the ninth feature information is used to indicate feature information of the third historical channel information, the seventh channel information, and the ninth channel information; Communication is performed based on the tenth channel information.

16. The method according to claim 14 or 15, characterized in that The method further comprises: When the third condition is met, the seventh characteristic information is set as a preconfigured parameter; the third condition includes at least one of the following: Determining that a number of times the channel information and / or feature information processed by the third AI model reaches or exceeds a threshold; Determining whether model performance information of the third AI model reaches or falls below a threshold; Fourth indication information is received, where the fourth indication information is used to instruct that the seventh characteristic information be set as a preconfigured parameter.

17. The method according to any one of claims 14 to 16, characterized in that The third historical channel information includes N1 channel information, the N1 channel information respectively corresponds to N1 time information, and N1 is a positive integer; Among them, among the N1 time information, there exists at least one i-th time information and one j-th time information that satisfies: the time interval between the i-th time information and the i+1-th time information is different from the time interval between the j-th time information and the j+1-th time information, i and j are both less than N1, and i is not equal to j.

18. The method according to any one of claims 14 to 17, characterized in that Before acquiring the seventh channel information, the method further includes: sending the third reference signal; wherein acquiring the seventh channel information includes: receiving the seventh channel information; or, The acquiring the seventh channel information includes: receiving the third reference signal, and determining the seventh channel information based on the third reference signal.

19. A communication device, characterized in that: Comprising means for performing the method according to any one of claims 1 to 18.

20. 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 18.

21. The communication device according to claim 20, wherein: The communication device is a chip or a chip system.

22. A readable storage medium, characterized in that The storage medium stores a computer program or instruction. When the computer program or instruction is executed by the communication device, the method according to any one of claims 1 to 18 is implemented.

23. A computer program product, characterized in that When the computer program product is run on a computer, the computer is caused to perform the method according to any one of claims 1 to 18.

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