Communication method and communication apparatus

Abstract

A communication method and a communication apparatus, applied to a scenario in which AI is combined with a wireless network. The method comprises: a second apparatus acquires first interference signal strength, and sends first indication information to a first apparatus, wherein the first indication information is used for indicating a first encoder model or a first encoder function, the first encoder model or the first encoder function is used for processing a CSI, and the first encoder model or the first encoder function is related to the first interference signal strength. By means of the technical solution of the present application, the first encoder model or the first encoder function is determined under the condition that the first interference signal strength is taken into account, in order to improve AI-based CSI feedback performance.

Description

Communication method and communication device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on December 28, 2023, with application number 202311852961.0, and invention name “A Communication Method and Communication Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communication technology, and more particularly, to a communication method and a communication device. Background Art

[0003] In communications systems, network equipment determines downlink channel configuration information, including resources, modulation and coding schemes (MCS), and precoding, for scheduling terminal devices' downlink data channels based on downlink channel state information (CSI). Terminal devices can calculate downlink CSI by measuring downlink reference signals and generate CSI reports that are fed back to the network equipment.

[0004] The introduction of artificial intelligence (AI) into wireless communication networks has led to the emergence of AI-based CSI feedback methods. AI models possess strong feature extraction capabilities and can effectively compress channel information, reducing information loss during the compression process and ensuring the accuracy of recovered channel information. However, research has found that when using AI-based CSI feedback, the CSI feedback performance corresponding to the same AI model or AI function can be poor at certain times. Summary of the Invention

[0005] The present application provides a communication method and a communication device to improve the performance of AI-based CSI feedback.

[0006] In a first aspect, a communication method is provided. The method can be performed by a first device, or can also be performed by a chip or circuit of the first device, which is not limited in this application. For ease of description, the following description is based on the example of execution by the first device. The first device can be a terminal device or a network device, or a chip, chip system or circuit in the terminal device or network device, or a functional module in the terminal device or network device that can call and execute a program.

[0007] The method includes: receiving first indication information from a second device, the first indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function is used to process channel state information CSI, and the first encoder model or the first encoder function is related to the first interference signal strength.

[0008] In the present application, the first encoder model or the first encoder function is related to the first interference signal strength, which can be understood as: the first encoder model or the first encoder function is determined according to the first interference signal strength, that is, the first encoder model or the first encoder function is selected or matched while considering the interference signal strength.

[0009] According to the solution provided in the present application, the first device can determine the first encoder model or the first encoder function based on the received first indication information, and then the first device can subsequently use the first encoder model or the model corresponding to the first encoder function to compress and quantize the CSI, and the second device can use the first decoder model or the model corresponding to the first decoder function to decompress and quantize the CSI, thereby improving the CSI feedback performance.

[0010] In the present application, the first encoder model corresponds to the first decoder model, that is, the first encoder model and the first decoder model are usually trained together and can be used in combination with each other. It should be understood that the number of AI models included in the first encoder model and the first decoder model used in matching is the same and corresponds one to one, and the first encoder model and the first decoder model can be understood as matching AI models. Among them, the first encoder model and the first decoder model can be called a two-end model, a bilateral model, a collaborative model, or a dual model, etc. For example, the first encoder model can be an encoder for compressing CSI, and the first decoder model can be a decoder for restoring compressed CSI. Similarly, the first encoder function corresponds to the first decoder function, and the model corresponding to the first encoder function corresponds to the model corresponding to the first decoder function.

[0011] In combination with the first aspect, in some implementations of the first aspect, the first encoder function includes one or more first encoder models.

[0012] It should be understood that the first encoder function may correspond to one or more encoder models having the same function, and the first device may determine the one or more encoder models through the first encoder function.

[0013] In combination with the first aspect, in certain implementations of the first aspect, the first indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or, the first indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

[0014] Based on the above scheme, the first indication information carries the identifier of the first encoder model and / or the model parameters of the first encoder model, so that the first device can determine the first encoder model, and then use the first encoder model to compress and quantize the CSI to ensure CSI feedback performance; or, the first indication information carries the identifier of the first encoder function and / or the model parameters of the first encoder function, so that the first device can determine the model corresponding to the first encoder function, and then use the model to compress and quantize the CSI to ensure CSI feedback performance.

[0015] In combination with the first aspect, in certain implementations of the first aspect, when the first device is a terminal device and the second device is a network device, before receiving the first indication information from the second device, the method also includes: receiving configuration information from the second device, the configuration information being used to indicate a first reference signal, the first reference signal including one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; performing channel measurement on the first reference signal to obtain first interference information, the first interference information being used to indicate the strength of the first interference signal; and sending the first interference information to the second device.

[0016] Based on the above scheme, the second device triggers the channel measurement process, that is, the first device can perform channel measurement on the first reference signal according to the received configuration information to obtain a channel measurement result, wherein the channel measurement result includes first interference information, which is used to determine the current channel interference level, so that the appropriate encoder model or encoder function can be selected or matched based on the first interference information to ensure network performance.

[0017] In combination with the first aspect, in certain implementations of the first aspect, when the first device is a terminal device and the second device is a network device, before receiving the first indication information from the second device, the method also includes: sending model library information to the second device, the model library information being used to indicate a mapping relationship between multiple encoder models and multiple interference signal strength ranges, or the model library information being used to indicate a mapping relationship between multiple encoder functions and multiple interference signal strength ranges, the first encoder model belongs to multiple encoder models, the first encoder function belongs to multiple encoder functions, and the first interference signal strength is included in an interference signal strength range among the multiple interference signal strength ranges.

[0018] Based on the above scheme, the terminal device sends the model library information to the network device, so that the network device can determine the corresponding first encoder model or first encoder function from the model library information based on the obtained first interference signal strength, and then the first device and the second device can subsequently use the first encoder model and its matching first decoder model, or use the model corresponding to the first encoder function and its matching first decoder function to compress and restore the channel information, thereby reducing the signaling overhead and improving the CSI feedback performance.

[0019] In combination with the first aspect, in certain implementations of the first aspect, when the first device is a network device and the second device is a terminal device, before receiving the first indication information from the second device, the method also includes: sending configuration information to the second device, the configuration information being used to indicate a first reference signal, the first reference signal including one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; wherein the first interference information is obtained based on a measurement of the first reference signal, and the first interference information is used to indicate the strength of the first interference signal.

[0020] In combination with the first aspect, in certain implementations of the first aspect, when the first device is a network device and the second device is a terminal device, before sending configuration information to the second device, the method also includes: receiving a request message from the second device, the request message being used to request the second device to send configuration information.

[0021] Based on the above scheme, the network device can determine the trigger signal interference measurement process based on the request message of the terminal device, that is, trigger the sending of configuration information to the terminal device, so that the terminal device can perform channel measurement on the first reference signal based on the configuration information to obtain the first interference information, and thus select a suitable first encoder model or first encoder function while considering the first interference information (or the first interference signal strength) to improve the CSI feedback performance.

[0022] In combination with the first aspect, in certain implementations of the first aspect, when the first device is a network device and the second device is a terminal device, the method also includes: sending second indication information to the second device, the second indication information being used to indicate that the first device has selected or matched the first decoder model or the first decoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

[0023] Based on the above scheme, the second indication information can be used to determine that the first device and the second device have selected or matched the first encoder model and the first decoder model, or the first encoder function and the first decoder function, so that compression quantization processing and decompression quantization processing of the first reference signal can be realized, thereby improving the CSI feedback performance.

[0024] In combination with the first aspect, in certain implementations of the first aspect, when the first device is a network device and the second device is a terminal device, the method further includes: receiving first interference information from the second device, the first interference information being used to indicate a first interference signal strength.

[0025] Based on the above scheme, the terminal device sends the first interference information to the network device. The network device can know from the first interference information that the first encoder model or the first encoder function subsequently indicated by the terminal device through the first indication information is associated with the first interference information, or in other words, the network device can know that the first encoder model or the first encoder function is determined based on the first interference information.

[0026] In combination with the first aspect, in certain implementations of the first aspect, when the first device is a terminal device and the second device is a network device, the method also includes: receiving a first CSI-RS from the second device; sending a first result to the second device, the first result being obtained by processing the first CSI using a first encoder model or a first encoder function, the first CSI being obtained by measuring the first CSI-RS, and the first CSI being related to the first interference signal strength.

[0027] Based on the above scheme, based on the selected or matched first encoder model and first decoder model, or the first encoder function and the first decoder function, the first device and the second device can effectively realize the measurement, compression quantization processing, decompression quantization processing, etc. of the first reference signal, thereby reducing the signaling overhead and improving the CSI feedback performance.

[0028] In combination with the first aspect, in certain implementations of the first aspect, when the first device is a network device and the second device is a terminal device, the method also includes: sending a first CSI-RS to the second device; receiving a first result from the second device, the first result being obtained by processing the first CSI based on a first encoder model or a first encoder function, and the first CSI being obtained by measuring the first CSI-RS.

[0029] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: obtaining a second result, the second result being obtained by processing the first result based on a first decoder model or a first decoder function, the first decoder model corresponding to the first encoder model, and the first decoder function corresponding to the first encoder function.

[0030] It should be understood that the first encoder model in the present application can be deployed on the first device side, and the second encoder model can be deployed on the second device side. The first device and the second device can use the first encoder model and the first decoder model to perform CSI feedback. Optionally, the first encoder model and the second encoder model can also be deployed on other device sides. For example, the first encoder model can be deployed on the third device side (or over the top (OTT) or cloud device side), and the second encoder model can be deployed on the fourth device side (or OTT or cloud device side). After measuring and obtaining the first CSI, the first device needs to exchange information with the third device. The third device uses the first encoder model to compress the first CSI to obtain feedback CSI, and sends the feedback CSI to the first device. Correspondingly, after obtaining the feedback CSI, the second device needs to exchange information with the fourth device. The fourth device uses the first decoder model to decompress the feedback CSI to obtain recovered CSI, and sends the recovered CSI to the second device.

[0031] In a second aspect, a communication method is provided. The method can be performed by a second device, or can also be performed by a chip or circuit of the second device, which is not limited in this application. For ease of description, the following description is based on the example of execution by the second device. The second device can be a terminal device or a network device, or a chip, chip system or circuit in the terminal device or network device, or a functional module in the terminal device or network device that can call and execute a program.

[0032] The method includes: obtaining a first interference signal strength; sending a first indication message to a first device, the first indication message being used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function being used to process channel state information CSI, the first encoder model or the first encoder function being related to the first interference signal strength.

[0033] In the present application, the first encoder model or the first encoder function is related to the first interference signal strength, which can be understood as: the first encoder model or the first encoder function is determined according to the first interference signal strength, that is, the first encoder model or the first encoder function is selected or matched while considering the interference signal strength.

[0034] According to the solution provided in the present application, the second device can select or match the corresponding first encoder model or first encoder function based on the obtained first interference signal strength, and indicate the first encoder model or first encoder function to the first device through the first indication information. Subsequently, the first device can use the first encoder model or the model corresponding to the first encoder function to compress and quantize the CSI, and the second device can use the first decoder model or the model corresponding to the first decoder function to decompress and quantize the CSI, thereby ensuring CSI feedback performance.

[0035] In conjunction with the second aspect, in some implementations of the second aspect, the first encoder function includes one or more first encoder models.

[0036] In combination with the second aspect, in certain implementations of the second aspect, the first indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or, the first indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

[0037] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a terminal device and the second device is a network device, obtaining the first interference signal strength includes: receiving first interference information from the first device, the first interference information is used to indicate the first interference signal strength.

[0038] In combination with the second aspect, in certain implementations of the second aspect, before receiving the first interference information from the first device, the method also includes: sending configuration information to the first device, the configuration information being used to indicate a first reference signal, the first reference signal including one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; wherein the first interference information is obtained based on channel measurement of the first reference signal.

[0039] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a terminal device and the second device is a network device, obtaining the first interference signal strength includes: obtaining the first interference information through local retrieval or prediction; or, obtaining the first interference information through cloud retrieval or prediction.

[0040] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a terminal device and the second device is a network device, before sending the first indication information to the first device, the method also includes: receiving model library information from the first device, the model library information is used to indicate the mapping relationship between multiple encoder models and multiple signal strength ranges, or the model library information is used to indicate the mapping relationship between multiple encoder functions and multiple interference signal strength ranges, the first encoder model belongs to multiple encoder models, the first encoder function belongs to multiple encoder functions, and the first interference signal strength is included in an interference signal strength range among the multiple interference signal strength ranges.

[0041] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a network device and the second device is a terminal device, obtaining the first interference signal strength includes: receiving configuration information from the first device, the configuration information is used to indicate a first reference signal, the first reference signal includes one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; performing channel measurement on the first reference signal according to the configuration information to obtain first interference information, the first interference information is used to indicate the first interference signal strength.

[0042] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a network device and the second device is a terminal device, obtaining the first interference signal strength includes: performing interference prediction or perception operations to obtain the first interference signal strength.

[0043] In combination with the second aspect, in certain implementations of the second aspect, before obtaining the first interference signal strength, when the first device is a network device and the second device is a terminal device, the method also includes: sending a request message to the first device, the request message being used to request the first device to send configuration information.

[0044] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a network device and the second device is a terminal device, the method also includes: receiving second indication information from the first device, the second indication information being used to indicate that the first device has selected or matched the first decoder model or the first encoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

[0045] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a network device and the second device is a terminal device, the method further includes: sending first interference information to the first device, where the first interference information is used to indicate the strength of the first interference signal.

[0046] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a terminal device and the second device is a network device, the method also includes: sending a first CSI-RS to the first device; receiving a first result from the first device, the first result being obtained by processing the first CSI based on a first encoder model or a first encoder function, the first CSI being obtained by measuring the first CSI-RS, and the first CSI being related to the first interference signal strength.

[0047] In combination with the second aspect, in certain implementations of the second aspect, the method also includes: obtaining a second result, the second result being obtained by processing the first result based on a first decoder model or a first decoder function, the first decoder model corresponding to the first encoder model, and the first decoder function corresponding to the first encoder function.

[0048] In combination with the second aspect, in certain implementations of the second aspect, when the first device is a network device and the second device is a terminal device, the method also includes: receiving a first CSI-RS from the first device; sending a first result to the first device, the first result being obtained by processing the first CSI based on a first encoder model or a first encoder function, and the first CSI being obtained by measuring the first CSI-RS.

[0049] The beneficial effects of the above-mentioned second aspect and certain implementation methods of the second aspect can be referred to the corresponding description of the first aspect, and will not be repeated here.

[0050] In a third aspect, a communication method is provided, which can be executed by a terminal device, or by a chip or circuit of the terminal device, which is not limited in this application. For ease of description, the following description is based on an example of execution by a terminal device.

[0051] The method includes: receiving first indication information from a network device, the first indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function is used to process channel state information CSI, and the first encoder model or the first encoder function is related to the first interference signal strength.

[0052] In combination with the third aspect, in some implementations of the third aspect, the first encoder function includes one or more first encoder models.

[0053] In combination with the third aspect, in certain implementations of the third aspect, the first indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or, the first indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

[0054] In combination with the third aspect, in certain implementations of the third aspect, before receiving the first indication information from the network device, the method also includes: receiving configuration information from the network device, the configuration information is used to indicate a first reference signal, the first reference signal including one or more of the following: channel state information reference signal CSI-RS, zero power channel state information reference signal ZP CSI-RS, channel state information interference measurement CSI-IM signal; performing channel measurement on the first reference signal to obtain first interference information, the first interference information is used to indicate the strength of the first interference signal; and sending the first interference information to the network device.

[0055] In combination with the third aspect, in certain implementations of the third aspect, before receiving the first indication information from the network device, the method also includes: sending model library information to the network device, the model library information being used to indicate a mapping relationship between multiple encoder models and multiple interference signal strength ranges, or the model library information being used to indicate a mapping relationship between multiple encoder functions and multiple interference signal strength ranges, the first encoder model belonging to multiple encoder models, the first encoder function belonging to multiple encoder functions, and the first interference signal strength being included in an interference signal strength range among the multiple interference signal strength ranges.

[0056] In combination with the third aspect, in certain implementations of the third aspect, the method also includes: receiving a first CSI-RS from a network device; sending a first result to the network device, the first result being obtained by processing the first CSI based on a first encoder model or a first encoder function, the first CSI being obtained by measuring the first CSI-RS, and the first CSI being related to the first interference signal strength.

[0057] In a fourth aspect, a communication method is provided, which can be executed by a network device, or by a chip or circuit of the network device, which is not limited in this application. For ease of description, the following description is based on an example of execution by a network device.

[0058] The method includes: obtaining a first interference signal strength; sending a first indication information to a terminal device, the first indication information being used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function being used to process channel state information CSI, and the first encoder model or the first encoder function being related to the first interference signal strength.

