Communication method and communication device

Abstract

The invention discloses a communication method and a communication device, which are applied to a scene in which AI and a wireless network are combined. The method comprises: a second device obtaining a first interference signal strength and sending first indication information to a first device, the first indication information being used for indicating a first encoder model or a first encoder function, the first encoder model or the first encoder function being used for processing CSI, and the first encoder model or the first encoder function being used for processing CSI; the first encoder model or the first encoder function is related to the first interference signal strength. According to the technical scheme, the first encoder model or the first encoder function is determined under the condition that the first interference signal strength is considered, so that the AI CSI feedback performance is expected to be improved.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and more particularly, to a communication method and a communication device. Background Art

[0002] In a communication system, a network device needs to determine resource, modulation and coding scheme (MCS), precoding, and other related configuration information of a downlink data channel for scheduling a terminal device according to downlink channel state information (CSI). The terminal device can calculate the downlink CSI by measuring a downlink reference signal and generate a CSI report to feedback to the network device.

[0003] After introducing artificial intelligence (AI) into a wireless communication network, an AI-based CSI feedback method has emerged. The AI model has a strong feature extraction ability, can effectively compress channel information, reduce information loss during the compression process, and ensure the accuracy of the restored channel information. However, it is found in research that when using the AI-based CSI feedback, the CSI feedback performance corresponding to the same AI model or AI function is poor at certain moments. Summary of the Invention

[0004] This application provides a communication method and a communication device, aiming to improve the AI-based CSI feedback performance.

[0005] In a first aspect, a communication method is provided. This method can be executed by a first device, or can also be executed by a chip or circuit of the first device. This application does not make any limitation in this regard. For the sake of description, the following takes the execution by the first device as an example. Among them, the first device may be a terminal device or a network device, or a chip, a chip system, or a circuit in the terminal device or the network device, or a functional module in the terminal device or the network device that can call and execute a program.

[0006] The method includes: receiving first indication information from a second 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), and the first encoder model or the first encoder function is related to a first interference signal strength.

[0007] In this application, the fact that the first encoder model or the first encoder function is related to the first interference signal strength 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 under the consideration of the interference signal strength.

[0008] According to the solution provided by this application, the first device can determine the first encoder model or the first encoder function based on the received first indication information. Subsequently, the first device can use the model corresponding to the first encoder model or the first encoder function to compress and quantize the CSI. The second device can use the model corresponding to the first decoder model or the first decoder function to decompress and quantize the CSI, thereby improving the CSI feedback performance.

[0009] In this application, the first encoder model corresponds to the first decoder model, that is, the first encoder model and the first decoder model are usually co-trained and can be used in a matching manner. It should be understood that the number of AI models included in the matching first encoder model and first decoder model is the same and they correspond one by one. The first encoder model and the first decoder model can be understood as a set of matching AI models. Among them, the first encoder model and the first decoder model can be called a dual model, a bilateral model, a collaborative model, or a paired 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 the compressed CSI. Similarly, if 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.

[0010] Combined with the first aspect, in some implementation manners of the first aspect, the first encoder function includes one or more first encoder models.

[0011] It should be understood that the first encoder function can correspond to one or more encoder models with the same function, and the first device can determine one or more encoder models through the first encoder function.

[0012] Combined with the first aspect, in some implementation manners of the first aspect, 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 of the first encoder function.

[0013] Based on the above solution, by carrying the identifier and / or the model parameters of the first encoder model in the first indication information, the first device can determine the first encoder model, and then use the first encoder model to compress and quantize the CSI, ensuring the CSI feedback performance; or, by carrying the identifier and / or the model parameters of the first encoder function in the first indication information, the first device can determine the model corresponding to the first encoder function, and then use the model to compress and quantize the CSI, ensuring the CSI feedback performance.

[0014] In combination with the first aspect, in some 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 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: 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 intensity of a first interference signal; and sending the first interference information to the second device.

[0015] Based on the above solution, the second device triggers a 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, where the channel measurement result includes the first interference information and is used to determine the current channel interference level, so that a suitable encoder model or encoder function can be selected or matched according to the first interference information subsequently to ensure network performance.

[0016] In combination with the first aspect, in some 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 further includes: sending model library information to the second device, the model library information being used to indicate the 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 the 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.

[0017] Based on the above solution, 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 according to the obtained first interference signal strength. Subsequently, the first device and the second device can use the first encoder model and its matched first decoder model, or use the model corresponding to the first encoder function and the model matched with the first decoder function corresponding to the model to compress and recover the channel information, which can improve the CSI feedback performance while reducing the signaling overhead.

[0018] In combination with the first aspect, in some 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 further includes: sending 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; wherein, 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.

[0019] In combination with the first aspect, in some implementations of the first aspect, when the first device is a network device and the second device is a terminal device, before sending the configuration information to the second device, the method further includes: receiving a request message from the second device, where the request message is used to request the second device to send the configuration information.

[0020] Based on the above solution, the network device can determine to trigger the interference measurement process of the signal based on the request message of the terminal device, that is, trigger sending the 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 considering the first interference information (or the first interference signal strength) to improve the CSI feedback performance.

[0021] In combination with the first aspect, in some 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: sending 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.

[0022] Based on the above solution, through the second indication information, it can be determined 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 as to implement compression quantization processing and decompression quantization processing of the first reference signal, etc., and improve the CSI feedback performance.

[0023] In combination with the first aspect, in some 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 the first interference information from the second device, where the first interference information is used to indicate the first interference signal strength.

[0024] Based on the above solution, the terminal device sends the first interference information to the network device. According to the first interference information, the network device can know that the first encoder model or the first encoder function indicated by the first indication information by the terminal device later 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 according to the first interference information.

[0025] Combined with the first aspect, in some implementation manners of the first aspect, 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, where the first result is obtained by processing a first CSI using a first encoder model or a 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.

[0026] Based on the above solution, based on the selected or matched first encoder model and first decoder model, or the first encoder function and first decoder function, the first device and the second device can effectively implement measurements, compression quantization processing, decompression quantization processing, etc. of the first reference signal, and can improve the CSI feedback performance while reducing the signaling overhead.

[0027] Combined with the first aspect, in some implementation manners of the first aspect, when the first device is a network device and the second device is a terminal device, the method further includes: sending a first CSI-RS to the second device; receiving a first result from the second device, where the first result is obtained by processing a first CSI based on a first encoder model or a first encoder function, and the first CSI is obtained by measuring the first CSI-RS.

[0028] Combined with the first aspect, in some implementation manners of the first aspect, 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, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

[0029] It should be understood that in this application, the first encoder model deployment can be on the first device side, and the second encoder model is deployed on the second device side. Then, the first device and the second device can use the first encoder model and the first decoder model for 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). Then, after the first device measures the first CSI, it needs to interact with the third device. The third device uses the first encoder model to compress the first CSI to obtain the feedback CSI and sends the feedback CSI to the first device. Correspondingly, after the second device obtains the feedback CSI, it needs to interact with the fourth device. The fourth device uses the first decoder model to decompress the feedback CSI to obtain the restored CSI and sends the restored CSI to the second device.

[0030] In a second aspect, a communication method is provided. This method can be executed by a second device, or alternatively, by a chip or circuit of the second device. This application does not limit this. For ease of description, the following will take the execution by the second device as an example. Among them, 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.

[0031] The method includes: obtaining a first interference signal strength; sending first indication information to a first 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), and the first encoder model or the first encoder function is related to the first interference signal strength.

[0032] In this application, the first encoder model or the first encoder function being related to the first interference signal strength 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 considering the interference signal strength.