[0059] In combination with the fourth aspect, in some implementations of the fourth aspect, the first encoder function includes one or more first encoder models.

[0060] In combination with the fourth aspect, in certain implementations of the fourth aspect, the first indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or, the first indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

[0061] In combination with the fourth aspect, in some implementations of the fourth aspect, obtaining the first interference signal strength includes: receiving first interference information from a terminal device, where the first interference information is used to indicate the first interference signal strength.

[0062] In combination with the fourth aspect, in certain implementations of the fourth aspect, before receiving the first interference information from the terminal device, the method also includes: sending configuration information to the terminal device, the configuration information being used to indicate a first reference signal, the first reference signal including one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; wherein the first interference information is obtained based on channel measurement of the first reference signal.

[0063] In combination with the fourth aspect, in certain implementations of the fourth aspect, obtaining the first interference signal strength includes: obtaining the first interference information through local retrieval or prediction; or obtaining the first interference information through cloud retrieval or prediction.

[0064] In combination with the fourth aspect, in certain implementations of the fourth aspect, the method also includes: receiving model library information from a terminal device, the model library information is used to indicate a mapping relationship between multiple encoder models and multiple signal strength ranges, or the model library information is used to indicate a mapping relationship between multiple encoder functions and multiple interference signal strength ranges, the first encoder model belongs to multiple encoder models, the first encoder function belongs to multiple encoder functions, and the first interference signal strength is included in an interference signal strength range among the multiple interference signal strength ranges.

[0065] In combination with the fourth aspect, in certain implementations of the fourth aspect, the method further includes: sending a first CSI-RS to a terminal device; receiving a first result from the terminal device, the first result being obtained by processing the first CSI based on a first encoder model or a first encoder function, and the first CSI being obtained by measuring the first CSI-RS.

[0066] In combination with the fourth aspect, in certain implementations of the fourth aspect, the method also includes: obtaining a second result, the second result being obtained by processing the first result based on a first decoder model or a first decoder function, the first decoder model corresponding to the first encoder model, and the first decoder function corresponding to the first encoder function.

[0067] In a fifth aspect, a communication method is provided, which can be executed by a terminal device, or by a chip or circuit of the terminal device, which is not limited in this application. For ease of description, the following description is based on an example of execution by a terminal device.

[0068] The method includes: obtaining a first interference signal strength; sending a second indication information to a network device, the second indication information being used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function being used to process channel state information CSI, and the first encoder model or the first encoder function being related to the first interference signal strength.

[0069] In combination with the fifth aspect, in certain implementations of the fifth aspect, the first encoder function includes one or more first encoder models.

[0070] In combination with the fifth aspect, in certain implementations of the fifth aspect, the second indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or, the second indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

[0071] In combination with the fifth aspect, in certain implementations of the fifth aspect, obtaining the first interference signal strength includes: receiving configuration information from a network device, the configuration information is used to indicate a first reference signal, the first reference signal including one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; performing channel measurement on the first reference signal according to the configuration information to obtain first interference information, the first interference information being used to indicate the first interference signal strength.

[0072] In combination with the fifth aspect, in certain implementations of the fifth aspect, obtaining the first interference signal strength includes: performing an interference prediction or perception operation to obtain the first interference signal strength.

[0073] In combination with the fifth aspect, in some implementations of the fifth aspect, before obtaining the first interference signal strength, the method further includes: sending a request message to the network device, where the request message is used to request the network device to send configuration information.

[0074] In combination with the fifth aspect, in certain implementations of the fifth aspect, the method also includes: receiving third indication information from the network device, the third indication information being used to indicate that the first device has selected or matched the first decoder model or the first encoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

[0075] In combination with the fifth aspect, in some implementations of the fifth aspect, the method further includes: sending first interference information to the network device, where the first interference information is used to indicate a first interference signal strength.

[0076] In combination with the fifth aspect, in certain implementations of the fifth aspect, the method also includes: receiving a first CSI-RS from a network device; sending a first result to the network device, the first result being obtained by processing the first CSI based on a first encoder model or a first encoder function, the first CSI being obtained by measuring the first CSI-RS, and the first CSI being related to the first interference signal strength.

[0077] In a sixth aspect, a communication method is provided, which can be executed by a network device, or by a chip or circuit of the network device, which is not limited in this application. For ease of description, the following description is based on an example of execution by a network device.

[0078] The method includes: receiving second indication information from a terminal device, the second indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function is used to process channel state information CSI, and the first encoder model or the first encoder function is related to the first interference signal strength.

[0079] In combination with the sixth aspect, in certain implementations of the sixth aspect, the first encoder function includes one or more first encoder models.

[0080] In combination with the sixth aspect, in certain implementations of the sixth aspect, the second indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or, the second indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

[0081] In combination with the sixth aspect, in certain implementations of the sixth aspect, before receiving the second indication information from the terminal device, the method also includes: sending configuration information to the terminal device, the configuration information being used to indicate a first reference signal, the first reference signal including one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; wherein the first interference information is obtained based on a measurement of the first reference signal, and the first interference information is used to indicate the strength of the first interference signal.

[0082] In combination with the sixth aspect, in certain implementations of the sixth aspect, before sending the configuration information to the terminal device, the method further includes: receiving a request message from the terminal device, where the request message is used to request the network device to send the configuration information.

[0083] In combination with the sixth aspect, in certain implementations of the sixth aspect, the method also includes: sending third indication information to the terminal device, the third indication information being used to indicate that the first device has selected or matched the first decoder model or the first decoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

[0084] In combination with the sixth aspect, in some implementations of the sixth aspect, the method further includes: receiving first interference information from a terminal device, where the first interference information is used to indicate a first interference signal strength.

[0085] In combination with the sixth aspect, in certain implementations of the sixth aspect, the method also includes: sending a first CSI-RS to the terminal device; receiving a first result from the terminal device, the first result being obtained by processing the first CSI based on a first encoder model or a first encoder function, and the first CSI being obtained by measuring the first CSI-RS.

[0086] In combination with the sixth aspect, in certain implementations of the sixth aspect, the method also includes: obtaining a second result, the second result being obtained by processing the first result based on a first decoder model or a first decoder function, the first decoder model corresponding to the first encoder model, and the first decoder function corresponding to the first encoder function.

[0087] The beneficial effects of the third to sixth aspects and some of their implementation methods can be referred to the corresponding descriptions of the first or second aspect and will not be repeated here.

[0088] In the seventh aspect, a communication device is provided, which includes: a transceiver unit for receiving first indication information from a second device, the first indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function is used to process channel state information CSI, and the first encoder model or the first encoder function is related to the first interference signal strength.

[0089] The transceiver unit may perform the reception and transmission processing in the aforementioned first aspect, and the processing unit of the communication device may perform other processing except the reception and transmission in the aforementioned first aspect.

[0090] In the eighth aspect, a communication device is provided, which includes: a processing unit for obtaining a first interference signal strength; a transceiver unit for sending first indication information to a first device, the first indication information being used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function being used to process channel state information CSI, and the first encoder model or the first encoder function being related to the first interference signal strength.

[0091] The transceiver unit may perform the reception and transmission processing in the aforementioned second aspect, and the processing unit of the communication device may perform other processing except reception and transmission in the aforementioned second aspect.

[0092] In the ninth aspect, a communication device is provided, which includes: a transceiver unit for receiving first indication information from a network device, the first indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function is used to process channel state information CSI, and the first encoder model or the first encoder function is related to the first interference signal strength.

[0093] The transceiver unit can perform the reception and transmission processing in the aforementioned third aspect, and the processing unit of the communication device can perform other processing except reception and transmission in the aforementioned third aspect.

[0094] In the tenth aspect, a communication device is provided, which includes: a processing unit for obtaining a first interference signal strength; a transceiver unit for sending first indication information to a terminal device, the first indication information being used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function being used to process channel state information CSI, and the first encoder model or the first encoder function being related to the first interference signal strength.

[0095] The transceiver unit can perform the reception and transmission processing in the aforementioned fourth aspect, and the processing unit of the communication device can perform other processing except reception and transmission in the aforementioned fourth aspect.

[0096] In the eleventh aspect, a communication device is provided, which includes: a processing unit for obtaining a first interference signal strength; a transceiver unit for sending second indication information to a network device, the second indication information being used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function being used to process channel state information CSI, and the first encoder model or the first encoder function being related to the first interference signal strength.

[0097] The transceiver unit can perform the reception and transmission processing in the aforementioned fifth aspect, and the processing unit of the communication device can perform other processing except reception and transmission in the aforementioned fifth aspect.

[0098] In the twelfth aspect, a communication device is provided, which includes: a transceiver unit for receiving second indication information from a terminal device, the second indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function is used to process channel state information CSI, and the first encoder model or the first encoder function is related to the first interference signal strength.

[0099] The transceiver unit can perform the reception and transmission processing in the aforementioned fourth aspect, and the processing unit of the communication device can perform other processing except reception and transmission in the aforementioned sixth aspect.

[0100] In a thirteenth aspect, a communication device is provided, comprising a processing circuit for executing a computer program so that the device executes the method in the above-mentioned first to sixth aspects and any possible implementation thereof.

[0101] Optionally, the processing circuit is one or more processors, or all or part of the circuits in one or more processors used for processing functions.

[0102] Optionally, the communication device further includes a memory, which is used to store the computer program, and the memory is one or more.

[0103] Optionally, the memory may be integrated with the processor, or the memory may be set separately from the processor, or the memory may be located within the processor.

[0104] Optionally, the communication device further includes a transceiver circuit, such as a transceiver or an input-output circuit.

[0105] In a fourteenth aspect, a communication system is provided, comprising: a terminal device and a network device, wherein the terminal device is configured to perform the method of the third or fifth aspect and any possible implementation thereof, and the network device is configured to perform the method of the fourth or sixth aspect and any possible implementation thereof. It should be understood that in this implementation, the first encoder model may be deployed on the terminal device, and the first decoder model may be deployed on the network device.

[0106] Optionally, if the first encoder model is deployed in network element #1 and the first decoder model is deployed in network element #2, the communication system may further include network element #1 and network element #2.

[0107] In a fifteenth aspect, a communication system is provided, comprising: a first apparatus and a second apparatus, wherein the second apparatus is configured to perform the method of the first aspect and any possible implementation thereof, and the first apparatus is configured to perform the method of the first aspect and any possible implementation thereof. It should be understood that in this implementation, the first encoder model may be deployed in the first apparatus, and the first decoder model may be deployed in the second apparatus.

[0108] Optionally, if the first encoder model is deployed in network element #1 and the first decoder model is deployed in network element #2, the communication system may further include network element #1 and network element #2.

[0109] In the sixteenth aspect, a computer-readable storage medium is provided, which stores a computer program or code. When the computer program or code is run on a computer, the computer executes the method in the above-mentioned first to sixth aspects and any possible implementation thereof.

[0110] In the seventeenth aspect, a chip is provided, comprising at least one processing circuit, which is used to run a computer program so that the chip executes the method in the above-mentioned first to sixth aspects and any possible implementation thereof.

[0111] The chip may include an output circuit or interface for sending information or data, and an input circuit or interface for receiving information or data.

[0112] In the eighteenth aspect, a computer program product is provided, comprising: a computer program code, which, when the computer program code is run on the computer, executes the method in the above-mentioned first to sixth aspects and any possible implementation thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] 1 and 2 are schematic diagrams of a communication system applicable to an embodiment of the present application;

[0114] FIG3 is a schematic block diagram of an autoencoder;

[0115] FIG4 is a schematic diagram of an AI application framework;

[0116] FIG5 is a schematic interaction flow chart of a first communication method provided in an embodiment of the present application;

[0117] FIG6 is a schematic interaction flow chart of a second communication method provided in an embodiment of the present application;

[0118] FIG7 is a schematic interaction flow chart of a third communication method provided in an embodiment of the present application;

[0119] FIG8 is a schematic interaction flow chart of a fourth communication method provided in an embodiment of the present application;

[0120] FIG9 is a schematic interaction flow chart of a fifth communication method provided in an embodiment of the present application;

[0121] FIG10 is a schematic block diagram of a communication device provided in an embodiment of the present application;

[0122] FIG11 is a schematic block diagram of another communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0123] The technical solution in this application will be described below with reference to the accompanying drawings.

[0124] The technical solutions provided in this application can be applied to various communication systems, such as: fifth generation (5G) or new radio (NR) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area networks (WLAN) systems, satellite communication systems, future communication systems such as sixth generation mobile communication systems, or a fusion system of multiple systems. The technical solutions provided in this application can also be applied to device to device (D2D) communication, vehicle to everything (V2X) communication, machine to machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0125] A device in a communication system can send signals to or receive signals from another device. The signals may include information, signaling, or data. The term "device" may also be replaced by an entity, a network entity, a communication device, a communication module, a node, a communication node, and the like. This application uses devices as examples for description. For example, a communication system may include at least one terminal device and at least one network device. A network device may send downlink signals to a terminal device, and / or a terminal device may send uplink signals to a network device. It is understood that the term "terminal device / network device" in this application may be replaced by a terminal device that performs the corresponding communication method in this application with the network device.

[0126] The terminal devices in the embodiments of the present application include various devices with wireless communication functions, which can be used to connect people, objects, machines, etc. The terminal devices can be widely used in various scenarios, such as: cellular communication, D2D, V2X, peer to peer (P2P), M2M, MTC, IoT, virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery, etc. The terminal device can be a terminal in any of the above scenarios, such as an MTC terminal, an IoT terminal, etc. The terminal device may be a user equipment (UE) of the third generation partnership project (3GPP) standard, a terminal, a fixed device, a mobile station device or a mobile device, a subscriber unit, a handheld device, a vehicle-mounted device, a wearable device, a cellular phone, a smart phone, a session initialization protocol (SIP) phone, a wireless data card, a personal digital assistant (PDA), a computer, a tablet computer, a notebook computer, a wireless modem, a handheld device (handset), a laptop computer, a computer with wireless transceiver function, a smart book, a vehicle, a satellite, a global positioning system (GPS) device, a target tracking device, an aircraft (such as a drone, a helicopter, a multi-copter, a quadcopter, or an airplane), a ship, a remote control device, a smart home device, an industrial device, or a device built into the above-mentioned device (such as a communication module, a modem or a chip in the above-mentioned device), or other processing devices connected to a wireless modem. For the sake of convenience of description, the terminal device will be described below by taking the terminal or UE as an example.

[0127] It should be understood that in some scenarios, a UE can also be used to act as a base station. For example, a UE can act as a scheduling entity that provides sidelink signals between UEs in scenarios such as V2X, D2D, or P2P.

[0128] In the embodiments of the present application, the device for implementing the function of the terminal device can be the terminal device, or it can be a device that can support the terminal device to implement the function, such as a chip system or chip, which can be installed in the terminal device. In the embodiments of the present application, the chip system can be composed of a chip, or it can include a chip and other discrete devices.

[0129] The network device in the embodiments of the present application may be a device for communicating with a terminal device, and the network device may include an access network device or a radio access network device, such as a base station. The access network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects the terminal device to a wireless network. Base station can broadly cover various names as follows, or replace the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmission point (TRP), transmission point (TP), master station, auxiliary station, multi-standard radio (motor slide retainer, MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The base station can also refer to a communication module, modem or chip used to be set in the aforementioned device or apparatus. The base station can also be a mobile switching center and a device that performs base station functions in D2D, V2X, and M2M communications, a network-side device in a 6G network, or a device that performs base station functions in future communication systems. The base station can support networks with the same or different access technologies. The embodiments of this application do not limit the specific technology and specific device form used by the network equipment.

[0130] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0131] In some deployments, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes implementing portions of the base station's functionality. For example, a RAN node can be a CU, DU, central unit-control plane (CU-CP), central unit-user plane (CU-UP), or RU. The CU and DU can be separate or included in the same network element, such as a BBU. A radio unit (RU) can be included in a radio frequency device or radio frequency unit, such as an RRU, AAU, or RRH.

[0132] The RAN node may support one or more types of fronthaul interfaces, with different fronthaul interfaces corresponding to DUs and RUs with different functions. If the fronthaul interface between the DU and the RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and the RU is another type of interface, relative to the CPRI, some of the downlink and / or uplink baseband functions, such as precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix (CP) for downlink, are moved from the DU to the RU for implementation; and for uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / cyclic prefix (CP) removal, are moved from the DU to the RU for implementation. In one possible implementation, the interface may be an enhanced common public radio interface (eCPRI). In the eCPRI architecture, the division between the DU and RU is different, corresponding to different types (category, Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

[0133] Taking eCPRI Cat A as an example, for downlink transmission, based on layer mapping, the DU is configured to implement layer mapping and one or more functions preceding it (i.e., coding, rate matching, scrambling, modulation, or one or more of layer mapping). Other functions after layer mapping (e.g., resource element (RE) mapping, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition) are moved to the RU for implementation. For uplink transmission, based on RE demapping, the DU is configured to implement demapping and one or more functions preceding it (i.e., decoding, derate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, or one or more of RE demapping). Other functions after demapping (e.g., digital BF or one or more of fast Fourier transform (FFT) / CP removal) are moved to the RU for implementation. It is understandable that for the functional description of DU and RU corresponding to various types of eCPRI, reference can be made to the eCPRI protocol, which will not be described in detail here.