[0033] According to the solution provided by this application, the second device can select or match the corresponding first encoder model or first encoder function according to 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 model corresponding to the first encoder model or first encoder function to implement compression and quantization processing of CSI, and the second device can use the model corresponding to the first decoder model or first decoder function to implement decompression and quantization processing of CSI, ensuring the CSI feedback performance.

[0034] In combination with the second aspect, in some implementation manners of the second aspect, the first encoder function includes one or more first encoder models.

[0035] In combination with the second aspect, in some implementation manners of the second aspect, the first indication information includes the identifier and / or model parameters of the first encoder model; or, the first indication information includes the identifier of the first encoder function and / or the model parameters corresponding to the first encoder function.

[0036] In combination with the second aspect, in some implementation manners 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, where the first interference information is used to indicate the first interference signal strength.

[0037] In combination with the second aspect, in some implementation manners of the second aspect, before receiving the first interference information from the first device, the method further includes: sending 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: 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; where the first interference information is obtained based on channel measurement of the first reference signal.

[0038] In combination with the second aspect, in some implementation manners 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.

[0039] In combination with the second aspect, in some 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 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.

[0040] In combination with the second aspect, in some 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, 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 a 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.

[0041] In combination with the second aspect, in some 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 an interference prediction or sensing operation to obtain the first interference signal strength.

[0042] In combination with the second aspect, in some 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 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.

[0043] In combination with the second aspect, in some 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: 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 encoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

[0044] In combination with the second aspect, in some 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 first interference signal strength.

[0045] In combination with the second aspect, in some implementations of the second aspect, when the first device is a terminal device and the second device is a network device, the method further 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 a first CSI based on a first encoder model or a first encoder function, the first CSI being measured from the first CSI-RS, and the first CSI being related to a first interference signal strength.

[0046] In combination with the second aspect, in some implementations of the second aspect, the method further 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.

[0047] In combination with the second aspect, in some 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: receiving a first CSI-RS from the first device; sending a first result to the first device, the first result being obtained by processing a first CSI based on a first encoder model or a first encoder function, the first CSI being measured from the first CSI-RS.

[0048] The beneficial effects of the above second aspect and some implementations of the second aspect can be correspondingly referred to the descriptions related to the first aspect, and will not be elaborated here.

[0049] In a third aspect, a communication method is provided. This method can be executed by a terminal device, or can be executed by a chip or circuit of the terminal device. This application does not make any limitations in this regard. For ease of description, the following will take the execution by the terminal device as an example for illustration.

[0050] The method includes: receiving first indication information from a network 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.

[0051] In combination with the third aspect, in some implementations of the third aspect, the first encoder function includes one or more first encoder models.

[0052] In combination with the third aspect, in some 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.

[0053] In combination with the third aspect, in some implementations of the third aspect, before receiving the first indication information from the network device, the method further 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, channel state information interference measurement CSI-IM signal; performing 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; and sending the first interference information to the network device.

[0054] In combination with the third aspect, in some implementations of the third aspect, before receiving the first indication information from the network device, the method further includes: sending 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.

[0055] In combination with the third aspect, in some implementations of the third aspect, the method further includes: receiving the first CSI-RS from the network device; sending a first result to the network device, where the first result is obtained by processing the 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.

[0056] In a fourth aspect, a communication method is provided. This method can be executed by a network device, or can be executed by a chip or circuit of the network device. This application does not make a limitation in this regard. For ease of description, the following takes the execution by the network device as an example for illustration.

[0057] The method includes: obtaining the first interference signal strength; sending the first indication information to the terminal device, where 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 the channel state information CSI, and the first encoder model or the first encoder function is related to the first interference signal strength.

[0058] In combination with the fourth aspect, in some implementations of the fourth aspect, the first encoder function includes one or more first encoder models.

[0059] In combination with the fourth aspect, in some implementations of the fourth aspect, the first indication information includes the identifier of the first encoder model and / or the model parameters of the first encoder model; or, the first indication information includes the identifier of the first encoder function and / or the model parameters corresponding to the first encoder function.

[0060] 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.

[0061] In combination with the fourth aspect, in some implementations of the fourth aspect, before receiving the first interference information from the terminal device, the method further includes: sending 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: 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; where the first interference information is obtained based on channel measurement of the first reference signal.

[0062] In combination with the fourth aspect, in some 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.

[0063] In combination with the fourth aspect, in some implementations of the fourth aspect, the method further includes: receiving model library information from a 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.

[0064] In combination with the fourth aspect, in some implementations of the fourth aspect, 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 the 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.

[0065] In combination with the fourth aspect, in some implementations of the fourth aspect, the method further includes: obtaining a second result, where the second result is obtained by processing the first result based on 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.

[0066] Fifth aspect, a communication method is provided. This method can be executed by a terminal device, or can also be executed by a chip or circuit of the terminal device. This application does not make a limitation in this regard. For ease of description, the following takes the execution by the terminal device as an example for illustration.

[0067] The method includes: obtaining a first interference signal strength; sending second indication information to a network 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 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.

[0068] In combination with the fifth aspect, in some implementation manners of the fifth aspect, the first encoder function includes one or more first encoder models.

[0069] In combination with the fifth aspect, in some implementation manners 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.

[0070] In combination with the fifth aspect, in some implementation manners of the fifth aspect, obtaining the first interference signal strength includes: receiving configuration information from a 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, and the first interference information is used to indicate the first interference signal strength.

[0071] In combination with the fifth aspect, in some implementation manners of the fifth aspect, obtaining the first interference signal strength includes: performing an interference prediction or sensing operation to obtain the first interference signal strength.

[0072] In combination with the fifth aspect, in some implementation manners 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.

[0073] In combination with the fifth aspect, in some implementation manners of the fifth aspect, the method further includes: receiving third indication information from the network device, where the third indication information is used to indicate that a first device has selected or matched a first decoder model or a first encoder function, the first decoder model corresponds to the first encoder model, and the first decoder function corresponds to the first encoder function.

[0074] In combination with the fifth aspect, in some implementations of the fifth aspect, the method further includes: sending first interference information to a network device, where the first interference information is used to indicate the first interference signal strength.

[0075] In combination with the fifth aspect, in some implementations of the fifth aspect, the method further includes: receiving a first CSI-RS from a network device; sending a first result to the network device, where the first result is obtained by processing a first CSI based on a first encoder model or a 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.

[0076] A sixth aspect provides a communication method, which can be executed by a network device, or can be executed by a chip or circuit of the network device, and this application does not limit this. For the sake of description, the following takes the execution by the network device as an example for illustration.

[0077] The method includes: receiving second indication information from a 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 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.

[0078] In combination with the sixth aspect, in some implementations of the sixth aspect, the first encoder function includes one or more first encoder models.

[0079] In combination with the sixth aspect, in some 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.

[0080] In combination with the sixth aspect, in some implementations of the sixth aspect, before receiving the second indication information from the terminal device, the method further includes: sending 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; where the first interference information is measured from the first reference signal, and the first interference information is used to indicate the first interference signal strength.

[0081] In combination with the sixth aspect, in some 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.

[0082] In combination with the sixth aspect, in some implementations of the sixth aspect, the method further includes: sending third indication information to a terminal device, where the third 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.

[0083] 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 the first interference signal strength.

[0084] In combination with the sixth aspect, in some implementations of the sixth aspect, the method further includes: sending a first CSI-RS to a 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.

[0085] In combination with the sixth aspect, in some implementations of the sixth aspect, the method further includes: obtaining a second result, where the second result is obtained by processing the first result based on 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.

[0086] The beneficial effects of the foregoing third aspect to sixth aspect and their some implementations can be correspondingly referred to the descriptions related to the first aspect or the second aspect, and will not be elaborated herein.

[0087] The seventh aspect provides a communication device, which includes: a transceiver unit, configured to receive first indication information from a second 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.

[0088] The transceiver unit may perform the receiving and sending processes in the foregoing first aspect, and the processing unit of the communication device may perform other processes in the foregoing first aspect except for receiving and sending.

[0089] The eighth aspect provides a communication device, which includes: a processing unit, configured to obtain the first interference signal strength; a transceiver unit, configured to send first indication information to a 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.

[0090] The transceiver unit can perform the receiving and sending processes in the foregoing second aspect, and the processing unit of the communication device can perform other processes in the foregoing second aspect except for receiving and sending.

[0091] In a ninth aspect, a communication device is provided, which includes: a transceiver unit, configured to 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 configured to process channel state information (CSI), and the first encoder model or the first encoder function is related to a first interference signal strength.

[0092] The transceiver unit can perform the receiving and sending processes in the foregoing third aspect, and the processing unit of the communication device can perform other processes in the foregoing third aspect except for receiving and sending.

[0093] In a tenth aspect, a communication device is provided, which includes: a processing unit, configured to obtain a first interference signal strength; a transceiver unit, configured to send first indication information to a terminal 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 configured to process channel state information (CSI), and the first encoder model or the first encoder function is related to a first interference signal strength.

[0094] The transceiver unit can perform the receiving and sending processes in the foregoing fourth aspect, and the processing unit of the communication device can perform other processes in the foregoing fourth aspect except for receiving and sending.

[0095] In an eleventh aspect, a communication device is provided, which includes: a processing unit, configured to obtain a first interference signal strength; a transceiver unit, configured to send second indication information to a network 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 is configured to process channel state information (CSI), and the first encoder model or the first encoder function is related to a first interference signal strength.

[0096] The transceiver unit can perform the receiving and sending processes in the foregoing fifth aspect, and the processing unit of the communication device can perform other processes in the foregoing fifth aspect except for receiving and sending.

[0097] In a twelfth aspect, a communication device is provided, which includes: a transceiver unit, configured to receive second indication information from a 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 is configured to process channel state information (CSI), and the first encoder model or the first encoder function is related to a first interference signal strength.

[0098] The transceiver unit can perform the receiving and sending processes in the foregoing fourth aspect, and the processing unit of the communication device can perform other processes except receiving and sending in the foregoing sixth aspect.

[0099] In a thirteenth aspect, a communication device is provided, including a processing circuit configured to execute a computer program to cause the device to perform the methods in the foregoing first aspect to sixth aspect and any possible implementation manners thereof.

[0100] Optionally, the processing circuit is one or more processors, or all or part of one or more processors are circuits for processing functions.

[0101] Optionally, the communication device further includes a memory for storing the computer program, and the memory is one or more.

[0102] Optionally, the memory can be integrated with the processor, or the memory is separately provided from the processor, or the memory is located within the processor.

[0103] Optionally, the communication device further includes a transceiver circuit, such as a transceiver or an input / output circuit.

[0104] In a fourteenth aspect, a communication system is provided, including: a terminal device and a network device. The terminal device is configured to perform the methods in the foregoing third aspect or fifth aspect and any possible implementation manners thereof, and the network device is configured to perform the methods in the foregoing fourth aspect or sixth aspect and any possible implementation manners thereof. It should be understood that this implementation manner can be understood as the first encoder model being deployed in the terminal device and the first decoder model being deployed in the network device.

[0105] 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.

[0106] In a fifteenth aspect, a communication system is provided, including: a first device and a second device. The second device is configured to perform the methods in the foregoing first aspect and any possible implementation manners thereof, and the first device is configured to perform the methods in the foregoing first aspect and any possible implementation manners thereof. It should be understood that this implementation manner can be understood as the first encoder model being deployed in the first device and the first decoder model being deployed in the second device.

[0107] 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.

[0108] In a sixteenth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program or code, which, when running on a computer, causes the computer to execute the methods in the above first aspect to sixth aspect and any possible implementation manners thereof.

[0109] In a seventeenth aspect, a chip is provided, including at least one processing circuit for running a computer program, so that the chip executes the methods in the above first aspect to sixth aspect and any possible implementation manners thereof.

[0110] Wherein, 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.

[0111] In an eighteenth aspect, a computer program product is provided. The computer program product includes: computer program code, which, when running on a computer, executes the methods in the above first aspect to sixth aspect and any possible implementation manners thereof. Description of the Drawings

[0112] Figure 1 and Figure 2 are schematic diagrams of a communication system applicable to the embodiments of the present application;

[0113] Figure 3 is a schematic block diagram of an autoencoder;

[0114] Figure 4 is a schematic diagram of an AI application framework;

[0115] Figure 5 is a schematic interaction flowchart of the first communication method provided by the embodiments of the present application;

[0116] Figure 6 is a schematic interaction flowchart of the second communication method provided by the embodiments of the present application;

[0117] Figure 7 is a schematic interaction flowchart of the third communication method provided by the embodiments of the present application;

[0118] Figure 8 is a schematic interaction flowchart of the fourth communication method provided by the embodiments of the present application;

[0119] Figure 9 is a schematic interaction flowchart of the fifth communication method provided by the embodiments of the present application;

[0120] Figure 10 is a schematic block diagram of a communication device provided by the embodiments of the present application;

[0121] Figure 11 It is a schematic block diagram of another communication device provided by an embodiment of the present application. Detailed implementation manners

[0122] Next, the technical solutions in the present application will be described with reference to the accompanying drawings.

[0123] The technical solutions provided by the present application can be applied to various communication systems, such as: the fifth generation (5G) or new radio (NR) system, the long term evolution (LTE) system, the LTE frequency division duplex (FDD) system, the LTE time division duplex (TDD) system, the wireless local area network (WLAN) system, the satellite communication system, future communication systems, such as the sixth generation mobile communication system, or a fusion system of multiple systems, etc. The technical solutions provided by the present 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 the Internet of Things (IoT) communication system or other communication systems.

[0124] A device in a communication system can send a signal to another device or receive a signal from another device. The signal can include information, signaling, data, etc. Herein, the device can also be replaced with an entity, a network entity, a communication device, a communication module, a node, a communication node, etc., and the device is used as an example for description in the present application. For example, a communication system can include at least one terminal device and at least one network device. The network device can send a downlink signal to the terminal device, and / or the terminal device can send an uplink signal to the network device. It can be understood that the terminal device / network device in the present application can be replaced with a terminal device, and the network device executes the corresponding communication method in the present application.

[0125] 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 applied to 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 wearables, smart transportation, smart city drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and other scenarios. 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 can be a user equipment (UE), 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 laptop computer, a wireless modem, a handset, a laptop computer, a computer with wireless transceiver functions, 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-helicopter, a quad-copter, or an airplane, etc.), a ship, a remote control device, a smart home device, an industrial device, or a device built into the above devices (such as a communication module, a modem, or a chip in the above devices), or other processing devices connected to a wireless modem. For the convenience of description, the terminal device will be described below by taking the terminal or UE as an example.

[0126] It should be understood that in some scenarios, the UE can also be used as a base station. For example, the UE can act as a scheduling entity, which provides sidelink signals between UEs in scenarios such as V2X, D2D, or P2P.

[0127] In the embodiments of the present application, the device for implementing the functions of the terminal device may be the terminal device itself, or a device capable of supporting the terminal device to implement such functions, such as a chip system or a chip, and this device may be installed in the terminal device. In the embodiments of the present application, the chip system may be composed of chips, or may include chips and other discrete devices.