[0134] In one possible design, the processing unit for implementing baseband functions in the BBU is called a baseband high layer (BBH) unit, and the processing unit for implementing baseband functions in the RRU / AAU / RRH is called a baseband low layer (BBL) unit.

[0135] 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, the radio access network may also be an open radio access network (O-RAN / ORAN) architecture. In the 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. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0136] In the embodiments of the present application, the device for implementing the function of the network device can be the network device, or it can be a device that can support the network device to implement the function, such as a chip system or chip, which can be installed in the network device. In the embodiments of the present application, the chip system can be composed of a chip, or it can include a chip and other discrete devices.

[0137] Network devices and terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons and satellites in the air. The embodiments of this application do not limit the scenarios in which network devices and terminal devices are located. In addition, terminal devices and network devices can be hardware devices, or they can be software functions running on dedicated hardware, software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities including dedicated or general-purpose hardware devices and software functions. This application does not limit the specific forms of terminal devices and network devices.

[0138] Network equipment may also include core network equipment, such as access and mobility management function (AMF), or operations, administration and maintenance equipment (OAM), or third-party equipment, such as over the top (OTT) equipment or cloud servers, or equipment equipped with an AI module, such as a RAN intelligent controller (RIC).

[0139] In the embodiment of the present application, the terminal device may be a terminal device or a component of the terminal device (such as a chip or circuit).

[0140] Optionally, the network device may be a network device provided with one or more AI modules. For example, the network device may be a core network device, an access network node (RAN node), or one or more devices in OAM. For example, the AI ​​module may be a RAN intelligent controller (RIC), such as a near real-time RIC or a non-real-time RIC. For example, a near real-time RIC is provided in a RAN node (e.g., a CU or DU), and a non-real-time RIC is provided in an OAM, a cloud server, a core network device, or other network devices.

[0141] First, a communication system applicable to the embodiments of the present application is briefly introduced as follows.

[0142] Figure 1 is a schematic diagram of a wireless communication system 100 applicable to an embodiment of the present application. As shown in Figure 1, the wireless communication system includes a wireless access network 100. The wireless access network 100 can be a next-generation (e.g., 6G or higher) wireless access network, or a traditional (e.g., 5G, 4G, 3G, or 2G) wireless access network. One or more terminal devices (120a-120j, collectively referred to as 120) can be connected to each other or to one or more network devices (110a, 110b, collectively referred to as 110) in the wireless access network 100. Figure 1 is only a schematic diagram, and the wireless communication system may also include other devices, such as core network devices, wireless relay devices, and / or wireless backhaul devices, which are not shown in Figure 1.

[0143] In practical applications, the wireless communication system may include multiple network devices and multiple terminal devices simultaneously, without limitation. A network device may serve one or more terminal devices simultaneously. A terminal device may also access one or more network devices simultaneously. The embodiments of the present application do not limit the number of terminal devices and network devices included in the wireless communication system.

[0144] Figure 2 is a schematic diagram of a wireless communication system 200 applicable to an embodiment of the present application. As shown in Figure 2, the wireless communication system 200 may include at least one network device, such as the network device 210 shown in Figure 2. The wireless communication system 200 may also include at least one terminal device, such as the terminal device 220 and the terminal device 230 shown in Figure 2. The wireless communication system 200 may also include an AI network element (also known as an AI entity), such as the AI ​​network element 240 shown in Figure 2, for performing AI-related operations, such as constructing a training data set or training an AI model.

[0145] In one possible implementation, the network device 210 may send data related to the training of the AI ​​model to the AI ​​network element 240, which constructs a training data set and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. The AI ​​network element 240 may send the results of the operations related to the AI ​​model to the network device 210, and forward them to the terminal device through the network device 210. For example, the results of the operations related to the AI ​​model may include at least one of the following: an AI model that has completed training, an evaluation result or a test result of the model, etc. Exemplarily, a portion of the trained AI model may be deployed on the network device 210, and another portion may be deployed on the terminal device. Alternatively, the trained AI model may be deployed on the network device 210. Alternatively, the trained AI model may be deployed on the terminal device.

[0146] It should be understood that Figure 2 illustrates only the example of a direct connection between AI network element 240 and network device 210. In other scenarios, AI network element 240 may also be connected to a terminal device. Alternatively, AI network element 240 may be connected to both network device 210 and a terminal device simultaneously. Alternatively, AI network element 240 may be connected to network device 210 through a third-party network element. This embodiment of the present application does not limit the connection relationship between the AI ​​network element and other network elements.

[0147] The AI ​​network element 240 may also be provided as a module in a network device and / or a terminal device, for example, in the network device 110b or the terminal device shown in FIG1 .

[0148] Optionally, the AI ​​network element 240 and the network device 210 may be different modules of the same device, or may be separate different devices. It should be noted that Figures 1 and 2 are simplified schematic diagrams for ease of understanding. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 1 and 2. In actual applications, the communication system may include multiple network devices and may also include multiple terminal devices. The embodiments of the present application do not limit the number of network devices and terminal devices included in the communication system.

[0149] To facilitate understanding of the embodiments of the present application, the terms involved in the embodiments of the present application are briefly explained below.

[0150] (1) AI model;

[0151] An AI model is an algorithm or computer program that can implement AI functions. An AI model represents the mapping relationship between the model's input and output. In other words, an AI model is a function model that maps inputs of a certain dimension to outputs of a certain dimension. The parameters of the function model can be obtained through machine learning training. For example, f(x) = mx 2 +n is a quadratic function model, which can be regarded as an AI model, and m and n are parameters of the AI ​​model, which can be obtained through machine learning training. For example, the AI ​​models mentioned in the embodiments below are not limited to neural networks, linear regression models, decision tree models, support vector machines (SVMs), Bayesian networks, Q learning models, or other machine learning (ML) models.

[0152] It is understood that the AI ​​model can be implemented as a hardware circuit, software, or a combination of software and hardware, without limitation. Non-limiting examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application, or software application.

[0153] (2) Machine learning (ML);

[0154] ML is an implementation of artificial intelligence. Machine learning is a method that empowers machines to learn, enabling them to perform tasks that cannot be accomplished through direct programming. In practical terms, machine learning utilizes data to train models and then uses these models to make predictions. There are many machine learning methods, such as neural networks (NNs), decision trees, and support vector machines. Machine learning theory primarily focuses on the design and analysis of algorithms that enable computers to learn automatically. Machine learning algorithms automatically analyze data to identify patterns and use these patterns to make predictions about unknown data.

[0155] (3) Neural network (NN);

[0156] Neural networks are a specific implementation of AI or machine learning. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, giving them the ability to learn arbitrary mappings.

[0157] A neural network can be composed of neural units, which can be a computational unit that takes xs and an intercept 1 as input. A neural network is formed by connecting many of these single neural units, meaning that the output of one neural unit can be the input of another. The input of each neural unit can be connected to the local receptive field of the previous layer to extract features from that local receptive field, which can be an area consisting of several neural units.

[0158] Taking the AI ​​model type as a neural network as an example, the AI ​​model involved in this application can be a deep neural network (DNN). Depending on the network construction method, DNN can include feedforward neural networks (FNN), convolutional neural networks (CNN), and recurrent neural networks (RNN).

[0159] (4) Auto-encoders (AE);

[0160] An autoencoder (AE) is a neural network for unsupervised learning. Its characteristic is that it uses input data as label data, so AE can also be understood as a neural network for self-supervised learning. AE can be used for data compression and recovery. For example, the encoder in AE can compress (encode) data A to obtain data B, and the decoder in AE can decompress (decode) data B to recover data A. Alternatively, it can be understood that the decoder is the inverse operation of the encoder.

[0161] For example, the AI ​​model in the embodiments of the present application may include an encoder and a decoder. The encoder and decoder are used in combination, and it can be understood that the encoder and decoder are a matching AI model. The encoder and decoder can be deployed on a terminal device and a network device, respectively. Optionally, the AI ​​model in the embodiments of the present application can be a single-ended model, which can be deployed on a terminal device or a network device.

[0162] (5) Two-side model;

[0163] The two-end model can also be called a bilateral model, a collaborative model, or a dual model. A two-end model is a model composed of multiple sub-models. The sub-models that make up the model must match each other. These sub-models can be deployed on different nodes.

[0164] The embodiments of the present application relate to an encoder for compressing CSI and a decoder for recovering compressed CSI. The encoder and decoder are used in combination, and it can be understood that the encoder and decoder are matching AI models. An encoder may include one or more AI models, and the decoder matched with the encoder also includes one or more AI models. The number of AI models included in the matching encoder and decoder is the same and corresponds one to one.

[0165] In one possible design, a set of matched encoders and decoders can be specifically two parts of the same AE. As shown in Figure 3, the encoder and decoder are deployed on different nodes respectively. The AE model is a typical bilateral model. The encoder and decoder of the AE model are usually trained together and can be used in matching ways. For example, the encoder can process the input V to obtain the processed result z, and the decoder can decode the encoder output z into the desired output V'. In other words, CSI feedback can be implemented based on the AI ​​model of AE. For example, the UE side compresses and quantizes the CSI through the encoder, and the base station recovers the CSI through the decoder. For the base station, the input of the model is the CSI fed back by the UE, and the output is the recovered CSI. The training of the model requires the CSI fed back by the UE as the true value label of the recovered CSI.

[0166] (6) Training data set and inference data;

[0167] In the field of machine learning, ground truth usually refers to data that is believed to be accurate or real.

[0168] A training dataset is used to train an AI model. It may include the input to the AI ​​model, or the input and target output of the AI ​​model. A training dataset includes one or more training data. Training data may include training samples input to the AI ​​model, or the target output of the AI ​​model. The target output may also be referred to as a label, sample label, or labeled sample. A label is the true value.

[0169] In the communications field, training datasets can include simulated data collected through simulation platforms, experimental data collected in experimental scenarios, or measured data collected in actual communication networks. Because the geographical environments and channel conditions in which data are generated vary, such as indoor and outdoor locations, mobile speeds, frequency bands, or antenna configurations, the collected data can be categorized during acquisition. For example, data with the same channel propagation environment and antenna configuration can be grouped together.

[0170] Model training essentially involves learning certain characteristics from training data. When training an AI model (such as a neural network), the goal is to ensure that the model's output is as close as possible to the desired predicted value. This is done by comparing the network's predictions with the desired target values. The weight vectors of each layer of the AI ​​model are then updated based on the difference between the two. (Of course, before the first update, there's usually an initialization process, which pre-configures the parameters for each layer of the AI ​​model.) For example, if the network's prediction is too high, the weight vectors are adjusted to predict a lower value. This adjustment is repeated until the AI ​​model predicts the desired target value, or a value very close to it. Therefore, it's necessary to predefine how to compare the difference between the predicted and target values. This is known as the loss function, or objective function. These are important equations used to measure the difference between the predicted and target values. For example, a higher loss function indicates a greater difference. Therefore, training an AI model becomes a process of minimizing this loss, keeping the loss function below a threshold or ensuring that the loss function meets the target requirement. For example, the AI ​​model is a neural network, and adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers, width, weights of neurons, or parameters in the activation function of neurons of the neural network.

[0171] Inference data can be used as input to a trained AI model for inference. During the inference process, the inference data is input into the AI ​​model, and the corresponding output is the inference result.

[0172] The design of an AI model primarily involves data collection (e.g., collecting training data and / or inference data), model training, and model inference. Furthermore, it can also include the application of inference results.

[0173] Figure 4 shows an AI application framework.

[0174] In the aforementioned data collection phase, the data source is used to provide training datasets and inference data. In the model training phase, an AI model is obtained by analyzing or training the training data provided by the data source. The AI ​​model represents the mapping relationship between the model's input and output. Learning the AI ​​model through the model training node is equivalent to learning the mapping relationship between the model's input and output using the training data. In the model inference phase, the AI ​​model trained in the model training phase is used to perform inference based on the inference data provided by the data source, obtaining an inference result. This phase can also be understood as inputting the inference data into the AI ​​model and obtaining an output from the AI ​​model, which is the inference result. The inference result can indicate the configuration parameters used (executed) by the execution object and / or the operations performed by the execution object. In the inference result application phase, the inference result is published. For example, the inference result can be centrally planned by the execution (actor) entity, for example, the execution entity can send the inference result to one or more execution objects (e.g., access network equipment or terminal devices) for execution. Alternatively, the execution entity can provide feedback on the model's performance to the data source to facilitate subsequent model update and training.

[0175] It is understandable that a communication system may include network elements with artificial intelligence capabilities. The aforementioned AI model design-related steps may be performed by one or more network elements with artificial intelligence capabilities. In one possible design, AI functions (such as AI modules or AI entities) may be configured within existing network elements in the communication system to implement AI-related operations, such as AI model training and / or inference. For example, the existing network element may be an access network device or a terminal device. Alternatively, in another possible design, an independent network element may be introduced into the communication system to perform AI-related operations, such as training an AI model. The independent network element may be referred to as an AI network element (or AI node, AI entity), etc., and the embodiments of the present application are not limited to these names. For example, the AI ​​network element may be directly connected to an access network device in the communication system, or indirectly connected to the access network device through a third-party network element. The third-party network element may be a network device such as an authentication management function (AMF) network element, a user plane function (UPF) network element, an operation and maintenance management (OAM), a server (such as a cloud server), or other network element, without limitation. Exemplarily, the independent AI network element can be deployed on one or more of the following: the access network device side, the terminal device side, or the core network side. Alternatively, it can be deployed on a server, such as a cloud server, or an over-the-top (OTT) device. For example, the AI ​​network element 240 in the communication system shown in FIG2 is shown.

[0176] The training process of different models can be deployed in different devices or nodes, or in the same device or node. The inference process of different models can be deployed in different devices or nodes, or in the same device or node. Taking the completion of the model training phase of a terminal device as an example, the terminal device can train the matching encoder and decoder, and then send the model parameters of the decoder to the network device. Taking the completion of the model training phase of a network device as an example, after the network device trains the matching encoder and decoder, it can indicate the model parameters of the encoder to the terminal device. Taking the completion of the model training phase of an independent AI network element as an example, the AI ​​network element can train the matching encoder and decoder, and then send the model parameters of the encoder to the terminal device and the model parameters of the decoder to the network device. Then, the model inference phase corresponding to the encoder is performed in the terminal device, and the model inference phase corresponding to the decoder is performed in the network device.

[0177] Among them, the model parameters may include one or more of the following structural parameters of the model (such as the number of layers and / or weights of the model, etc.), the input parameters of the model (such as input dimension, number of input ports), or the output parameters of the model (such as output dimension, number of output ports). It can be understood that the input dimension may refer to the size of an input data. For example, when the input data is a sequence, the input dimension corresponding to the sequence may indicate the length of the sequence. The number of input ports may refer to the number of input data. Similarly, the output dimension may refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence may indicate the length of the sequence. The number of output ports may refer to the number of output data.

[0178] (7) Channel information;

[0179] In a communication system (for example, an LTE communication system or an NR communication system, etc.), the network device needs to determine the resources, MCS, and precoding configurations of the downlink data channel of the scheduling terminal device based on channel information. It can be understood that channel information can reflect channel characteristics, channel quality, etc. Channel information can also be referred to as channel state information (CSI) or channel environment information. It can be understood that the CSI in this application is not limited to traditional CSI, such as one or more of channel quality indication (CQI), precoding matrix indicator (PMI), rank indicator (RI), and CSI-RS resource indicator (CRI), but can also be channel response information, such as a channel response matrix, or reference signal receiving power (RSRP), or signal to interference plus noise ratio (SINR).

[0180] CSI measurement refers to the receiver determining the channel information based on the reference signal sent by the transmitter, i.e., estimating the channel information using a channel estimation method. For example, the reference signal may include at least one of the following: a channel state information reference signal (CSI-RS), a synchronizing signal / physical broadcast channel block (SSB), a sounding reference signal (SRS), or a demodulation reference signal (DMRS). CSI-RS, SSB, and DMRS can be used to measure downlink CSI, while SRS and DMRS can be used to measure uplink CSI.

[0181] In FDD communication scenarios, because uplink and downlink channels lack reciprocity, or cannot be guaranteed, network equipment typically sends downlink reference signals to terminal devices. The terminal devices then perform channel and interference measurements based on the received downlink reference signals to estimate downlink CSI. The terminal devices then generate CSI reports based on a protocol-defined method or a network device configuration, and feed these reports back to the network device, enabling it to obtain downlink CSI.