[0128] The network device in the embodiments of the present application may be a device for communicating with the terminal device. This network device may include an access network device or a radio access network device. For example, the network device may be 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 the wireless network. The base station may generally cover various names as follows, or be replaced with the following names, such as: Node B, evolved Node B (eNB), next generation Node B (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, slave station, multi-mode radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, building base band 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 may 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 may also refer to a communication module, a modem or a chip disposed in the foregoing device or apparatus. The base station may also be a mobile switching center and a device that undertakes the base station function in D2D, V2X, M2M communications, a network-side device in a 6G network, a device that undertakes the base station function in a future communication system, etc. The base station may support networks of the same or different access technologies. The embodiments of the present application do not limit the specific technologies and specific device forms adopted by the network device.

[0129] A base station can be fixed or mobile. For example, a helicopter or a drone can be configured to act as a mobile base station, and one or more cells can move according to the location of the mobile base station. In other examples, a helicopter or a drone can be configured to be used as a device for communicating with another base station.

[0130] In some deployments, multiple RAN nodes cooperate to assist a terminal in achieving wireless access, and different RAN nodes respectively implement some functions of the base station. For example, the RAN node can be a CU, a DU, a central unit-control plane (CU-CP), a central unit-user plane (CU-UP), or an RU, etc. The CU and the DU can be set separately, or can also be included in the same network element, such as a BBU. The radio unit (RU) can be included in a radio device or a radio unit, such as included in an RRU, an AAU, or an RRH.

[0131] The RAN node can support one or more types of fronthaul interfaces. Different fronthaul interfaces respectively correspond 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 of the baseband functions, and the RU is configured to implement one or more of the radio frequency functions. If the fronthaul interface between the DU and the RU is another interface, compared with the CPRI, some of the downlink and / or uplink baseband functions, for example, for the downlink, one or more of precoding, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP), are moved from the DU to the RU for implementation. For the uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / removing cyclic prefix (CP) are moved from the DU to the RU for implementation. In a possible implementation manner, this interface can be an enhanced common public radio interface (eCPRI). In the eCPRI architecture, the splitting method between the DU and the RU is different, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0132] Taking eCPRI Cat A as an example, for downlink transmission, with layer mapping as the division, the DU is configured to implement layer mapping and one or more functions before it (i.e., one or more of encoding, rate matching, scrambling, modulation, or layer mapping), while other functions after layer mapping (such as one or more of resource element (RE) mapping, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP)) are moved to the RU for implementation. For uplink transmission, with de-RE mapping as the division, the DU is configured to implement demapping and one or more functions before it (i.e., one or more of decoding, derate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, or de-RE mapping), while other functions after demapping (such as one or more of digital BF or fast Fourier transform (FFT) / removing CP) are moved to the RU for implementation. It can be understood that for the function descriptions of the DU and RU corresponding to various types of eCPRI, reference can be made to the eCPRI protocol, which will not be elaborated here.

[0133] In a possible design, the processing unit in the BBU for implementing baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH for implementing baseband functions is called the baseband low (BBL) unit.

[0134] In different systems, the 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, the CU may also be called the O-CU (open CU), the DU may also be called the O-DU, the CU-CP may also be called the O-CU-CP, the CU-UP may also be called the O-CU-UP, and the RU may also be called the O-RU. Any one of the CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0135] In the embodiments of the present application, the device for implementing the functions of a network device may be a network device or a device capable of supporting the network device to implement such functions, such as a chip system or a chip, and this device may be installed in the network device. In the embodiments of the present application, the chip system may be composed of chips or may include chips and other discrete devices.

[0136] The network device and the terminal device may be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; may also be deployed on water; and may further be deployed on airplanes, balloons, and satellites in the air. In the embodiments of the present application, the scenarios where the network device and the terminal device are located are not limited. In addition, the terminal device and the network device may be hardware devices or software functions running on dedicated hardware or software functions running on general hardware. For example, they are virtualized functions instantiated on a platform (such as a cloud platform), or entities including dedicated or general hardware devices and software functions. The present application does not limit the specific forms of the terminal device and the network device.

[0137] The network device may further include core network devices, such as an access and mobility management function (AMF), or an operations, administration, and maintenance device (OAM), or a third-party device, such as an over the top (OTT) device or a cloud server, etc., or a device provided with an AI module, such as a RAN intelligent controller (RIC).

[0138] In the embodiments of the present application, the terminal device may be a terminal device or a component of the terminal device (such as a chip or a circuit).

[0139] Optionally, the network device may be a network device provided with one or more AI modules. For example, the network device may be one or more devices among core network devices, radio access network (RAN) nodes, or OAMs. 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, etc. For example, the near real-time RIC is set in a RAN node (such as a CU or a DU), and the non-real-time RIC is set in an OAM, a cloud server, a core network device, or other network devices.

[0140] First, a communication system applicable to the embodiments of the present application is briefly introduced as follows.

[0141] Figure 1is a schematic diagram of a wireless communication system 100 applicable to the embodiments of the present application. As Figure 1 shown, the wireless communication system includes a radio access network 100. The radio access network 100 may be a next-generation (e.g., 6G or higher) radio access network or a traditional (e.g., 5G, 4G, 3G, or 2G) radio access network. One or more terminal devices (120a - 120j, collectively referred to as 120) may be interconnected or connected to one or more network devices (110a, 110b, collectively referred to as 110) in the radio access network 100. Figure 1 This is just a schematic diagram. The wireless communication system may also include other devices, such as core network devices, wireless relay devices, and / or wireless backhaul devices, etc., which are not Figure 1 shown in the figure.

[0142] In practical applications, the wireless communication system may include multiple network devices simultaneously, or may include multiple terminal devices simultaneously, without limitation. One network device may serve one or more terminal devices simultaneously. One 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.

[0143] Figure 2 is a schematic diagram of a wireless communication system 200 applicable to the embodiments of the present application. As Figure 2 shown, the wireless communication system 200 may include at least one network device, such as Figure 2 the network device 210 shown, and the wireless communication system 200 may also include at least one terminal device, such as Figure 2 the terminal device 220 and the terminal device 230 shown. The wireless communication system 200 may also include an AI network element (also referred to as an AI entity), such as Figure 2 the AI network element 240 shown, for performing AI-related operations, such as constructing a training data set or training an AI model, etc.

[0144] In a possible implementation, the network device 210 may send data related to the training of the AI model to the AI network element 240. The AI network element 240 constructs a training data set and trains the AI model. For example, the data related to the training of the AI model may include the data reported by the terminal device. The AI network element 240 may send the results of 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 operations related to the AI model may include at least one of the following: the AI model that has completed training, the evaluation result of the model, or the test result, etc. Exemplarily, a part of the AI model that has completed training may be deployed on the network device 210, and another part may be deployed on the terminal device. Alternatively, the AI model that has completed training may be deployed on the network device 210. Or, the AI model that has completed training may be deployed on the terminal device.

[0145] It should be understood that Figure 2 only the example where the AI network element 240 is directly connected to the network device 210 is described. In other scenarios, the AI network element 240 may also be connected to the terminal device. Or, the AI network element 240 may be connected to both the network device 210 and the terminal device at the same time. Or, the AI network element 240 may also be connected to the network device 210 through a third-party network element. The embodiments of the present application do not limit the connection relationship between the AI network element and other network elements.

[0146] The AI network element 240 may also be set as a module in the network device and / or the terminal device. For example, it may be set in Figure 1 the network device 110b or the terminal device shown.

[0147] Optionally, the AI network element 240 may be different modules of the same device as the network device 210, or may be separate different devices. It should be noted that Figure 1 and Figure 2 are only simplified schematic diagrams for easy understanding. For example, the communication system may further include other devices, such as wireless relay devices and / or wireless backhaul devices, etc., Figure 1 and Figure 2 which are not shown in the figure. In practical applications, the communication system may include multiple network devices or 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.

[0148] To facilitate the understanding of the embodiments of the present application, the terms involved in the embodiments of the present application are briefly described below.

[0149] (1) AI model;

[0150] An AI model is an algorithm or computer program that can implement AI functions. The AI model represents the mapping relationship between the input and output of the model. In other words, the AI model is a function model that maps inputs of a certain dimension to outputs of a certain dimension, and 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. m and n are the parameters of this AI model, and m and n can be obtained through machine learning training. Exemplarily, the AI models mentioned in the embodiments below of this application 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.

[0151] It can be understood that the implementation of the AI model can be a hardware circuit, software, or a combination of software and hardware, without limitation. Non-limiting examples of software include: program code, programs, subprograms, instructions, instruction sets, code, code segments, software modules, application programs, or software applications, etc.

[0152] (2) Machine learning (ML);

[0153] ML is an implementation method of artificial intelligence. Machine learning is a method that can endow machines with the ability to learn, so that machines can complete functions that cannot be completed by direct programming. In a practical sense, machine learning is a method of using data to train a model and then using the model for prediction. There are many machine learning methods, such as neural networks (NNs), decision trees, support vector machines, etc. Machine learning theory mainly designs and analyzes algorithms that allow computers to learn automatically. Machine learning algorithms are a class of algorithms that automatically analyze and obtain rules from data and use the rules to predict unknown data.

[0154] (3) Neural network (NN);

[0155] A neural network is a specific implementation form of AI or machine learning. According to the universal approximation theorem, a neural network can theoretically approximate any continuous function, enabling the neural network to have the ability to learn any mapping.

[0156] A neural network can be composed of neural units, and a neural unit can refer to an operation unit that takes xs and an intercept of 1 as inputs. A neural network is a network formed by connecting many such single neural units together, that is, the output of one neural unit can be the input of another neural unit. The input of each neural unit can be connected to the local receptive field of the previous layer to extract the features of the local receptive field, and the local receptive field can be a region composed of several neural units.

[0157] Taking the type of the AI model as a neural network as an example, the AI model involved in this application can be a deep neural network (DNN). According to the construction method of the network, DNN can include a feedforward neural network (FNN), a convolutional neural network (CNN), a recurrent neural network (RNN), etc.

[0158] (4) Auto-encoders (AE);

[0159] An auto-encoder AE is a neural network for unsupervised learning. Its characteristic is that it takes the input data as the labeled data. Therefore, AE can also be understood as a neural network for self-supervised learning. AE can be used for data compression and recovery. Exemplarily, the encoder in AE can perform compression (encoding) processing on data A to obtain data B, and the decoder in AE can perform decompression (decoding) processing on data B to recover data A. Or it can be understood that the decoder is the inverse operation of the encoder.

[0160] Exemplarily, the AI model in the embodiments of this application can include an encoder and a decoder. The encoder and the decoder are used in a matching manner, and it can be understood that the encoder and the decoder are a supporting pair of AI models. The encoder and the decoder can be respectively deployed on a terminal device and a network device. Optionally, the AI model in the embodiments of this application can be a single-end model, and this AI model can be deployed on a terminal device or a network device.

[0161] (5) Two-side model;

[0162] The two-side model can also be called a bilateral model, a collaborative model, or a dual model, etc. The two-side model refers to a model composed of a combination of multiple sub-models. The multiple sub-models that make up this model need to match each other. These multiple sub-models can be deployed in different nodes.

[0163] The embodiments of the present application relate to an encoder for compressing CSI and a decoder for restoring compressed CSI. The encoder and the decoder are used in a matching manner. It can be understood that the encoder and the decoder are a supporting AI model. An encoder may include one or more AI models, and the decoder that matches 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 they correspond one by one.

[0164] In a possible design, a set of matching encoder and decoder can be specifically two parts in the same AE. As Figure 3 shown, the encoder and the decoder are respectively deployed on different nodes. The AE model is a typical bilateral model. The encoder and the decoder of the AE model are usually co-trained and can be used in a matching manner. For example, the encoder can process the input V to obtain the processed result z, and the decoder can decode the output z of the encoder into the desired output V'. That is to say, the CSI feedback can be realized based on the AI model of AE. For example, the UE side compresses and quantifies the CSI through the encoder, and the base station restores 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 restored CSI. And the training of the model requires the CSI fed back by the UE as the ground truth label of the restored CSI.

[0165] (6) Training data set and inference data;

[0166] In the field of machine learning, the ground truth usually refers to the data that is considered accurate or real data.

[0167] The training data set is used for the training of the AI model. The training data set may include the input of the AI model, or include the input and target output of the AI model. Among them, the training data set includes one or more training data. The training data may include the training samples input to the AI model, or may include the target output of the AI model. Among them, the target output may also be referred to as a label, sample label or labeled sample. The label is the ground truth.

[0168] In the field of communication, the training data set may include simulation data collected through a simulation platform, or may include experimental data collected in an experimental scenario, or may also include measured data collected in an actual communication network. Due to differences in the geographical environment and channel conditions where the data is generated, for example, differences in indoor, outdoor, moving speed, frequency band or antenna configuration, etc., when acquiring data, the collected data can be classified. For example, data with the same channel propagation environment and antenna configuration is classified into one category.

[0169] Model training essentially involves learning certain features from the training data. During the process of training an AI model (such as a neural network model), since we hope the output of the AI model is as close as possible to the value we truly want to predict, we can compare the predicted value of the current network with the true target value, and then update the weight vector of each layer of the AI model based on the difference between the two. (Of course, there is usually an initialization process before the first update, that is, pre-configuring parameters for each layer in the AI model.) For example, if the predicted value of the network is too high, we adjust the weight vector to make it predict lower, and keep adjusting until the AI model can predict the true target value or a value very close to the true target value. Therefore, it is necessary to pre-define "how to compare the difference between the predicted value and the target value", which is the loss function or objective function. They are important equations for measuring the difference between the predicted value and the target value. Among them, taking the loss function as an example, the higher the output value (loss) of the loss function, the greater the difference. Then the training of the AI model becomes a process of minimizing this loss as much as possible, making the value of the loss function less than a threshold, or making the value of the loss function meet the target requirements. For example, if the AI model is a neural network, adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers of the neural network, the width, the weights of the neurons, or the parameters in the activation function of the neurons.

[0170] Inference data can be used as the input of the trained AI model for inference of the AI model. During the model inference process, when the inference data is input into the AI model, the corresponding output, which is the inference result, can be obtained.

[0171] The design of the AI model mainly includes a data collection link (such as collecting training data and / or inference data), a model training link, and a model inference link. Further, it can also include an inference result application link.

[0172] Figure 4 is an AI application framework.

[0173] In the foregoing 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. Among them, the AI model represents the mapping relationship between the input and output of the model. Obtaining the AI model through the model training node is equivalent to learning the mapping relationship between the input and output of the model 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, and an inference result is obtained. This phase can also be understood as: inputting the inference data into the AI model, and obtaining the output through the AI model, and this output 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 uniformly planned by the execution entity. For example, the execution entity can send the inference result to one or more execution objects (such as access network devices or terminal devices, etc.) for execution. Another example is that the execution entity can also feedback the performance of the model to the data source to facilitate subsequent implementation of model update training.