[0182] Exemplarily, CSI may include at least one of the following: channel quality indication (CQI), precoding matrix indicator (PMI), rank indicator (RI), CSI-RS resource indicator (CRI), layer indicator (LI), reference signal receiving power (RSRP), or signal to interference plus noise ratio (SINR), etc. The signal to interference plus noise ratio may also be called signal to interference plus noise ratio. Among them, RI is used to indicate the number of layers of downlink transmission recommended by the terminal device, CQI is used to indicate the modulation and coding mode supported by the current channel conditions determined by the terminal device, and PMI is used to indicate the precoding recommended by the terminal device. The number of precoding layers indicated by PMI corresponds to RI.

[0183] It should be understood that the RI, CQI, and PMI indicated in the above CSI report are only recommended values ​​for the terminal device, and the network device may perform downlink transmission according to part or all of the information indicated in the CSI report. Alternatively, the network device may not perform downlink transmission according to the information indicated in the CSI report.

[0184] For example, the introduction of AI technology into wireless communication networks has resulted in a CSI feedback method based on an AI model. Terminal devices use the AI ​​model to compress and feedback CSI, and network devices use the AI ​​model to recover the compressed CSI. In AI-based CSI feedback, the terminal device transmits a sequence (e.g., a bit sequence), which reduces CSI overhead compared to traditional CSI feedback methods.

[0185] Taking Figure 3 as an example, the encoder in Figure 3 can be called a CSI generator, and the decoder can be called a CSI reconstructor. For example, the encoder can be deployed in a terminal device, and the decoder can be deployed in a network device. The terminal device can use the encoder to generate CSI feedback information z from the original CSI information V. The terminal device reports a CSI report, which can include the CSI feedback information z. The network device can reconstruct the CSI information through the decoder, thereby obtaining the recovered CSI information V'.

[0186] The CSI original information V may be obtained by the terminal device through CSI measurement. For example, the CSI original information V may include the channel response of the downlink channel or the eigenvector matrix (a matrix composed of eigenvectors) of the downlink channel. The encoder processes the eigenvector matrix of the downlink channel to obtain CSI feedback information z. In other words, the compression and / or quantization operation of the eigenmatrix according to the codebook in the related scheme is replaced by the operation of processing the eigenmatrix by the encoder to obtain CSI feedback information z. The terminal device reports the CSI feedback information z. The network device processes the CSI feedback information z through the decoder to obtain CSI recovery information V'.

[0187] Below, the training process and reasoning process of the AI ​​model in the embodiment of the present application are further illustrated.

[0188] The training data used to train AI models includes training samples and sample labels. For example, the training samples are channel information determined by the terminal device, and the sample labels are the actual channel information, i.e., the true value CSI. If the encoder and decoder belong to the same autoencoder, the training data can only include the training samples, or the training samples are the sample labels.

[0189] In the field of wireless communications, true CSI can be understood as high-precision CSI. The specific training process includes: the model training node uses the encoder to process the channel information, that is, the training sample, to obtain CSI feedback information, and uses the decoder to process the feedback information to obtain the recovered channel information, that is, the CSI recovery information, and then calculates the difference between the CSI recovery information and the corresponding sample label, that is, the value of the loss function, and updates the parameters of the encoder and decoder according to the value of the loss function, so that the difference between the recovered channel information and the corresponding sample label is minimized, that is, the loss function is minimized. Exemplarily, the loss function can be the minimum mean square error (MSE) or cosine similarity. Repeat the above operations to obtain an encoder and decoder that meet the target requirements. The above-mentioned model training node can be a terminal device, a network device or other network elements with AI functions in a communication system.

[0190] It should be understood that the above description uses the AI ​​model for CSI compression as an example. The AI ​​model can also be used in other scenarios within CSI feedback. For example, the AI ​​model can be used for CSI prediction, i.e., predicting channel information at one or more future moments based on channel information measured at one or more historical moments. The embodiments of this application do not limit the specific use of the AI ​​model in CSI feedback scenarios.

[0191] Model identification is to enable network devices and UEs to have a common understanding of an AI model. Typically, the UE needs to register the model with the network device. That is, the UE informs the network device that it has an AI model, as well as the AI ​​model's functions, application scenarios, corresponding configurations, and other information. The network device configures a model identifier (ID) for the UE. The UE and the network device then have a consistent understanding of which AI model the model ID refers to. In the subsequent model management process, the model ID can be used to indicate the specific model corresponding to the model ID.

[0192] Function identification is to enable network equipment and UE to have a common understanding of an AI function. AI function usually does not specifically refer to a specific AI model, but refers to a class of AI models with the same function, that is, AI function can correspond to multiple AI models. Normally, the UE needs to report to the network device the AI ​​functions supported by the UE, as well as the application scenarios of each AI function, the corresponding configuration and other information. The network device can configure an AI function ID for the UE, so that the UE and the network device have a consistent understanding of which AI function the function ID refers to. In the subsequent AI function management process, the function ID can be used to indicate the specific AI function corresponding to the function ID. Alternatively, the network device can use the application scenarios and / or corresponding configurations corresponding to the AI ​​function to manage the AI ​​function.

[0193] The study found that when a dual-end model is selected for CSI feedback using model ID or function ID, the network performance corresponding to the same AI model or AI function is poor at certain times, and thus good CSI feedback performance cannot be obtained.

[0194] Based on this, an embodiment of the present application provides a communication method and a communication device, which select and match models or functions while considering the strength of interference signals, in order to improve CSI feedback performance.

[0195] The communication method provided by the embodiment of the present application will be described in detail below with reference to the accompanying drawings. The embodiments provided in the present application can be applied to the communication system shown in Figure 1 or Figure 2 above, without limitation. The present application proposes the following methods shown in Figures 5 to 9. It should be understood that the method embodiments shown in Figures 5 to 9 can be combined with each other, and the steps in the method embodiments shown in Figures 5 to 9 can be referenced to each other. In the embodiments of the present application, the method embodiments shown in Figures 6 to 9 can be regarded as possible implementation methods for realizing the functions of the method embodiment shown in Figure 5.

[0196] Figure 5 is a flow chart of a communication method 500 provided in an embodiment of the present application. As shown in Figure 5, the method flow can be executed by a first device and a second device. The first device or the second device can be a terminal device or a network device, or a chip, a chip system or a circuit in a terminal device or a network device, or a functional module / or device in a terminal device or a network device that can call and execute a program. Optionally, the first device or the second device can also be an AI entity (also called an AI network element), such as a model training network element, a model storage network element, or a model reasoning network element, such as OAM, OTT, or a cloud server. For ease of description, the embodiments of the present application are respectively described by taking the first device as a terminal device and the second device as a network device (case one); or the first device as a network device and the second device as a terminal device (case two) as an example. The method includes the following steps.

[0197] Case 1: The first device is a terminal device, and the second device is a network device.

[0198] S510: The second device obtains the first interference signal strength.

[0199] Exemplarily, the first interference signal strength can be characterized by the first interference information. For example, the first interference signal strength can be represented by the value of the interference power, in decibel milliwatts (decibel relative to one milliwatt, dBm), or the first interference signal strength can be represented by the value of the signal to interference plus noise ratio SINR, in decibel milliwatts (decibel, dB). In the present application, the first interference information can be a specific interference power value, such as 8dBm, or the first interference information can be a specific SINR value, such as 8dB. Therefore, unless otherwise emphasized, the first interference information in the embodiment of the present application can be replaced by the first interference power value or the first SINR value. Optionally, the first interference information can also be represented by the signal to interference plus noise ratio (SNR), referred to as the signal to noise ratio, which is used to indicate the ratio of the strength of the useful signal to the strength of the interference signal (noise and interference), that is, the interference level.

[0200] In the first example, the second device obtains the first interference signal strength from the first device. For example, the first device sends first interference information to the second device, where the first interference information is used to represent the first interference signal strength. Correspondingly, the second device receives the first interference information from the first device.

[0201] It should be understood that the first device sending the first interference information to the second device may be the first device sending the first interference information itself to the second device, or the first device sending an identifier or index information corresponding to the first interference information to the second device. This application does not specifically limit the method of sending the first interference information, as long as the second device can obtain the first interference signal strength.

[0202] Optionally, before the second device receives the first interference information from the first device, or before the first device sends the first interference information to the second device, the method further includes: the second device may send configuration information to the first device, where the configuration information is used to indicate the first reference signal. Accordingly, the first device receives the configuration information from the second device and performs channel measurement on the first reference signal to obtain a channel measurement result, where the channel measurement result includes the first interference information, i.e., the first interference information is obtained based on the channel measurement of the first reference signal.

[0203] Exemplarily, the configuration information may include one or more of the following: a CSI resource configuration ID (CSI-ResourceConfigId), a CSI-RS resource set table (CSI-RS-ResourceSetList), or a time domain behavior of CSI measurement (resourceType).

[0204] Exemplarily, the first reference signal includes one or more of the following:

[0205] (1) Channel State Information Reference Signal (CSI-RS);

[0206] Exemplarily, the CSI-RS is used for downlink channel estimation or measurement to obtain CSI, such as RI, CQI, or PMI.

[0207] (2) Zero Power Channel State Information reference signal (ZP CSI-RS);

[0208] It should be understood that on the resources configured with this ZP CSI-RS, the gNB does not send a CSI-RS reference signal and the power is 0.

[0209] (3) Channel State Information Interference Measurement (CSI-IM) signal;

[0210] For example, a resource specifically used to estimate the strength of interference signals, called CSI-IM, is defined in a multiple input multiple output (MIMO) system. CSI-IM is not a downlink reference signal, and its function is to measure interference. The serving base station does not send any signal on the resources configured by CSI-IM. The interference signal measured by the UE on CSI-IM comes from the neighboring cell or the background noise. For example, the UE counts the received interference and noise intensity, and calculates the signal to interference plus noise ratio SINR based on this, thereby determining the block error rate (BLER) corresponding to the SINR. Based on the limit of BLER < 10%, the UE reports the corresponding CQI.

[0211] In a second example, the second device may locally obtain the first interference signal strength. For example, the second device may obtain the first interference information through local retrieval or prediction; or the second device may obtain the first interference information through cloud retrieval or prediction. The first interference information indicates the first interference signal strength.

[0212] Local retrieval or prediction may refer to the second device outputting analysis results in a format defined by the specification through artificial intelligence and big data analysis. The output analysis results generally come in two forms: statistical analysis of historical data and prediction of future data.

[0213] Retrieval means that the second device can search for the interference information of the corresponding time point in the past time period stored through the locally recorded historical data, and use the interference information as the current interference information, that is, the first interference information. For example, the interference information at 10:00 a.m. on January 1, 2023 can be equivalent to the interference information at 10:00 a.m. on January 1, 2022. For example, the interference information stored here can be stored locally on the network device, or it can be stored non-locally, such as in the core network, ORAN, or cloud.

[0214] Prediction means that the second device can predict the interference it is subject to and obtain a predicted value of interference information. The method used for prediction can be that the second device predicts through a neural network trained by artificial intelligence, and the training data involved in the training process can be related data of interference information previously collected by the network device, or it can be related data predicted by the second device using a prediction function obtained by fitting based on big data analysis, wherein the fitted data can be related data of interference information previously collected by the network device; or, the second device can predict its own business load to obtain a predicted value of business load, and then predict the interference information based on the high and low business load. It should be understood that a high business load indicates high interference, and a low business load indicates low interference. For example, the network element predicted here can be a network device, such as a core network device, an ORAN device, or a cloud device.

[0215] S520: The second device sends first indication information to the first device. Correspondingly, the first device receives the first indication information from the second device.

[0216] The first indication information is used to indicate a first encoder model or a first encoder function, which is used to compress or quantize the CSI. This means that the second device can obtain the first encoder model or the first encoder function through the first indication information and then determine the first decoder. In other words, the first indication information can be used to indicate the first encoder, or the first indication information can also be used to indicate the first decoder. That is, in this application, "indicating...encoder..." can be replaced with "indicating...decoder..."

[0217] It should be noted that the first encoder and the first decoder correspond to each other, specifically, the first encoder model corresponds to the first decoder model, or the first encoder function corresponds to the first decoder function, then the model corresponding to the first encoder function corresponds to the model corresponding to the first decoder function. It should be understood that the first encoder model and the first decoder model are usually trained together and can be used in combination with each other. The number of AI models included in the first encoder model and the first decoder model used for matching is the same and corresponds one to one. The first encoder model and the first decoder model can be understood as matching AI models. Among them, the first encoder model can be an encoder for compressing CSI, and the first decoder model can be a decoder for recovering compressed CSI.

[0218] Exemplarily, the first encoder function includes one or more first encoder models. It should be understood that an AI function generally does not specifically refer to a specific AI model, but rather refers to a class of AI models with the same function, that is, an AI function can correspond to multiple AI models. In other words, the first encoder function can correspond to one or more first encoder models with the same function, and the first device and the second device can determine the one or more first encoder models through the first encoder function.

[0219] Exemplarily, the first indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or, the first indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

[0220] The identifier of the first encoder model may be a model ID, the identifier of the first encoder function may be a function ID, and the model parameters of the first encoder model, or the model parameters corresponding to the first encoder function may include one or more of the following:

[0221] (1) Structural parameters of the model;

[0222] For example, the structural parameters of the model include at least one of the following: the number of layers and width of the neural network, the weights of neurons, or parameters in the activation function of neurons, etc.

[0223] (2) Model input parameters;

[0224] For example, model input parameters include input dimension and / or number of input ports. It is understood that the input dimension may refer to the size of an input data item. For example, when the input data item is a sequence, the input dimension corresponding to the sequence may indicate the length of the sequence. The number of input ports may refer to the number of input data items.

[0225] (3) Model output parameters;

[0226] For example, output dimension and / or number of output ports. It is understood that the output dimension may refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence may indicate the length of the sequence. The number of output ports may refer to the number of output data.

[0227] Optionally, before executing the above step S520, the second device obtains model library information.

[0228] In which, the model library information is used to indicate the mapping relationship between multiple encoder models and multiple interference signal strength ranges, the first encoder model belongs to multiple encoder models, or, the model library information is used to indicate the mapping relationship between multiple encoder functions and multiple interference signal strength ranges, the first encoder function belongs to multiple encoder functions, and the first interference signal strength is included in an interference signal strength range among the multiple interference signal strength ranges.

[0229] In one example, the second device obtains model library information from the first device. For example, the first device sends the model library information to the second device, and the second device receives the model library information from the first device. Optionally, the second device can also obtain the model library information from a model training network element, a model storage network element, or a model inference network element, such as an OTT or a cloud server. This application does not limit the specific implementation method for the second device to obtain the model library information.

[0230] In the present application, the first encoder model or the first encoder function is related to the first interference signal strength, which can be understood as: the first encoder model or the first encoder function is determined based on the first interference signal strength. For example, after the second device determines the first interference signal strength in step S510, it can determine the first encoder model corresponding to the first interference signal strength in combination with the model library information; or, after the second device determines the first interference signal strength in step S510, it can determine the first encoder function corresponding to the first interference signal strength in combination with the model library information.

[0231] In the present application, the mapping relationship between multiple encoder models and multiple interference signal strength ranges, or the mapping relationship between multiple encoder functions and multiple interference signal strength ranges can be predefined, or configured or preconfigured through signaling, where predefinition can include predefinition, such as protocol definition, and preconfiguration can be achieved by pre-saving corresponding codes, tables, strings or other methods that can be used to indicate relevant information in the device. This application does not limit its specific implementation method.

[0232] Optionally, the mapping relationship can exist in the form of a table, function, text, or string, such as for storage or transmission.

[0233] In this application, the interference signal strength can be characterized by the value of interference power and / or SINR. It should be understood that the larger the value of the interference power, the greater the signal interference, the smaller the value of the interference power, the smaller the signal interference, the smaller the value of the SINR, the greater the signal interference, and the larger the value of the SINR, the smaller the signal interference. Optionally, the interference signal strength can also be characterized by other parameters, which is not limited in this application.

[0234] Below, the mapping relationship between multiple encoder models indicated by the model library information and multiple interference signal strength ranges is exemplified in the form of a table, wherein different interference signal strength ranges correspond to different encoder models, as shown in Table 1.

[0235] Table 1

[0236] As shown in Table 1, the encoder models include AI CSI feedback encoder model #1, AI CSI feedback encoder model #2, ..., AI CSI feedback encoder model #8, and AI CSI feedback encoder model #9. The interference signal strength corresponding to AI CSI feedback encoder models #1 to #5 is represented by the interference power value, and the corresponding interference power ranges are -20dBm≤x<-10dBm, -10dBm≤x<0dBm, 0dBm≤x<5dBm, 5dBm≤x<10dBm, and 10dBm≤x<15dBm, respectively. The interference signal strength corresponding to AI CSI feedback encoder models #6 to #9 is represented by the SINR value, and the corresponding SINR ranges are -20dB≤x<-10dB, -10dB≤x<0dB, 0dB≤x<5dB, and 5dB≤x<10dB, respectively.