[0174] It can be understood that a network element with artificial intelligence capabilities may be included in a communication system. The above-mentioned processes related to AI model design can be executed by one or more network elements with artificial intelligence capabilities. In a possible design, AI capabilities (such as an AI module or an AI entity) can be configured within existing network elements in the communication system to implement AI-related operations, such as the training and / or inference of an AI model. For example, the existing network element can be an access network device or a terminal device, etc. Or in another possible design, an independent network element can also be introduced into the communication system to execute AI-related operations, such as training an AI model. This independent network element can be referred to as an AI network element (or an AI node, an AI entity, etc.), and the embodiments of this application do not limit this name. Exemplarily, this AI network element can be directly connected to the access network device in the communication system, or can be indirectly connected to the access network device through a third-party network element. Among them, the third-party network element can be a network device such as an authentication management function (AMF) network element, a user plane function (UPF) network element, operation administration and maintenance (OAM), a server (such as a cloud server), or other network elements, without limitation. Exemplarily, this independent AI network element can be deployed on one or more of the access network device side, the terminal device side, or the core network side. Optionally, it can be deployed on a server, such as a cloud server, or on an over the top (OTT) device. For example, Figure 2 The AI network element 240 in the shown communication system.

[0175] The training processes of different models can be deployed in different devices or nodes, or can also be deployed in the same device or node. The inference processes of different models can be deployed in different devices or nodes, or can also be deployed in the same device or node. Taking the terminal device to complete the model training process as an example, after the terminal device trains the supporting encoder and decoder, it can send the model parameters of the decoder to the network device. Taking the network device to complete the model training process as an example, after the network device trains the supporting encoder and decoder, it can indicate the model parameters of the encoder to the terminal device. Taking the independent AI network element to complete the model training process as an example, after the AI network element trains the supporting encoder and decoder, it can send the model parameters of the encoder to the terminal device and send the model parameters of the decoder to the network device. Then, the model inference process corresponding to the encoder is performed in the terminal device, and the model inference process corresponding to the decoder is performed in the network device.

[0176] Among them, the model parameters may include one or more of the following: structural parameters of the model (such as the number of layers of the model, and / or weights, etc.), input parameters of the model (such as input dimension, number of input ports), or 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.

[0177] (7) Channel information;

[0178] In a communication system (such as, for example, an LTE communication system or an NR communication system, etc.), the network device needs to determine configurations such as resources, MCS, and precoding of the downlink data channel for scheduling the terminal device based on the channel information. It can be understood that the channel information can reflect channel characteristics, channel quality, etc. The channel information may also be referred to as channel state information (CSI), or, channel environment information. It should 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), CSI-RS resource indicator (CRI), and may 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), etc.

[0179] CSI measurement refers to the receiving end solving the channel information based on the reference signal sent by the transmitting end, that is, estimating the channel information by using the channel estimation method. Exemplarily, the reference signal may include at least one of the following: channel state information reference signal (CSI-RS), synchronizing signal / physical broadcast channel block (SSB), sounding reference signal (SRS), or demodulation reference signal (DMRS). CSI-RS, SSB, and DMRS, etc. can be used to measure downlink CSI, and SRS and DMRS, etc. can be used to measure uplink CSI.

[0180] In the FDD communication scenario, since the uplink and downlink channels do not have reciprocity, or rather, the reciprocity of the uplink and downlink channels cannot be guaranteed, the network device usually sends a downlink reference signal to the terminal device, and the terminal device performs channel measurement and interference measurement based on the received downlink reference signal to estimate the downlink CSI. The terminal device generates a CSI report according to the protocol-predefined method or the method configured by the network device, and feeds it back to the network device so that it can obtain the downlink CSI.

[0181] 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 can also be referred to as the signal-to-interference-and-noise ratio. Among them, RI is used to indicate the number of layers of the downlink transmission recommended by the terminal device, CQI is used to indicate the modulation and coding method that the terminal device judges can be supported by the current channel conditions, and PMI is used to indicate the precoding recommended by the terminal device. The number of layers of the precoding indicated by PMI corresponds to RI.

[0182] It should be understood that the RI, CQI, PMI, etc. indicated in the above CSI report are only recommended values of the terminal device, and the network device may perform downlink transmission according to some or all of the information indicated in the CSI report. Alternatively, the network device may also not perform downlink transmission according to the information indicated in the CSI report.

[0183] Exemplarily, introducing AI technology into a wireless communication network has produced a CSI feedback method based on an AI model. The terminal device uses the AI model to compress and feedback CSI, and the network device uses the AI model to recover the compressed CSI. In the AI CSI feedback, the terminal device transmits a sequence (such as a bit sequence), and compared with the traditional CSI feedback method, the overhead of CSI is reduced.

[0184] Take Figure 3 as an example, Figure 3 the encoder in

[0185] can be called a CSI generator, and the decoder can be called a CSI reconstructor. For example, the encoder can be deployed in the terminal device, and the decoder can be deployed in the network device. The terminal device can generate CSI feedback information z from the original CSI information V through the encoder. The terminal device reports a CSI report, and this CSI report may include the CSI feedback information z. The network device can reconstruct the CSI information through the decoder, that is, obtain the CSI recovery information V'.

[0186] Next, the training process and inference process of the AI model in the embodiments of the present application will be further described exemplarily.

[0187] The training data for training the AI model includes training samples and sample labels. Exemplarily, the training samples are the channel information determined by the terminal device, and the sample labels are the real channel information, that is, the true value CSI. For the case where the encoder and the decoder belong to the same autoencoder, the training data may only include training samples, or in other words, the training samples are the sample labels.

[0188] In the field of wireless communication, the true CSI can be understood as high-precision CSI. The specific training process includes: the model training node processes the channel information, i.e., the training samples, using an encoder to obtain CSI feedback information, and processes the feedback information using a decoder to obtain the restored channel information, i.e., CSI restoration information. Then, the difference between the CSI restoration information and the corresponding sample label is calculated, i.e., the value of the loss function. The parameters of the encoder and decoder are updated according to the value of the loss function to minimize the difference between the restored channel information and the corresponding sample label, i.e., to minimize the loss function. Exemplarily, the loss function can be the mean square error (MSE) or cosine similarity. By repeating the above operations, the encoder and decoder that meet the target requirements can be obtained. The above model training node can be a terminal device, a network device, or other network elements with AI capabilities in the communication system.

[0189] It should be understood that the above is only an example of using an AI model for CSI compression. In CSI feedback, the AI model can also be used in other scenarios. For example, the AI model can be used for CSI prediction, i.e., predicting the channel information at one or more future moments based on the channel information measured at one or more historical moments. The embodiments of the present application do not limit the specific use of the AI model in the CSI feedback scenario.

[0190] Model identification is to enable the network device and the UE to have a common understanding of an AI model. Usually, the UE needs to register the model with the network device, i.e., the UE notifies the network device that the UE has an AI model, as well as information such as the function, application scenario, and corresponding configuration of the AI model. The network device configures a model identifier (ID) for the UE, and then the UE and the network device 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.

[0191] Function identification is to enable the network device and the UE to have a common understanding of an AI function. An AI function usually does not specifically refer to a particular AI model, but rather refers to a class of AI models with the same function, i.e., an AI function can correspond to multiple AI models. Usually, the UE needs to report to the network device the AI functions supported by the UE, as well as information such as the application scenario and corresponding configuration of each AI function. The network device can configure an AI function ID for the UE, and then 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. Or, the network device can use the application scenario and / or corresponding configuration corresponding to the AI function to manage the AI function.

[0192] It is found that when a dual - end model is selected for CSI feedback using a model ID or a function ID, the network performance corresponding to the same AI model or AI function is poor at some moments, and thus good CSI feedback performance cannot be obtained.

[0193] Based on this, embodiments of the present application provide a communication method and a communication device, which select and match models or functions while considering the interference signal strength, in order to improve CSI feedback performance.

[0194] The communication method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments provided by the present application can be applied to the above - mentioned Figure 1 or Figure 2 shown communication system, without limitation. The present application proposes the following Figures 5 to 9 shown method. It should be understood that Figures 5 to 9 the method embodiments shown can be combined with each other, Figures 5 to 9 and the steps in the method embodiments shown can be referenced to each other. In the embodiments of the present application, Figures 6 to 9 the method embodiments shown can be regarded as possible implementation manners for realizing the functions of Figure 5 the method embodiments shown.

[0195] Figure 5 is a schematic flowchart of the communication method 500 provided by the embodiments of the present application. As Figure 5 shown, this method process can be executed by a first device and a second device. Among them, 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 the terminal device or the network device, or a functional module / device in the terminal device or the 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 inference network element, such as OAM, OTT, or a cloud server, etc. 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 1); or the first device as a network device and the second device as a terminal device (Case 2) as examples. The method includes the following multiple steps.

[0196] Case 1: The first device is a terminal device and the second device is a network device.

[0197] S510, the second device obtains the first interference signal strength.

[0198] 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, with the unit of decibel relative to one milliwatt (dBm). Alternatively, the first interference signal strength can be represented by the value of the signal-to-interference plus noise ratio (SINR), with the unit of decibel (dB). In this application, the first interference information can be a specific interference power value, such as 8 dBm, or the first interference information can be a specific SINR value, such as 8 dB. Therefore, unless otherwise emphasized, the first interference information in the embodiments of this 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), simply 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.

[0199] In the first example, the second device obtains the first interference signal strength from the first device. For example, the first device sends the first interference information to the second device, where the first interference information is used to characterize the first interference signal strength. Correspondingly, the second device receives the first interference information from the first device.

[0200] It should be understood that when the first device sends the first interference information to the second device, it can be the first device sending the first interference information itself, or the first device sending the identifier or index information corresponding to the first interference information to the second device. This application does not specifically limit the sending method of the first interference information, as long as the second device can obtain the first interference signal strength.

[0201] Optionally, before the second device receives the first interference information from the first device, or rather, before the first device sends the first interference information to the second device, the method further includes: the second device can send configuration information to the first device, and the configuration information is used to indicate the first reference signal. Correspondingly, 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, that is, the first interference information is obtained based on the channel measurement of the first reference signal.

[0202] Exemplarily, the configuration information can include one or more of the following: CSI resource configuration ID (CSI-ResourceConfigId), CSI-RS resource set table (CSI-RS-ResourceSetList), or the time-domain behavior of CSI measurement (resourceType).

[0203] Exemplarily, the first reference signal includes one or more of the following:

[0204] (1) Channel State Information Reference Signal (CSI-RS);

[0205] Exemplarily, CSI-RS is used for downlink channel estimation or measurement to obtain CSI, such as RI, CQI, or PMI, etc.

[0206] (2) Zero Power Channel State Information reference signal (ZP CSI-RS);

[0207] It should be understood that on the resources configured with this ZP CSI-RS, the gNB does not send CSI-RS reference signals and the power is 0.

[0208] (3) Channel State Information Interference Measurement (CSI-IM) signal;

[0209] Exemplarily, in a multiple input multiple output (MIMO) system, a type of resource specifically defined to estimate the interference signal strength is called CSI-IM. CSI-IM is not a downlink reference signal and its function is for interference measurement. The serving base station does not send any signals on the resources configured with CSI-IM. The interference signals measured by the UE on CSI-IM come from neighboring cells or the background noise. For example, the UE statistically analyzes the received interference and noise intensity, calculates the signal-to-interference-plus-noise ratio (SINR) based on this, determines the block error rate (BLER) corresponding to the SINR, and reports the corresponding CQI according to the limit of BLER < 10%.

[0210] In the second example, the second device can locally obtain the first interference signal strength. For example, the second device can retrieve or predict the first interference information locally; or, the second device can retrieve or predict the first interference information through the cloud. Among them, the first interference information is used to indicate the first interference signal strength.

[0211] Among them, local retrieval or prediction may refer to: the second device outputs the analysis result in the form defined by the specification through artificial intelligence and big data analysis. Generally, the output analysis result includes two forms, one is the statistical analysis of historical data, and the other is the prediction of future data.

[0212] Retrieval means that the second device can search for interference information corresponding to a time point in a stored past time period through locally recorded past historical data, and use this interference information as the current interference information, that is, the first interference information. For example, the interference information at 10:00 am on January 1, 2023 can be equivalent to the interference information at 10:00 am on January 1, 2022. For instance, the stored interference information here can be stored locally by the network device or non-locally, such as stored in the core network, ORAN, or the cloud.

[0213] Prediction means that the second device can predict the interference it receives to obtain a predicted value of the interference information. Among them, the method used for prediction can be that the second device predicts through a neural network trained by artificial intelligence. The training data involved in the training process can be the relevant data of the interference information collected by the network device before, or alternatively, it can be the relevant data for the second device to use a prediction function obtained by fitting based on big data analysis, where the fitted data can be the relevant data of the interference information collected by the network device before; or the second device can predict the service load of itself to obtain a predicted value of the service load, and then determine the predicted value of the interference information based on the high or low situation of the service load. It should be understood that a high service load indicates large interference, and a low service load indicates small 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.

[0214] S520, the second device sends the first indication information to the first device. Correspondingly, the first device receives the first indication information from the second device.

[0215] 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 compress or quantize the CSI. That is, 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, "indicate... encoder..." can be replaced with "indicate... decoder...".

[0216] It should be noted that the first encoder corresponds to the first decoder. 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 co-trained and can be used in matching. The number of AI models included in the first encoder model and the first decoder model used in matching is the same and they correspond one by one. The first encoder model and the first decoder model can be understood as a set of 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 restoring the compressed CSI.

[0217] Exemplarily, the first encoder function includes one or more first encoder models. It should be understood that the AI function generally does not specifically refer to a certain specific AI model, but refers to a class of AI models with the same function, that is, the AI function can correspond to multiple AI models. That is to say, 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 one or more first encoder models through the first encoder function.

[0218] Exemplarily, 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.

[0219] Among them, the identifier of the first encoder model can be the model ID, the identifier of the first encoder function can be the function ID, and the model parameters of the first encoder model, or the model parameters corresponding to the first encoder function can include one or more of the following:

[0220] (1) The structural parameters of the model;

[0221] For example, the structural parameters of the model include at least one of the following: the number of layers of the neural network, the width, the weights of the neurons, or the parameters in the activation function of the neurons, etc.

[0222] (2) The input parameters of the model;

[0223] For example, the input parameters of the model include the input dimension and / or the number of input ports, etc. It can be understood that the input dimension can refer to the size of an input data. For example, when the input data is a sequence, the input dimension corresponding to the sequence can indicate the length of the sequence. The number of input ports can refer to the number of input data.

[0224] (3) The output parameters of the model;

[0225] For example, the output dimension, and / or the number of output ports, etc. It can be understood that the output dimension can refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence can indicate the length of the sequence. The number of output ports can refer to the quantity of the output data.

[0226] Optionally, before performing the above step S520, the second device acquires model library information.

[0227] Wherein, 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 the 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 the multiple encoder functions. The first interference signal strength is included in one of the multiple interference signal strength ranges.

[0228] In one example, the second device acquires the model library information from the first device. For example, the first device sends the model library information to the second device. Correspondingly, the second device receives the model library information from the first device. Optionally, the second device can also acquire the model library information from a model training network element, a model storage network element, or a model inference network element, such as OTT, or a cloud server, etc. The present application does not limit the specific implementation manner for the second device to acquire the model library information.

[0229] In the present application, the first encoder model or the first encoder function is related to the first interference signal strength. It can be understood that: the first encoder model or the first encoder function is determined according to the first interference signal strength. For example, after the second device determines the first interference signal strength in step S510, the first encoder model corresponding to the first interference signal strength can be determined by combining the model library information; or, after the second device determines the first interference signal strength in step S510, the first encoder function corresponding to the first interference signal strength can be determined by combining the model library information.