[0237] For example, assuming that the first interference signal strength obtained by the second device in step S510 is 12 dBm, it means that the first interference signal strength belongs to the interference power range of 10 dBm ≤ x < 15 dBm. Correspondingly, it can be determined that the first encoder model is AI CSI feedback encoder model #5. The second device can then carry the model ID and / or model parameters of AI CSI feedback encoder model #5 in the first indication information to indicate AI CSI feedback encoder model #5. For another example, assuming that the first interference signal strength obtained by the second device in step S510 is 4 dB, it means that the first interference signal strength belongs to the SINR range of 0 dB ≤ x < 5 dB. Correspondingly, it can be determined that the first encoder model is AI CSI feedback encoder model #8. The second device can then carry the model ID and / or model parameters of AI CSI feedback encoder model #8 in the first indication information to indicate AI CSI feedback encoder model #8.

[0238] It should be noted that the above Table 1 is only an example given for ease of understanding and does not constitute a limitation on the technical solution of this application.

[0239] Optionally, the present application does not limit the number of encoder models (e.g., AI CSI feedback encoder models) in Table 1, or in other words, the present application does not limit the number of corresponding relationships between encoder models and interference signal strength ranges in Table 1 (e.g., a row in a table).

[0240] For example, the AI ​​CSI feedback encoder model #1 to the AI ​​CSI feedback encoder model #5, and the AI ​​CSI feedback encoder model #6 to the AI ​​CSI feedback encoder model #9 in Table 1 can be independently formed into new tables; for another example, the AI ​​CSI feedback encoder model #1 to the AI ​​CSI feedback encoder model #3, and the AI ​​CSI feedback encoder model #4 and the AI ​​CSI feedback encoder model #5 in Table 1 can also be independently formed into new tables; for another example, the AI ​​CSI feedback encoder model #6 and the AI ​​CSI feedback encoder model #7, and the AI ​​CSI feedback encoder model #8 and the AI ​​CSI feedback encoder model #9 in Table 1 can also be independently formed into new tables, that is, Table 1 can be split into multiple other tables for example, and this application does not limit this, nor does the splitting method limit this.

[0241] Optionally, the present application does not limit the value of the interference signal strength range in Table 1, or in other words, the present application does not specifically limit the interval size corresponding to the interference signal strength range in Table 1, wherein the interval sizes corresponding to multiple interference signal strength ranges can be the same (equally spaced) or different (unequally spaced), and the interval size corresponding to the interference signal strength range and the specific value range can be predefined or preconfigured, or can be configured by signaling.

[0242] For example, in Table 1, the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder model #1 and the AI ​​CSI feedback encoder model #2 is the same, which is 10dBm; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder model #3, the AI ​​CSI feedback encoder model #4 and the AI ​​CSI feedback encoder model #5 is the same, which is 5dBm; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder model #6 and the AI ​​CSI feedback encoder model #7 is the same, which is 10dB; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder model #8 and the AI ​​CSI feedback encoder model #9 is the same, which is 5dB; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder model #1 and the AI ​​CSI feedback encoder model #3 is different, and the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder model #7 and the AI ​​CSI feedback encoder model #8 is different. This application does not limit this.

[0243] Optionally, the interference power range corresponding to the AI ​​CSI feedback encoder model #1 to the AI ​​CSI feedback encoder model #5 in the above Table 1 can be replaced with the first range to the fifth range, and the SINR range corresponding to the AI ​​CSI feedback encoder model #6 to the AI ​​CSI feedback encoder model #9 can be replaced with the sixth range to the ninth range, etc. This application does not limit this.

[0244] It should be noted that since the first encoder model matches the first decoder model, or in other words, multiple first encoder models correspond one-to-one to multiple first decoder models, the mapping relationship between multiple decoder models and multiple interference signal strength ranges can also be uniquely determined according to the above Table 1, as shown in the following Table 2.

[0245] Table 2

[0246] It should be noted that Table 2 is only an example given for ease of understanding and does not constitute a limitation on the technical solution of the present application. Optionally, the above Table 1 and Table 2 can be implemented independently or in combination. For example, the above Table 1 and Table 2 can be combined into one table, and the present application does not limit this. For example, for a specific interference signal strength range, one or more rows in Table 1 can be reflected in one table with one or more corresponding rows in Table 2. For example, the mapping relationship of the first 5 rows in Table 1 and the mapping relationship of the first 5 rows in Table 2 can be combined into one table, and the mapping relationship of the last 4 rows in Table 1 and the mapping relationship of the last 4 rows in Table 2 can be combined into one table, and the present application does not limit this.

[0247] Table 3 below illustrates the mapping relationship between multiple encoder functions indicated by the model library information and multiple interference signal strength ranges, where different interference signal strength ranges correspond to different encoder functions.

[0248] Table 3

[0249] As shown in Table 3, the encoder functions include AI CSI feedback encoder function #1, AI CSI feedback encoder function #2, ..., AI CSI feedback encoder function #8, and AI CSI feedback encoder function #9. The interference signal strength corresponding to AI CSI feedback encoder function #1 to AI CSI feedback encoder function #4 is represented by the interference power value, and the corresponding interference power ranges are -20dBm≤x<-10dBm, -10dBm≤x<0dBm, 0dBm≤x<5dBm, and 5dBm≤x<10dBm, respectively. The interference signal strength corresponding to AI CSI feedback encoder function #5 to AI CSI feedback encoder model #9 is represented by the SINR value, and the corresponding SINR ranges are -20dB≤x<-10dB, -10dB≤x<0dB, 0dB≤x<5dB, 5dB≤x<10dB, and 10dB≤x<20dB, respectively.

[0250] For example, assuming that the first interference signal strength obtained by the second device in step S510 is 6dBm, it means that the first interference signal strength belongs to the interference power range of 5dBm≤x<10dBm. Correspondingly, it can be determined that the first encoder function is AI CSI feedback encoder function #4. The second device can then carry the function ID of AI CSI feedback encoder function #4 and / or the model parameters corresponding to AI CSI feedback encoder function #4 in the first indication information to indicate the AI ​​CSI feedback encoder function #4. For another example, assuming that the first interference signal strength obtained by the second device in step S510 is 8dB, it means that the first interference signal strength belongs to the SINR range of 5dB≤x<10dB. Correspondingly, it can be determined that the first encoder function is AI CSI feedback encoder function #8. The second device can then carry the model ID and / or model parameters of AI CSI feedback encoder function #8 in the first indication information to indicate the AI ​​CSI feedback encoder function #8.

[0251] It should be noted that the above Table 3 is only an example given for ease of understanding and does not constitute a limitation on the technical solution of this application.

[0252] Optionally, the present application does not limit the number of encoder functions in Table 3 (for example, AI CSI feedback encoder functions), or in other words, the present application does not limit the number of corresponding relationships between encoder functions and interference signal strength ranges in Table 3 (for example, a row in the table).

[0253] For example, AI CSI feedback encoder function #1 to AI CSI feedback encoder function #4, and AI CSI feedback encoder function #5 to AI CSI feedback encoder function #9 in Table 3 can be independently formed into new tables; alternatively, AI CSI feedback encoder function #1 and AI CSI feedback encoder function #2, and AI CSI feedback encoder function #3 and AI CSI feedback encoder function #4 in Table 3 can also be independently formed into new tables; alternatively, AI CSI feedback encoder function #5 to AI CSI feedback encoder function #7, and AI CSI feedback encoder function #8 and AI CSI feedback encoder function #9 in Table 3 can also be independently formed into new tables. That is, Table 3 can be split into multiple other tables for example, and this application does not limit this, nor does the splitting method limit it.

[0254] Optionally, the present application does not limit the value of the interference signal strength range in Table 3, or in other words, the present application does not specifically limit the interval size corresponding to the interference signal strength range in Table 3, wherein the interval sizes corresponding to multiple interference signal strength ranges can be the same (equally spaced) or different (unequally spaced), and the interval size corresponding to the interference signal strength range and the specific value range can be predefined or preconfigured, or can be configured by signaling.

[0255] For example, in Table 3, the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder function #1 and the AI ​​CSI feedback encoder function #2 is the same, which is 10dBm; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder function #3 and the AI ​​CSI feedback encoder function #4 is the same, which is 5dBm; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder function #1 and the AI ​​CSI feedback encoder function #3 is different; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder function #5, the AI ​​CSI feedback encoder function #6 and the AI ​​CSI feedback encoder function #9 is the same, which is 10dB; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder function #7 and the AI ​​CSI feedback encoder function #8 is the same, which is 5dB; the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder function #1 and the AI ​​CSI feedback encoder function #4 is different, and the interval size of the interference signal strength range corresponding to the AI ​​CSI feedback encoder function #6 and the AI ​​CSI feedback encoder function #8 is different. This application does not limit this.

[0256] Optionally, each AI CSI feedback encoder function may contain one or more AI CSI feedback encoder models.

[0257] Optionally, the interference power range corresponding to the AI ​​CSI feedback encoder function #1 to the AI ​​CSI feedback encoder model #4 in the above Table 3 can be replaced with the first range to the fourth range, and the SINR range corresponding to the AI ​​CSI feedback encoder model #5 to the AI ​​CSI feedback encoder model #9 can be replaced with the fifth range to the ninth range, etc. This application does not limit this.

[0258] It should be noted that since the first encoder function matches the first decoder function, or in other words, multiple first encoder functions correspond one-to-one to multiple first decoder functions, the mapping relationship between multiple decoder functions and multiple interference signal strength ranges can also be uniquely determined according to the above Table 3, as shown in the following Table 4.

[0259] Table 4

[0260] It should be noted that Table 4 is only an example given for ease of understanding and does not constitute a limitation on the technical solution of the present application. Optionally, the above Table 34 and Table 4 can be implemented independently or in combination. For example, the above Table 3 and Table 4 can be combined into one table, and the present application does not limit this. For example, for a specific interference signal strength range, one or more rows in Table 3 can be reflected in one table with one or more corresponding rows in Table 4. For example, the mapping relationship of the first 4 rows in Table 3 and the mapping relationship of the first 4 rows in Table 4 can be combined into one table, and the mapping relationship of the last 5 rows in Table 3 and the mapping relationship of the last 5 rows in Table 4 can be combined into one table, and the present application does not limit this.

[0261] Optionally, the mapping relationship between the multiple encoder models and the multiple interference signal strength ranges shown in Table 1 above, and the mapping relationship between the multiple encoder functions and the multiple interference signal strength ranges shown in Table 3 can be implemented independently or in combination, that is, the above Table 1 and Table 3 can be combined into one table, and this application does not limit this. For example, for a specific interference signal strength range, one or more rows in Table 1 can be reflected in one table with one or more corresponding rows in Table 3, such as the mapping relationship of the first 4 rows in Table 1 and the mapping relationship of the first 4 rows in Table 3 can be combined into one table.

[0262] Similarly, the mapping relationship between the multiple decoder models and the multiple interference signal strength ranges shown in Table 3 above, and the mapping relationship between the multiple decoder functions and the multiple interference signal strength ranges shown in Table 4 can be implemented independently or in combination, that is, the above Table 3 and Table 4 can be combined into one table, and this application does not limit this. For example, for a specific interference signal strength range, one or more rows in Table 3 can be reflected in one table with one or more corresponding rows in Table 4, such as the mapping relationship of the first four rows in Table 3 and the mapping relationship of the first four rows in Table 4 can be combined into one table, and this application does not limit this.

[0263] For example, assuming that the first interference signal strength obtained by the second device in step S510 is -8 dBm, and according to the model library information shown in Table 1 or Table 3, the first interference signal strength falls within the interference power range of -10 dBm ≤ x < 0 dBm, the second device may determine that the first encoder model is AI CSI feedback encoder model #2, or the second device may determine that the first encoder function is AI CSI feedback encoder function #2. Furthermore, in step S520, the second device may indicate the first encoder model or first encoder function via first indication information. For example, the second device may include one or more of the model ID of AI CSI feedback encoder model #2 or the function ID of AI CSI feedback encoder function #2 in the first indication information, indicating the AI ​​CSI feedback encoder model #2 and the AI ​​CSI feedback encoder function #2, respectively. Alternatively, the second device may also include one or more of the model parameters of AI CSI feedback encoder model #2 or the model parameters corresponding to AI CSI feedback encoder function #2 in the first indication information, indicating the AI ​​CSI feedback encoder model #2 and the AI ​​CSI feedback encoder function #2, respectively.

[0264] That is to say, the second device can determine the first encoder model or first encoder function corresponding to the first interference signal strength based on the first interference signal strength obtained in step S510 and the above-mentioned model library information, and indicate the first encoder model or first encoder function to the first device through the first indication information. Subsequently, the first device can feedback CSI to the second device based on the model corresponding to the first encoder model or the first encoder function.

[0265] Optionally, for Tables 1 to 4 above, the mapping relationship between the encoder model and / or encoder function and the interference signal strength range shown in Table 1 and / or Table 2 can be understood as model library information on the terminal device side, used to determine the first encoder model and / or first encoder function based on the first interference information (or first interference signal strength), and the decoder model and / or decoder function shown in Table 3 and / or Table 4 can be understood as model library information on the network device side, used to determine the first decoder model and / or first decoder function based on the first interference information (or first interference signal strength).

[0266] Based on steps S510 and S520 above, the first device and the second device have selected or matched a dual-end model or function related to the first interference signal strength, such as a first encoder model and a first decoder model (which may be referred to as a dual-end model), or a first encoder function or a first decoder function (the model corresponding to the first encoder function and the model corresponding to the first encoder / decoder function may also be referred to as a dual-end model). Optionally, the first device may provide CSI feedback to the second device based on the dual-end model.

[0267] Based on the above steps S510 and S520, after the first device and the second device complete the selection or matching of the dual-end model, the first device and the second device can perform CSI feedback based on the dual-end model, thereby improving CSI feedback performance.

[0268] Exemplarily, the second device sends a first CSI-RS to the first device. Correspondingly, the first device receives the first CSI-RS from the second device and performs channel measurement on the first CSI-RS to obtain the first CSI. The first device can then use the first encoder model determined above or the model corresponding to the first encoder function to compress and quantize the first CSI to obtain feedback CSI (i.e., the first result), and send the feedback CSI to the second device. After receiving the feedback CSI from the first device, the second device can use the first decoder model determined above or the model corresponding to the first decoder function to decompress and quantize the feedback CSI to obtain recovered CSI (i.e., the second result).

[0269] That is to say, the first CSI can be used as the input of the first encoder model, and the output of the first encoder is the feedback CSI (i.e., the first result). Correspondingly, the feedback CSI is used as the input of the first decoder, and the output of the first decoder is the recovered CSI (i.e., the second result).

[0270] It should be understood that the first encoder model corresponds to the first decoder model. The first encoder model and the first decoder model are usually trained together and can be used in combination with each other. The two can be understood as matching AI models. Similarly, the first encoder function corresponds to the first decoder function. For specific interpretations, please refer to the relevant description above.

[0271] In this application, the first CSI is related to the first interference signal strength, which can be understood as follows: after the second device obtains the first interference signal strength, it can send a first CSI-RS within a first time period, so that the first device measures the first CSI-RS to obtain the first CSI and compresses or quantizes the first CSI. The first time period should be as small as possible, such as within 10ms, to ensure that the first encoder model corresponding to the first interference signal strength or the model corresponding to the first encoder function is suitable for compressing or quantizing the first CSI, thereby improving CSI feedback performance.

[0272] Optionally, the first CSI is related to the first interference signal strength, which can also be understood as: the first interference signal strength is obtained based on the measurement amount of the first CSI-RS, for example, the first device measures the measurement amount of the first CSI-RS to obtain the first CSI, wherein the first CSI includes first interference information, and the first interference information is used to indicate the first interference signal strength.

[0273] Optionally, the first encoder model in the embodiments of the present application can be deployed on the first device side, and the first decoder model can be deployed on the second device side. For example, after measuring and obtaining the first CSI, the first device uses the first encoder model to compress the first CSI to obtain feedback CSI, and then sends the feedback CSI to the second device. Correspondingly, after obtaining the feedback CSI, the second device uses the first decoder model to decompress the feedback CSI to obtain recovered CSI, thereby improving CSI feedback performance.

[0274] Optionally, the first encoder model or the first decoder model in the embodiment of the present application may also be deployed on a third device side or a fourth device side, and the third device or the fourth device may be a model training network element, a model storage network element, or a model inference network element, such as an OAM, OTT, or cloud server. For example, after measuring and obtaining the first CSI, the first device may send the first CSI to the third device, and the third device may use the first encoder model to compress the first CSI to obtain feedback CSI, and send the feedback CSI to the first device, and then the first device may send the feedback CSI to the second device. Correspondingly, after obtaining the feedback CSI, the second device may send the feedback CSI to the fourth device, and the fourth device may use the first decoder model to decompress the feedback CSI to obtain recovered CSI, and send the recovered CSI to the second device, so as to improve the CSI feedback performance.

[0275] Case 2: The first device is a network device, and the second device is a terminal device.

[0276] S510: The second device obtains the first interference signal strength.

[0277] The meaning of the first interference signal strength can be referred to the above related description and will not be explained here.

[0278] In the first example, the second device obtains the first interference signal strength from the first device. For example, the first device sends configuration information to the second device. In response, the second device receives the configuration information from the first device and performs a channel measurement on the first reference signal based on the configuration information to obtain a channel measurement result. The channel measurement result includes the first interference information, and the first interference information is used to represent the first interference signal strength. In other words, the first interference information is obtained based on the channel measurement of the first reference signal.

[0279] Among them, the configuration information is used to indicate the first reference signal, and the configuration information includes one or more of the following: CSI resource configuration ID (CSI-ResourceConfigId), CSI-RS resource set table (CSI-RS-ResourceSetList), or CSI measurement time domain behavior (resourceType), and the first reference signal includes one or more of the following: CSI-RS, ZP CSI-RS, or CSI-IM. For the interpretation of the first reference signal and the association between the first interference information and the first interference signal strength, please refer to the above-mentioned relevant description.

[0280] Optionally, the second device obtains the first interference signal strength from the first device, and the first device may proactively indicate the first interference signal strength to the second device, or the first device may indicate the first interference signal strength to the second device based on a request message from the second device. For example, before executing step S510 above, the second device sends a request message to the first device, where the request message is used to request the first device to send configuration information. Correspondingly, after receiving the request message from the second device, the first device triggers the sending of configuration information to the second device, so that the second device performs channel measurement on the first reference signal based on the configuration information to obtain the first interference signal strength.

[0281] In the second example, the second device can locally obtain the first interference signal strength. For example, the second device can obtain the first interference signal strength by performing interference prediction or sensing operations. For example, the second device can output the analysis results in a format defined by the specification through artificial intelligence and big data analysis. The specific implementation method can be found in the relevant description above and will not be repeated here.

[0282] Based on the above example, the second device obtains the first interference signal strength. Optionally, the second device may send the first interference information (or the first interference signal strength) to the first device. Correspondingly, after receiving the first interference information (or the first interference signal strength) from the second device, the first device may know that the first encoder model or the first encoder function indicated by the second device in step S520 is associated with the first interference information (or the first interference signal strength). Furthermore, the first device can also determine the first decoder model that matches the first encoder model, or can determine the first decoder function that matches the first encoder function, for CSI feedback between the first device and the second device. Among them, the specific interpretation of the association between the first encoder model or the first encoder function and the first interference information (or the first interference signal strength) can be referred to the above related description and will not be explained here.

[0283] S520: The second device sends first indication information to the first device. Correspondingly, the first device receives the first indication information from the second device.

[0284] Among them, the first indication information is used to indicate the first encoder model or the first encoder function, and the first encoder model or the first encoder function is used to process CSI, or the first indication information can be used to indicate the first encoder, or the first decoder. The specific meaning can be referred to the relevant description above.

[0285] Exemplarily, the first indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; alternatively, the first indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function. For specific interpretations, please refer to the relevant description above.

[0286] Optionally, before executing the above step S520, the second device obtains model library information.

[0287] The meaning and presentation of the model library information can be found in the above descriptions and will not be explained here.

[0288] Exemplarily, the second device can obtain the model library information from the first device, or the second device can also obtain the model library information from a model training network element, a model storage network element or a model inference network element, such as OAM, OTT, or a cloud server. The specific implementation method can refer to the above-mentioned relevant description.

[0289] In an embodiment of the present application, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function. For specific interpretations, please refer to the above-mentioned relevant descriptions. After executing the above-mentioned step S520, the first device can determine the first encoder model or the first encoder function selected by the second device based on the received first indication information, and then can determine the first decoder model that matches the first encoder model, or can determine the first decoder function that matches the first encoder function. Optionally, the first device can send a second indication information to the second device, and the second indication information is used to indicate the first decoder model or the first encoder function that the first device has selected or matched. At this time, the first device and the second device complete the selection or matching of the dual-end model, and the two can subsequently perform CSI feedback based on the dual-end model.

[0290] Based on the above steps S510 and S520, after the first device and the second device complete the selection or matching of the dual-end model, the first device and the second device can perform CSI feedback based on the dual-end model, thereby improving CSI feedback performance.

[0291] In one example, a first device sends a first CSI-RS to a second device. Correspondingly, the second device receives the first CSI-RS from the first device and performs channel measurement on the first CSI-RS to obtain a first CSI. The second device can then compress and quantize the first CSI using the first encoder model or the model corresponding to the first encoder function determined above to obtain feedback CSI (i.e., a first result), and send the feedback CSI to the second device. After receiving the feedback CSI from the second device, the first device can decompress and quantize the feedback CSI using the first decoder model or the model corresponding to the first decoder function determined above to obtain a recovered CSI (i.e., a second result).

[0292] That is to say, the first CSI can be used as the input of the first encoder model, and the output of the first encoder is the feedback CSI (i.e., the first result). Correspondingly, the feedback CSI is used as the input of the first decoder, and the output of the first decoder is the recovered CSI (i.e., the second result).

[0293] In the present application, the first CSI is related to the first interference signal strength. For specific interpretations, please refer to the above related descriptions and will not be explained here.

[0294] Optionally, the first encoder model in the embodiment of the present application can be deployed on the first device side, the first decoder model can be deployed on the second device side, or the first encoder model or the first decoder model can also be deployed on the third device side. The third device can be a model training network element, a model storage network element, or a model inference network element, such as OAM, OTT or cloud server, etc. This application does not limit this.

[0295] To sum up, the second device obtains the first interference signal strength and indicates the first encoder model or first encoder function related to the first interference signal strength to the first device through the first indication information. At the same time, the second device determines the first decoder model that matches the first encoder model, or determines the first decoder function that matches the first encoder function, that is, selects or matches the dual-end model for CSI feedback taking into account the first interference signal strength, in order to improve the CSI feedback performance.

[0296] Below, with reference to Figures 6 and 7, using the example of a first device being a terminal and a second device being a network device, we illustrate how the second device (e.g., a gNB) triggers the selection or matching of the first encoder model. Figures 6 and 7 can be viewed as further refinements of scenario 1 in Figure 5. The first interference information in Figure 6 is obtained by the first device (e.g., a UE) through channel measurement of the first reference signal. In contrast, the first interference information in Figure 7 is retrieved or predicted locally / in the cloud by the second device (e.g., a gNB). The first interference information is obtained to determine the first encoder model, thereby enabling the selection or matching of the first encoder model and the first decoder.

[0297] Figure 6 is a schematic flow chart of a communication method 600 provided in an embodiment of the present application. As shown in Figure 6, the method can be performed by a first device (e.g., a UE) and a second device (e.g., a gNB) and includes the following steps. Optionally, the first device may be a chip, chip system, or circuit in a terminal device, or a functional module / or device in a terminal device capable of invoking and executing a program. The second device may be a chip, chip system, or circuit in a network device, or a functional module / or device in a network device capable of invoking and executing a program. Optionally, the first device or the second device may be an AI entity (also known as an AI network element) or a chip or storage device for an AI entity, such as a model training network element, a model storage network element, or a model inference network element, such as an OAM, OTT, or cloud server, etc., although this application does not limit this. Optionally, the first device may include a terminal device and an AI entity, and / or the second device may include a network device and an AI entity, although this is not limited herein.

[0298] S610: Optionally, the UE sends model library information to the gNB, and correspondingly, the gNB receives the model library information from the UE.

[0299] The model library information is used to indicate the mapping relationship between multiple encoder models and multiple interference signal strength ranges. For specific interpretations, please refer to the relevant description of the above method 500.

[0300] S620: The gNB sends configuration information to the UE. Correspondingly, the UE receives the configuration information from the gNB.

[0301] The configuration information is used to instruct to perform channel measurement on the first reference signal. For the specific interpretation of the first reference signal and the configuration information and the specific implementation of the channel measurement, reference may be made to the relevant description of the above method 500.

[0302] S630: The UE performs channel measurement on the first reference signal to obtain first interference information.

[0303] Exemplarily, the first interference information is used to indicate the first interference signal strength. For example, the first interference information may be a specific interference power value, such as 8 dBm.

[0304] S640: The UE sends first interference information to the gNB. Correspondingly, the gNB receives the first interference information from the UE.

[0305] S650: The gNB determines a first encoder model based on the first interference information.

[0306] Optionally, if step S610 is performed, the gNB knows the UE's model library information. The gNB can determine the UE's current interference signal strength based on the received first interference information and determine the corresponding first encoder model based on the model library information obtained in step S610. For example, assuming the interference power value reported by the UE is 8 dBm, the gNB can select AI CSI feedback encoder model #3 (i.e., the first encoder model) from Table 1 for subsequent CSI feedback.

[0307] Optionally, if step S610 is not performed, it means that the gNB is unaware of the model library information of the UE. In this case, the gNB may determine the corresponding first decoder model, such as AI CSI feedback decoder model #3, from the gNB model library information in Table 2 based on the received first interference information, and then determine the corresponding first encoder model (e.g., AI CSI feedback encoder model #3) based on the correspondence between the first encoder model and the first decoder model (i.e., the two are dual-end models). The correspondence between the first encoder model and the first decoder model may be predefined, configured, or preconfigured, and this application does not limit this.

[0308] Exemplarily, the AI ​​CSI feedback encoder model #3 and the AI ​​CSI feedback decoder model #3 can be the encoder and decoder shown in Figure 3, respectively. For example, the AI ​​CSI feedback encoder model #3 and the AI ​​CSI feedback decoder model #3 in the embodiment of the present application can be used for CSI compression processing and CSI decompression processing, respectively.

[0309] S660: The gNB sends first indication information to the UE. Correspondingly, the UE receives the first indication information from the gNB.

[0310] The first indication information is used to indicate a first encoder model, such as AI CSI feedback encoder model #3.

[0311] For example, the first indication information carries the model ID of AI CSI feedback encoder model #3 and / or the model parameters of AI CSI feedback encoder model #3. For specific interpretations, refer to the relevant description of the above method 500. Furthermore, after receiving the first indication information, the UE can select AI CSI feedback encoder model #3 from the local model library based on the model ID of AI CSI feedback encoder model #3 and / or the model parameters of AI CSI feedback encoder model #3.

[0312] Optionally, the UE may send feedback information to the gNB indicating that the UE has successfully selected AI CSI feedback encoder model #3. This means that for AI CSI feedback scenarios, the UE and gNB have identified and paired a dual-end model, such as AI CSI feedback encoder model #3 and AI CSI feedback decoder model #3.

[0313] Based on the above steps, after the UE and the gNB complete the selection or matching of the dual-end model, the UE and the gNB can perform CSI feedback based on the dual-end model, for example, see the following steps S670 to S690.

[0314] S670: The gNB sends a first CSI-RS to the UE. Correspondingly, the UE receives the first CSI-RS from the gNB.

[0315] S680: The UE measures the first CSI-RS to obtain the first CSI.

[0316] S690: The UE sends the first result to the gNB. Correspondingly, the gNB receives the first result from the UE.

[0317] In one example, the first encoder model is deployed on the UE side, and the first decoder model is deployed on the gNB side. The UE can compress the first CSI obtained by measuring the first CSI-RS using the first encoder model to obtain feedback CSI (i.e., the first result), and send the first result to the gNB. Correspondingly, after receiving the first result, the gNB decompresses the first result using the first decoder model to obtain recovered CSI (i.e., the second result), completing CSI feedback.

[0318] In another example, the first encoder model is deployed in NE #1 and the first decoder model is deployed in NE #2. The UE can send the first CSI obtained by measuring the first CSI-RS to NE #1. NE #1 compresses the first CSI using the first encoder model to obtain feedback CSI (i.e., the first result) and sends the first result to the UE. The UE then sends the first result to the gNB. Correspondingly, after receiving the first result, the gNB sends the first result to NE #2. NE #2 decompresses the first result using the first decoder model to obtain recovered CSI (i.e., the second result) and sends the second result to the gNB, completing CSI feedback.

[0319] According to the above solution, the gNB triggers a first interference information (or first interference signal strength) measurement process to obtain the first interference information, determines a first encoder model based on the obtained first interference information, and indicates the first encoder model to the UE by sending first indication information. This enables selection and pairing of the first encoder model and the first decoder model for CSI feedback. In other words, the dual-end model is selected or matched while taking the first interference signal strength into consideration, thereby improving CSI feedback performance.

[0320] Figure 7 is a schematic flow chart of a communication method 700 provided in an embodiment of the present application. As shown in Figure 7, the method can be performed by a first device (e.g., a UE) and a second device (e.g., a gNB) and includes the following steps. Optionally, the first device may be a chip, chip system, or circuit in a terminal device, or a functional module / or device in a terminal device capable of invoking and executing a program. The second device may be a chip, chip system, or circuit in a network device, or a functional module / or device in a network device capable of invoking and executing a program. Optionally, the first device or the second device may be an AI entity (also known as an AI network element) or a chip or storage device for an AI entity, such as a model training network element, a model storage network element, or a model inference network element, such as an OAM, OTT, or cloud server, etc., although this application does not limit this. Optionally, the first device may include a terminal device and an AI entity, and / or the second device may include a network device and an AI entity, although this is not limited herein.

[0321] S710: Optionally, the UE sends model library information to the gNB, and correspondingly, the gNB receives the model library information from the UE.

[0322] The content and interpretation of the model library information may refer to the relevant description of step S610 of the above method 600.

[0323] S720: The gNB obtains first interference information through local / cloud retrieval or prediction.

[0324] The content and interpretation of the first interference information, as well as the specific implementation of local / cloud retrieval or prediction to obtain the first interference information may refer to the relevant description of the above method 500.

[0325] S730. The gNB determines a first encoder model based on the first interference information.

[0326] Optionally, if step S710 is performed, the gNB knows the model library information of the UE. At this time, the gNB can determine the current interference signal strength of the UE based on the first interference information obtained by retrieval or prediction reasoning, and determine the corresponding first encoder model according to the model library information in Table 1, such as AI CSI feedback encoder model #3.

[0327] Optionally, if step S710 is not performed, it indicates that the gNB is unaware of the UE's model library information. In this case, the gNB determines the corresponding first decoder model, such as AI CSI feedback decoder model #3, based on the first interference information retrieved or predicted and inferred, and from the model library information in Table 2. Furthermore, based on the correspondence between the first encoder model and the first decoder model (i.e., both are dual-end models), the gNB determines the corresponding first encoder model, such as AI CSI feedback encoder model #3. The correspondence between the first encoder model and the first decoder model may be predefined, configured, or preconfigured, and this application does not limit this.

[0328] Based on the above steps, after the UE and the gNB complete the selection or matching of the dual-end model, the UE and the gNB can perform CSI feedback based on the dual-end model, for example, see the following steps S740 to S770.

[0329] S740: The gNB sends first indication information to the UE. Correspondingly, the UE receives the first indication information from the gNB.

[0330] S750: The gNB sends a first CSI-RS to the UE. Correspondingly, the UE receives the first CSI-RS from the gNB.

[0331] S760: The UE measures the first CSI-RS to obtain the first CSI.

[0332] S770: The UE sends the first result to the gNB. Correspondingly, the gNB receives the first result from the UE.

[0333] The specific implementation of steps S740 to S770 may refer to the relevant description of steps S660 to S690 of the above method 600.

[0334] According to the above solution, the gNB triggers local (or cloud-based) retrieval or prediction to obtain first interference information, determines a first encoder model based on the obtained first interference information, and indicates the first encoder model to the UE by sending first indication information. This enables selection and pairing of the first encoder model and the first decoder model for CSI feedback. Specifically, the dual-end model is selected or matched while taking into account the strength of the first interference signal, thereby improving CSI feedback performance.

[0335] Below, in combination with Figures 8 and 9, taking the first device as a network device and the second device as a terminal device as an example, the selection or matching of the first encoder model triggered by the second device (such as UE) is explained, wherein Figures 8 and 9 can be regarded as further refinements of Case 2 in Figure 5. The first interference information in Figure 8 is obtained by the second device (such as UE) through channel measurement of the first reference signal. In comparison, the first interference information in Figure 9 is obtained by local interference prediction or perception processing of the second device (such as UE). The first encoder model is determined by obtaining the first interference information, and then the selection or matching of the first encoder model and the first decoder is realized.

[0336] Figure 8 is a schematic flow chart of a communication method 800 provided in an embodiment of the present application. As shown in Figure 8, the method can be performed by a second device (e.g., a UE) and a first device (e.g., a gNB) and includes the following steps. Optionally, the second device may be a chip, chip system, or circuit in a terminal device, or a functional module / or device in a terminal device capable of invoking and executing a program. The first device may be a chip, chip system, or circuit in a network device, or a functional module / or device in a network device capable of invoking and executing a program. Optionally, the first device or the second device may be an AI entity or a chip or storage device for an AI entity, such as a model training network element, a model storage network element, or a model inference network element, such as an OAM, OTT, or cloud server, etc., although this application does not limit this. Optionally, the first device may include a network device and an AI entity; and optionally, the second device may include a terminal device and an AI entity, although this is not limited herein.

[0337] S810. Optionally, the UE sends a request message to the gNB. Correspondingly, the gNB receives the request message from the UE.

[0338] The request message is used to request the gNB to send configuration information to the UE.

[0339] S820: The gNB sends configuration information to the UE. Correspondingly, the UE receives the configuration information from the gNB.

[0340] S830: The UE performs channel measurement on the first reference signal to obtain first interference information.

[0341] For the configuration information involved in steps S820 and S830, the content and interpretation of the first interference information, and the specific implementation thereof, reference may be made to the relevant description of steps S620-S630 of the method 600.

[0342] S840: The UE determines a first encoder model according to the first interference information.

[0343] In one example, the UE may determine the first encoder model based on the first interference information and the local model library information. For example, assuming that the first interference signal strength indicated by the first interference information measured by the UE is 8 dBm, the UE may determine the corresponding first encoder model from Table 1 above, such as AI CSI feedback encoder model #3, for subsequent CSI feedback. The meaning and representation of the model library information may refer to the relevant description of the above method 500 and will not be described here.

[0344] S850: The UE sends first indication information to the gNB. Correspondingly, the gNB receives the first indication information from the UE.

[0345] The content and interpretation of the first indication information may refer to the relevant description of the above method 500.

[0346] In one example, after receiving the first indication information, the gNB can determine the first encoder model, such as AI CSI feedback encoder model #3, based on the model ID of the first encoder model and / or the model parameters of the first encoder model carried in the first indication information, and then select the first decoder model, such as AI CSI feedback decoder model #3, from the local model library information.

[0347] Optionally, the gNB can send feedback information to the UE to indicate that the gNB has successfully selected AI CSI feedback decoder model #3. In other words, for the AI ​​CSI feedback scenario, the UE and gNB have identified and paired the dual-end model, such as AI CSI feedback encoder model #3 and AI CSI feedback decoder model #3.

[0348] Based on the above steps, after the UE and the gNB complete the selection or matching of the dual-end model, the UE and the gNB can perform CSI feedback based on the dual-end model, for example, see the following steps S860 to S880.

[0349] S860: The gNB sends a first CSI-RS to the UE. Correspondingly, the UE receives the first CSI-RS from the gNB.

[0350] S870: The UE measures the first CSI-RS to obtain the first CSI.

[0351] S880: The UE sends the first result to the gNB. Correspondingly, the gNB receives the first result from the UE.

[0352] For the specific implementation of steps S860 to S880 , reference may be made to the relevant description of steps S670 to S690 of the above method 600 .

[0353] According to the above solution, the UE triggers a first interference information (or first interference signal strength) measurement process to obtain the first interference information, determines a first encoder model based on the obtained first interference information, and indicates the first encoder model to the gNB by sending first indication information. This enables the selection and pairing of the first encoder model and the first decoder model for CSI feedback. In other words, the dual-end model is selected or matched while taking the first interference signal strength into consideration, thereby improving CSI feedback performance.

[0354] Figure 9 is a schematic flow chart of a communication method 900 provided in an embodiment of the present application. As shown in Figure 9, the method can be performed by a second device (e.g., a UE) and a first device (e.g., a gNB) and includes the following steps. Optionally, the second device may be a chip, chip system, or circuit in a terminal device, or a functional module / or device in a terminal device capable of invoking and executing a program. The first device may be a chip, chip system, or circuit in a network device, or a functional module / or device in a network device capable of invoking and executing a program. Optionally, the first device or the second device may be an AI entity or a chip or storage device for an AI entity, such as a model training network element, a model storage network element, or a model inference network element, such as an OAM, OTT, or cloud server, etc., although this application does not limit this. Optionally, the first device may include a terminal device and an AI entity; and optionally, the second device may include a network device and an AI entity, although this is not limited herein.

[0355] S910: The UE performs interference prediction or perception processing to obtain first interference information.

[0356] The content and interpretation of the first interference information may refer to the relevant description of the above method 500.

[0357] S920. Optionally, the UE sends first interference information to the gNB. Correspondingly, the gNB receives the first interference information from the UE.

[0358] It should be understood that after receiving the first interference information from the UE, the gNB may know that the first encoder model determined by the UE in subsequent steps S930-S940 or the first encoder model indicated by the first indication information is associated with the first interference information. In other words, the gNB determines that the first encoder model subsequently indicated by the UE is determined based on the first interference information. The specific interpretation of the association between the first encoder model and the first interference information can be found in the description of method 500 above and is not further explained here.

[0359] S930: The UE determines a first encoder model according to the first interference information.

[0360] S940: The UE sends first indication information to the gNB. Correspondingly, the gNB receives the first indication information from the UE.

[0361] At step S950, the gNB sends a first CSI-RS to the UE. Correspondingly, the UE receives the first CSI-RS from the gNB.

[0362] S960: The UE measures the first CSI-RS to obtain the first CSI.

[0363] S970: The UE sends the first result to the gNB. Correspondingly, the gNB receives the first result from the UE.

[0364] Among them, the content and interpretation of the first indication information involved in the above steps S930 to S970, as well as the specific implementation method, can refer to the relevant description of steps S840-S880 of the above method 800.

[0365] According to the above solution, the UE triggers interference measurement or sensing to obtain first interference information, determines a first encoder model based on the obtained first interference information, and indicates the first encoder model to the gNB by sending first indication information. This enables the selection and pairing of the first encoder model and the first decoder model for CSI feedback. Specifically, the dual-end model is selected or matched while considering the strength of the first interference signal, thereby improving CSI feedback performance.

[0366] It should be noted that Figures 6 to 9 above are mainly used as an example to illustrate the selection or matching of the first encoder model (and / or the first decoder model). It should be understood that the specific implementation of the selection or matching of the first encoder function (and / or the first decoder function) can refer to the relevant description of the selection or matching of the first encoder model (and / or the first decoder model) above, and will not be described here.

[0367] Optionally, in an embodiment of the present application, the selection or matching of the first encoder model (and / or the first decoder model), and the selection or matching of the first encoder function (and / or the first decoder function) can be implemented independently or in combination, and the present application does not limit this. For example, for the obtained first interference information (or the first interference signal strength), the second device can determine the first encoder model and / or the first encoder function accordingly. Among them, the first encoder function can correspond to one or more first encoder models.

[0368] The method provided in the embodiments of the present application is described in detail above with reference to Figures 1 to 9 . Below, the apparatus provided in the embodiments of the present application is described in detail with reference to Figures 10 and 11 . It should be understood that the description of the apparatus embodiment corresponds to the description of the method embodiment. Therefore, for matters not described in detail, reference can be made to the method embodiment above, and for the sake of brevity, they will not be repeated here.

[0369] Figure 10 is a schematic diagram of a communication device 1000 provided in an embodiment of the present application. As shown in Figure 10, the communication device 1000 includes a processing module 1001 and a communication module 1002. The communication device 1000 can be a terminal device, or a communication device applied to a terminal device or used in conjunction with a terminal device and capable of implementing a method executed by the terminal device, such as a chip, a chip system, or a circuit. Alternatively, the communication device 1000 can be a network device, or a communication device applied to a network device or used in conjunction with a network device and capable of implementing a method executed by the network device, such as a chip, a chip system, or a circuit.

[0370] The communication module may also be referred to as a transceiver module, transceiver, transceiver, or transceiver device. The processing module may also be referred to as a processor, processing board, processing unit, or processing device. Optionally, the communication module is used to perform the sending and receiving operations of the terminal device or network device in the above method. The device used to implement the receiving function in the communication module can be considered a receiving unit, and the device used to implement the sending function in the communication module can be considered a sending unit. That is, the communication module includes a receiving unit and a sending unit.

[0371] When the communication device 1000 is applied to a terminal device, the processing module 1001 may be used to implement the processing function of the first device in each of the above embodiments, and the communication module 1002 may be used to implement the transceiver function of the first device in each of the above embodiments.

[0372] When the communication device 1000 is applied to a network device, the processing module 1001 can be used to implement the processing function of the second device in the above embodiments, and the communication module 1002 can be used to implement the transceiver function of the second device in the above embodiments.

[0373] In addition, it should be noted that the aforementioned communication module and / or processing module can be implemented by a virtual module, for example, the processing module can be implemented by a software functional unit or a virtual device, and the communication module can be implemented by a software function or a virtual device. Alternatively, the processing module or the communication module can also be implemented by a physical device, for example, if the device is implemented using a chip / circuit (such as an integrated circuit or a logic circuit, etc.). The communication module can be an input and output circuit and / or a communication interface, performing input operations (corresponding to the aforementioned receiving operations) and output operations (corresponding to the aforementioned sending operations); the processing module is an integrated processor or microprocessor or circuit (such as an integrated circuit or a logic circuit, etc.).

[0374] The division of modules in this application is illustrative and represents only a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the examples of this application may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in either hardware or software functional modules.

[0375] FIG11 is a schematic diagram of another communication device 1100 provided in an embodiment of the present application. As shown in FIG11 , optionally, the communication device 1100 may be the aforementioned first device or second device, or a chip or chip system for the aforementioned first device or second device. Optionally, the communication device 1100 may be the aforementioned terminal device or network device, or a chip or chip system for the aforementioned terminal device or network device. Optionally, in the present application, the chip system may be composed of a chip, or may include a chip and other discrete devices.

[0376] The communication device 1100 can be used to implement the functions of any device (e.g., the first device or the second device) in the communication system described in the above examples. The communication device 1100 may include at least one processing circuit 1110. Optionally, the processing circuit 1110 is coupled to a memory, and the memory may be located within the device, or the memory may be integrated with the processor, or the memory may be located outside the device. For example, the communication device 1100 may also include at least one memory 1120. The memory 1120 stores the necessary computer programs, computer programs or instructions and / or data for implementing any of the above examples; the processing circuit 1110 may execute the computer program stored in the memory 1120 to complete the method in any of the above examples.

[0377] The communication device 1100 may also include a transceiver circuit 1130, and the communication device 1100 can exchange information with other devices through the transceiver circuit 1130. Exemplarily, the transceiver circuit 1130 can be a transceiver, a circuit, a bus, a module, a pin, or other types of transceiver circuits. When the communication device 1100 is a chip-type device or circuit, the transceiver circuit 1130 in the device 1100 can also be an input-output circuit, or an interface circuit, which can input information (or receive information) and output information (or send information). When the communication device 1100 is a first device, a second device, a terminal device, or a network device, the transceiver circuit 1130 can be a transmitter, a receiver, a transceiver, or a communication interface, which is not limited here.

[0378] The processing circuit 1110 may be one or more processors, or all or part of the processing circuits in one or more processors. The processing circuit 1110 may be an integrated processor, microprocessor, integrated circuit, or logic circuit, and the processor may determine output information based on input information.

[0379] Coupling in this application refers to an indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, and is used for information exchange between devices, units, or modules. Processing circuit 1110 may operate in conjunction with memory 1120 and transceiver circuit 1130. This application does not limit the specific connection medium between the processing circuit 1110, memory 1120, and transceiver circuit 1130.

[0380] Optionally, as shown in FIG11 , the processing circuit 1110, the memory 1120, and the transceiver circuit 1130 are interconnected via a bus 1140. Optionally, the bus may include an address bus, a data bus, a control bus, or other types of buses. Furthermore, for ease of illustration, FIG11 shows one bus 1140, but this does not mean that there is only one bus or only one type of bus.

[0381] It should be understood that the processors mentioned in the embodiments of the present application may be the following devices or the circuit portions of the following devices used for processing functions: a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0382] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory and / or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM). For example, RAM can be used as an external cache. By way of example and not limitation, RAM includes the following forms: static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0383] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) can be integrated into the processor.

[0384] It should also be noted that the memory described herein is intended to comprise, but not be limited to, these and any other suitable types of memory.

[0385] In an embodiment of the present application, the method described in the above embodiment can be executed by the terminal device and the network device, or can be executed by the chip, chip system or circuit of the terminal device and the network device, and the chip, chip system or circuit can be installed in the terminal device and the network device.

[0386] An embodiment of the present application provides a computer-readable storage medium on which computer instructions for implementing the methods executed by a device (such as a terminal device or a network device) in the above-mentioned method embodiments are stored.

[0387] For example, when the computer program is executed by a computer, the computer can implement the methods performed by a device (such as a terminal device, or a network device, etc.) in each embodiment of the above method.

[0388] An embodiment of the present application provides a computer program product comprising instructions, which, when executed by a computer, implement the methods performed by a device (such as a terminal device, or a network device (or a positioning device), etc.) in the above-mentioned method embodiments.

[0389] An embodiment of the present application provides a communication system, which includes the terminal device and / or network device described in each of the above embodiments. For example, the system includes the terminal device and / or network device described in the above embodiments. For another example, the system includes the terminal device and / or network device described in the above embodiments.

[0390] The explanation of the relevant contents and beneficial effects of any of the above-mentioned devices can be referred to the corresponding method embodiments provided above, which will not be repeated here.

[0391] To facilitate understanding of the above embodiments provided in this application, the following points are explained:

[0392] In this application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0393] In the present application, "at least one" means one or more, and "more" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can 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. In the text description of the present application, the character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b and c can mean: a, or b, or c, or a and b, or a and c, or b and c, or a, b and c. Wherein a, b and c can be single or multiple, respectively.

[0394] In this application, the terms "first," "second," and various numerical references are used for descriptive purposes only and are not intended to limit the scope of the embodiments of this application. For example, they are used to distinguish between different messages, rather than to describe a specific order or precedence. It should be understood that the terms described in this manner are interchangeable, where appropriate, to allow for the description of scenarios beyond the embodiments of this application.

[0395] In this application, the terms "comprises" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product or apparatus.

[0396] In this application, "used for indication" can include direct indication and indirect indication. When describing a certain indication information as indicating A, it can include whether the indication information directly indicates A or indirectly indicates A, and it does not necessarily mean that the indication information carries A. Direct indication of information A means including information A; implicit indication of information A means indicating information A through the correspondence between information A and information B and the direct indication of information B. The correspondence between information A and information B can be predefined, pre-stored, pre-burned, or pre-configured.

[0397] In this application, information C is used to determine information D, which includes both information D being determined solely based on information C and information D being determined based on information C and other information. Furthermore, information C can also be used to determine information D indirectly, for example, where information D is determined based on information E, and information E is determined based on information C.

[0398] In the present application, "network element A sends information A to network element B" can be understood as the destination end of the information A or the intermediate network element in the transmission path between the destination end and the network element B, which may include directly or indirectly sending information to network element B. "Network element B receives information A from network element A" can be understood as the source end of the information A or the intermediate network element in the transmission path between the source end and the network element A, which may include directly or indirectly receiving information from network element A. The information may be processed as necessary between the source end and the destination end of the information transmission, such as format changes, but the destination end can understand the valid information from the source end. Similar expressions in this application can be understood similarly and will not be elaborated here.

[0399] It can be understood that some optional features in the various embodiments of the present application may not depend on other features in certain scenarios, and may also be combined with other features in certain scenarios, without limitation.

[0400] It can also be understood that in some of the above embodiments, sending information is mentioned multiple times. For example, "network element A sends information A to network element B", which can be understood as the destination end of the information A or the intermediate network element in the transmission path between the destination end and the network element B, and can include directly or indirectly sending information to network element B. "Network element B receives information A from network element A" can be understood as the source end of the information A or the intermediate network element in the transmission path between the source end and the network element A, and can include directly or indirectly receiving information from network element A. The information may be processed as necessary between the source end and the destination end of the information transmission, such as format changes, etc., but the destination end can understand the valid information from the source end. Similar expressions in this application can be understood similarly and will not be repeated here.

[0401] It can also be understood that in some of the above embodiments, the AI ​​model is mainly used as an example for illustrative description. It can be understood that the above AI model can also be used for other purposes.

[0402] It can also be understood that the solutions in the various embodiments of the present application can be reasonably combined and used, and the explanations or descriptions of the various terms appearing in the embodiments can be referenced or explained with each other in the various embodiments, without limitation to this.

[0403] It can also be understood that in the above-mentioned various method embodiments, the methods and operations implemented by the terminal device or positioning device can also be implemented by components (such as chips or circuits) that can be implemented by the terminal device or positioning device, without limitation.

[0404] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0405] 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.

[0406] 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 through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0407] 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.

[0408] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0409] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling 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 each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0410] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A communication method, characterized in that, Applied to a first device, comprising: Receiving first indication information from a second device, the first indication information being used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function being used to process channel state information (CSI), and the first encoder model or the first encoder function being related to a first interference signal strength.

2. The method according to claim 1, characterized in that, The first encoder function includes one or more of the first encoder models.

3. The method according to claim 1 or 2, wherein The first indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or The first indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

4. The method according to any one of claims 1 to 3, characterized in that When the first device is a terminal device and the second device is a network device, before receiving the first indication information from the second device, the method further includes: Receiving configuration information from the second device, the configuration information being used to indicate a first reference signal, the first reference signal including one or more of the following: channel state information reference signal (CSI-RS), zero-power channel state information reference signal (ZP CSI-RS), or channel state information interference measurement (CSI-IM) signal; Performing channel measurement on the first reference signal to obtain first interference information, the first interference information being used to indicate the first interference signal strength; Sending the first interference information to the second device.

5. The method according to any one of claims 1 to 4, characterized in that When the first device is a terminal device and the second device is a network device, before receiving the first indication information from the second device, the method further includes: Sending model library information to the second device, the model library information being used to indicate a mapping relationship between a plurality of encoder models and a plurality of interference signal strength ranges, or the model library information being used to indicate a mapping relationship between a plurality of encoder functions and a plurality of interference signal strength ranges, the first encoder model belonging to the plurality of encoder models, the first encoder function belonging to the plurality of encoder functions, and the first interference signal strength being included in one of the plurality of interference signal strength ranges.

6. The method according to any one of claims 1 to 5, characterized in that When the first device is a terminal device and the second device is a network device, the method further includes: Receiving a first CSI-RS from the second device; Sending a first result to the second device, the first result being obtained by processing a first CSI based on the first encoder model or the first encoder function, the first CSI being measured from the first CSI-RS, and the first CSI being related to the first interference signal strength.

7. The method according to any one of claims 1 to 3, characterized in that, When the first device is a network device and the second device is a terminal device, before receiving the first indication information from the second device, the method further includes: Send configuration information to the second device, where the configuration information is used to indicate a first reference signal, and the first reference signal includes one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; Among them, the first interference information is obtained based on the measurement of the first reference signal, and the first interference information is used to indicate the first interference signal strength.

8. The method according to claim 7, characterized in that Before sending the configuration information to the second device, the method further includes: Receive a request message from the second device, where the request message is used to request the second device to send the configuration information.

9. The method according to any one of claims 1 to 3, 7 or 8, characterized in that, When the first device is a network device and the second device is a terminal device, the method further includes: Send second indication information to the second device, where the second indication information is used to indicate that the first device has selected or matched a first decoder model or a first decoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

10. The method according to any one of claims 1 to 3, or 7 to 9, characterized in that When the first device is a network device and the second device is a terminal device, the method further includes: Send the first CSI-RS to the second device; Receive a first result from the second device, where the first result is obtained by processing a first CSI based on the first encoder model or the first encoder function, and the first CSI is obtained by measuring the first CSI-RS.

11. The method according to claim 10, wherein The method further includes: Obtain a second result, where the second result is obtained by processing the first result based on a first decoder model or a first decoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

12. A communication method, characterized in that, When applied to a second device, it includes: Obtain the first interference signal strength; Send first indication information to the first device, where the first indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function is used to process channel state information CSI, and the first encoder model or the first encoder function is related to the first interference signal strength.

13. The method according to claim 12, wherein The first encoder function includes one or more of the first encoder models.

14. The method according to claim 12 or 13, characterized in that, The first indication information includes the identifier and / or the model parameters of the first encoder model; or, the first indication information includes the identifier and / or the model parameters corresponding to the first encoder function.

15. The method according to any one of claims 12 to 14, characterized in that When the first device is a terminal device and the second device is a network device, obtaining the first interference signal strength includes: Receive first interference information from the first device, where the first interference information is used to indicate the first interference signal strength.

16. The method according to claim 15, wherein Before receiving the first interference information from the first device, the method further includes: Send configuration information to the first device, where the configuration information is used to indicate a first reference signal, and the first reference signal includes one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; Wherein, the first interference information is obtained based on channel measurement of the first reference signal.

17. The method according to any one of claims 12 to 14, characterized in that When the first device is a terminal device and the second device is a network device, the obtaining of the first interference signal strength includes: Obtaining the first interference information through local retrieval or prediction; or, Obtaining the first interference information through cloud retrieval or prediction; Wherein, the first interference information is used to indicate the first interference signal strength.

18. The method according to any one of claims 12 to 17, characterized in that, When the first device is a terminal device and the second device is a network device, before sending the first indication information to the first device, the method further includes: Receiving model library information from the first device, where the model library information is used to indicate the mapping relationship between multiple encoder models and multiple signal strength ranges, or the model library information is used to indicate the mapping relationship between multiple encoder functions and multiple interference signal strength ranges, the first encoder model belongs to the multiple encoder models, the first encoder function belongs to the multiple encoder functions, and the first interference signal strength is included in one of the multiple interference signal strength ranges.

19. The method according to any one of claims 12 to 14, characterized in that When the first device is a network device and the second device is a terminal device, the obtaining of the first interference signal strength includes: Receiving configuration information from the first device, where the configuration information is used to indicate a first reference signal, and the first reference signal includes one or more of the following: a channel state information reference signal CSI-RS, a zero-power channel state information reference signal ZP CSI-RS, or a channel state information interference measurement CSI-IM signal; Performing channel measurement on the first reference signal according to the configuration information to obtain first interference information, where the first interference information is used to indicate the first interference signal strength.

20. The method according to claim 19, wherein The method further includes: sending a request message to the first device, where the request message is used to request the first device to send the configuration information.

21. The method according to any one of claims 12 to 14, characterized in that, When the first device is a network device and the second device is a terminal device, the obtaining of the first interference signal strength includes: Performing an interference prediction or sensing operation to obtain the first interference signal strength.

22. The method according to any one of claims 12 to 21, characterized in that, When the first device is a network device and the second device is a terminal device, the method further includes: Receiving second indication information from the first device, where the second indication information is used to indicate that the first device has selected or matched a first decoder model or a first decoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

23. The method according to any one of claims 12 to 22, characterized in that When the first device is a network device and the second device is a terminal device, the method further includes: Send first interference information to the first device, where the first interference information is used to indicate the first interference signal strength.

24. The method according to any one of claims 12 to 23, characterized in that When the first device is a terminal device and the second device is a network device, the method further includes: Send a first CSI-RS to the first device; Receive a first result from the first device, where the first result is obtained by processing first CSI based on the first encoder model or the first encoder function, the first CSI is obtained by measuring the first CSI-RS, and the first CSI is related to the first interference signal strength.

25. The method according to claim 24, wherein The method further includes: Obtain a second result, where the second result is obtained by processing the first result based on a first decoder model or a first decoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

26. The method according to any one of claims 12 to 25, characterized in that, When the first device is a network device and the second device is a terminal device, the method further includes: Receive a first CSI-RS from the first device; Send a first result to the first device, where the first result is obtained by processing first CSI based on the first encoder model or the first encoder function, and the first CSI is obtained by measuring the first CSI-RS.

27. A communication method, characterized in that, Applied to a terminal device or a chip in the terminal device, it includes: Receive first indication information from a network device, where the first indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function is used to process channel state information (CSI), and the first encoder model or the first encoder function is related to the first interference signal strength.

28. The method according to claim 27, wherein The first encoder function includes one or more of the first encoder models.

29. The method according to claim 27 or 28, characterized in that, The first indication information includes an identifier of the first encoder model and / or model parameters of the first encoder model; or, the first indication information includes an identifier of the first encoder function and / or model parameters corresponding to the first encoder function.

30. The method according to any one of claims 27 to 29, characterized in that, Before receiving the first indication information from the network device, the method further includes: Receive configuration information from the network device, where the configuration information is used to indicate a first reference signal, and the first reference signal includes one or more of the following: channel state information reference signal (CSI-RS), zero-power channel state information reference signal (ZP CSI-RS), channel state information interference measurement (CSI-IM) signal; Perform channel measurement on the first reference signal to obtain first interference information, where the first interference information is used to indicate the first interference signal strength; Send the first interference information to the network device.

31. The method according to any one of claims 27 to 30, characterized in that, Before receiving the first indication information from the network device, the method further includes: Send model library information to the network device, where the model library information is used to indicate the mapping relationship between multiple encoder models and multiple interference signal strength ranges, or the model library information is used to indicate the mapping relationship between multiple encoder functions and multiple interference signal strength ranges. The first encoder model belongs to the multiple encoder models, the first encoder function belongs to the multiple encoder functions, and the first interference signal strength is included in one of the multiple interference signal strength ranges.

32. The method according to any one of claims 27 to 31, characterized in that, The method further includes: Receive a first CSI-RS from the network device; Send a first result to the network device, where the first result is obtained by processing a first CSI based on the first encoder model or the first encoder function. The first CSI is measured from the first CSI-RS, and the first CSI is related to the first interference signal strength.

33. A communication method, characterized in that, Applied to a network device or a chip in a network device, it includes: Obtain a first interference signal strength; Send first indication information to the terminal device, where the first indication information is used to indicate a first encoder model or a first encoder function, and the first encoder model or the first encoder function is used to process channel state information (CSI). The first encoder model or the first encoder function is related to the first interference signal strength.

34. The method according to claim 33, characterized in that, The first encoder function includes one or more of the first encoder models.

35. The method according to claim 33 or 34, characterized in that, The first indication information includes the identifier and / or the model parameters of the first encoder model; or, the first indication information includes the identifier and / or the model parameters corresponding to the first encoder function.

36. The method according to any one of claims 33 to 35, characterized in that, The obtaining of the first interference signal strength includes: Receive first interference information from the terminal device, where the first interference information is used to indicate the first interference signal strength.

37. The method according to claim 36, wherein Before receiving the first interference information from the terminal device, the method further includes: Send configuration information to the terminal device, where the configuration information is used to indicate a first reference signal, and the first reference signal includes one or more of the following: channel state information reference signal (CSI-RS), zero-power channel state information reference signal (ZP CSI-RS), or, channel state information interference measurement (CSI-IM) signal; Wherein, the first interference information is obtained based on channel measurement of the first reference signal.

38. The method according to any one of claims 33 to 35, characterized in that, The obtaining of the first interference signal strength includes: Obtain the first interference information through local retrieval or prediction; or, Obtain the first interference information through cloud retrieval or prediction; Wherein, the first interference information is used to indicate the first interference signal strength.

39. The method according to any one of claims 33 to 38, characterized in that, The method further includes: Receiving model library information from the terminal device, where the model library information is used to indicate the mapping relationship between multiple encoder models and multiple signal strength ranges, or the model library information is used to indicate the mapping relationship between multiple encoder functions and multiple interference signal strength ranges. The first encoder model belongs to the multiple encoder models, the first encoder function belongs to the multiple encoder functions, and the first interference signal strength is included in one of the multiple interference signal strength ranges.

40. The method according to any one of claims 33 to 39, characterized in that, The method further includes: Sending a first CSI-RS to the terminal device; Receiving a first result from the terminal device, where the first result is obtained by processing a first CSI based on the first encoder model or the first encoder function, and the first CSI is measured from the first CSI-RS.

41. The method according to claim 40, characterized in that, The method further includes: Obtaining a second result, where the second result is obtained by processing the first result based on a first decoder model or a first decoder function, and the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

42. A communication method, characterized in that, Applied to a terminal device or a chip in the terminal device, including: Obtaining a first interference signal strength; Sending second indication information to the network device, where the second indication information is used to indicate a first encoder model or a first encoder function, and the first encoder model or the first encoder function is used to process channel state information (CSI), and the first encoder model or the first encoder function is related to the first interference signal strength.

43. The method according to claim 42, wherein, The first encoder function includes one or more of the first encoder models.

44. The method according to claim 42 or 43, characterized in that, The second indication information includes the identifier of the first encoder model and / or the model parameters of the first encoder model; or the second indication information includes the identifier of the first encoder function and / or the model parameters corresponding to the first encoder function.

45. The method according to any one of claims 42 to 44, characterized in that, The obtaining of the first interference signal strength includes: Receiving configuration information from the network device, where the configuration information is used to indicate a first reference signal, and the first reference signal includes one or more of the following: channel state information reference signal (CSI-RS), zero-power channel state information reference signal (ZP CSI-RS), or channel state information interference measurement (CSI-IM) signal; Performing channel measurement on the first reference signal according to the configuration information to obtain first interference information, where the first interference information is used to indicate the first interference signal strength.

46. The method according to claim 45, wherein Before obtaining the first interference signal strength, the method further includes: Sending a request message to the network device, where the request message is used to request the network device to send the configuration information.

47. The method according to any one of claims 42 to 44, characterized in that The obtaining of the first interference signal strength includes: Performing an interference prediction or sensing operation to obtain the first interference signal strength.

48. The method according to any one of claims 42 to 46, characterized in that, The method further includes: Receive third indication information from the network device, where the third indication information is used to indicate that the terminal device has selected or matched a first decoder model or a first encoder function, the first decoder model corresponding to the first encoder model, and the first decoder function corresponding to the first encoder function.

49. The method according to any one of claims 42 to 48, characterized in that, The method further includes: Send first interference information to the network device, where the first interference information is used to indicate the first interference signal strength.

50. The method according to any one of claims 42 to 49, characterized in that, The method further includes: Receive a first CSI-RS from the network device; Send a first result to the network device, where the first result is obtained by processing a first CSI based on the first encoder model or the first encoder function, the first CSI being measured from the first CSI-RS, and the first CSI being related to the first interference signal strength.

51. A communication method, characterized in that, Applied to a network device or a chip in the network device, it includes: Receive second indication information from the terminal device, where the second indication information is used to indicate a first encoder model or a first encoder function, the first encoder model or the first encoder function being used to process channel state information (CSI), and the first encoder model or the first encoder function being related to the first interference signal strength.

52. The method according to claim 51, wherein The first encoder function includes one or more of the first encoder models.

53. The method according to claim 51 or 52, characterized in that, The second indication information includes the identifier and / or the model parameters of the first encoder model; or, the second indication information includes the identifier and / or the model parameters corresponding to the first encoder function.

54. The method according to any one of claims 51 to 53, characterized in that, Before receiving the second indication information from the terminal device, the method further includes: Send configuration information to the terminal device, where the configuration information is used to indicate a first reference signal, the first reference signal including one or more of the following: channel state information reference signal (CSI-RS), zero-power channel state information reference signal (ZP CSI-RS), or, channel state information interference measurement (CSI-IM) signal; Wherein, the first interference information is obtained by measuring the first reference signal, and the first interference information is used to indicate the first interference signal strength.

55. The method according to claim 54, wherein Before sending the configuration information to the terminal device, the method further includes: Receive a request message from the terminal device, where the request message is used to request the network device to send the configuration information.

56. The method according to any one of claims 51 to 55, characterized in that, The method further includes: Send third indication information to the terminal device, where the third indication information is used to indicate that the terminal device has selected or matched a first decoder model or a first decoder function, the first decoder model corresponding to the first encoder model, and the first decoder function corresponding to the first encoder function.

57. The method according to any one of claims 51 to 56, characterized in that, The method further includes: Receive first interference information from the terminal device, where the first interference information is used to indicate the first interference signal strength.

58. The method according to any one of claims 51 to 57, characterized in that, The method further includes: Send a first CSI-RS to the terminal device; Receive a first result from the terminal device, where the first result is obtained by processing a first CSI based on the first encoder model or the first encoder function, and the first CSI is obtained by measuring the first CSI-RS.

59. The method according to claim 58, wherein, The method further includes: Obtain a second result, where the second result is obtained by processing the first result based on a first decoder model or a first decoder function, the first decoder model corresponding to the first encoder model, and the first decoder function corresponding to the first encoder function.

60. A communication device, characterized in that, Comprising a module for executing the method according to any one of claims 1-11, 27-32, 42-50, or a module for executing the method according to any one of claims 12-26, 33-41, 51-59.

61. A communication device, characterized in that, Comprising at least one processor for executing a computer program or instructions in a memory to cause the method according to any one of claims 1-11, 27-32, 42-50 to be executed, or to cause the method according to any one of claims 12-26, 33-41, 51-59 to be executed.

62. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, and when the computer program runs on a computer, to cause the method according to any one of claims 1-59 to be executed.

63. A computer program product, characterized in that, Comprising a computer program or instructions, and when the computer program or instructions are executed by a processor, to cause the method according to any one of claims 1-59 to be executed.

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