[0230] 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 by signaling or pre-configured. Among them, predefined can include predefined in advance, such as protocol definition. Pre-configuration can be implemented by pre-saving corresponding codes, tables, strings, or other ways that can be used to indicate relevant information in the device. The present application does not limit its specific implementation manner.

[0231] Optionally, the mapping relationship can exist in the form of a table, a function, a text, or a string, such as storage or transmission.

[0232] 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 interference power, the greater the signal interference; the smaller the value of interference power, the smaller the signal interference. The smaller the value of SINR, the greater the signal interference; the larger the value of SINR, the smaller the signal interference. Optionally, the interference signal strength can also be characterized by other parameters, which are not limited in this application.

[0233] Next, an example of the mapping relationship between multiple encoder models indicated by the model library information and multiple interference signal strength ranges is given in the form of a table. Different interference signal strength ranges correspond to different encoder models, as shown in Table 1.

[0234] Table 1

[0235] Encoder model Interference signal strength range AI CSI feedback encoder model #1 Interference power range: -20dBm ≤ x < -10dBm AI CSI feedback encoder model #2 Interference power range: -10dBm ≤ x < 0dBm AI CSI feedback encoder model #3 Interference power range: 0 dBm ≤ x < 5 dBm AI CSI feedback encoder model #4 Interference power range: 5 dBm ≤ x < 10 dBm AI CSI feedback encoder model #5 Interference power range: 10 dBm ≤ x < 15 dBm AI CSI feedback encoder model #6 SINR range: -20 dB ≤ x < -10 dB AI CSI feedback encoder model #7 SINR range: -10dB ≤ x < 0dB AI CSI feedback encoder model #8 SINR range: 0 dB ≤ x < 5 dB AI CSI feedback encoder model #9 SINR range: 5 dB ≤ x < 10 dB

[0236] As can be seen from 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. Among them, the interference signal strength corresponding to AI CSI feedback encoder model #1 to AI CSI feedback encoder model #5 is characterized by the value of interference power, 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 model #6 to AI CSI feedback encoder model #9 is characterized by the value of SINR, and the corresponding SINR ranges are -20dB ≤ x < -10dB, -10dB ≤ x < 0dB, 0dB ≤ x < 5dB, and 5dB ≤ x < 10dB respectively.

[0237] For example, assume that the first interference signal strength obtained by the second device in step S510 is 12 dBm. This indicates 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. Then, the second device can carry the model ID and / or model parameters of AI CSI feedback encoder model #5 in the first indication information to indicate this AI CSI feedback encoder model #5. Another example, assume that the first interference signal strength obtained by the second device in step S510 is 4 dB. This indicates 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. Then, the second device can carry the model ID and / or model parameters of AI CSI feedback encoder model #8 in the first indication information to indicate this AI CSI feedback encoder model #8.

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

[0239] Optionally, this application does not limit the number of encoder models (such as AI CSI feedback encoder models) in Table 1. Or rather, this application does not limit the number of corresponding relationships between the encoder models in Table 1 and the interference signal strength ranges (such as one row in the table).

[0240] For example, AI CSI feedback encoder model #1 to AI CSI feedback encoder model #5, and AI CSI feedback encoder model #6 to AI CSI feedback encoder model #9 in Table 1 can be respectively independent into new tables; another example, AI CSI feedback encoder model #1 to AI CSI feedback encoder model #3, and AI CSI feedback encoder model #4 and AI CSI feedback encoder model #5 in Table 1 can also be respectively independent into new tables; yet another example, AI CSI feedback encoder model #6 and AI CSI feedback encoder model #7, and AI CSI feedback encoder model #8 and AI CSI feedback encoder model #9 in Table 1 can also be respectively independent into new tables. That is to say, Table 1 can be split into multiple other tables for illustration. This application does not limit this, and the splitting method is also not limited.

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

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

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

[0244] It should be noted that since the first encoder model matches the first decoder model, or rather, multiple first encoder models correspond to multiple first decoder models one by one, the mapping relationship between multiple decoder models and multiple interference signal strength ranges can also be uniquely determined according to Table 1 above, as shown in Table 3 below.

[0245] Table 3

[0246] Decoder model Interference signal strength range AI CSI feedback decoder model #1 Interference power range: -20dBm ≤ x < -10dBm AI CSI feedback decoder model #2 Interference power range: -10dBm ≤ x < 0dBm AI CSI feedback decoder model #3 Interference power range: 0 dBm ≤ x < 5 dBm AI CSI feedback decoder model #4 Interference power range: 5 dBm ≤ x < 10 dBm AI CSI feedback decoder model #5 Interference power range: 10 dBm ≤ x < 15 dBm AI CSI feedback decoder model #6 SINR range: -20 dB ≤ x < -10 dB AI CSI feedback decoder model #7 SINR range: -10 dB ≤ x < 0 dB AI CSI feedback decoder model #8 SINR range: 0 dB ≤ x < 5 dB AI CSI feedback decoder model #9 SINR range: 5 dB ≤ x < 10 dB

[0247] It should be noted that Table 3 is only an example given for easy understanding and does not constitute a limitation on the technical solution of the present application. Optionally, the above Table 1 and Table 3 can be implemented independently or in combination. For example, the above Table 1 and Table 3 can be combined into one table, and the present application does not make any limitation on this. For example, for a specific interference signal strength range, one or more rows in Table 1 can be presented in one table with the corresponding one or more rows in Table 3. For example, the mapping relationships of the first 5 rows in Table 1 and the first 5 rows in Table 3 can be combined into one table, and the mapping relationships of the last 4 rows in Table 1 and the last 4 rows in Table 3 can be combined into one table, and the present application does not make any limitation on this.

[0248] The following Table 2 gives an example of the mapping relationship between multiple encoder functions indicated by model library information and multiple interference signal strength ranges, where different interference signal strength ranges correspond to different encoder functions.

[0249] Table 2

[0250] Encoder function Interference signal strength range AI CSI feedback encoder function #1 Interference power range: -20dBm ≤ x < -10dBm AI CSI feedback encoder function #2 Interference power range: -10dBm ≤ x < 0dBm AI CSI feedback encoder function #3 Interference power range: 0 dBm ≤ x < 5 dBm AI CSI feedback encoder function #4 Interference power range: 5 dBm ≤ x < 10 dBm AI CSI feedback encoder function #5 SINR range: -20 dB ≤ x < -10 dB AI CSI feedback encoder function #6 SINR range: -10 dB ≤ x < 0 dB AI CSI feedback encoder function #7 SINR range: 0 dB ≤ x < 5 dB AI CSI feedback encoder function #8 SINR range: 5 dB ≤ x < 10 dB AI CSI feedback encoder function #9 SINR range: 10 dB ≤ x < 20 dB

[0251] As can be seen from Table 2, 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. Among them, the interference signal strength corresponding to AI CSI feedback encoder function #1 to AI CSI feedback encoder function #4 is characterized by the value of interference power, 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 characterized by the value of SINR, and the corresponding SINR ranges are -20dB ≤ x < -10dB, -10dB ≤ x < 0dB, 0dB ≤ x < 5dB, 5dB ≤ x < 10dB, and 10dB ≤ x < 20dB respectively.

[0252] For example, assume that the first interference signal strength obtained by the second device in step S510 is 6 dBm. This indicates that the first interference signal strength belongs to the interference power range of 5 dBm ≤ x < 10 dBm. Correspondingly, it can be determined that the first encoder function is the AICSI feedback encoder function #4. Then, the second device can carry the function ID of the AI CSI feedback encoder function #4 and / or the model parameters corresponding to the AI CSI feedback encoder function #4 in the first indication information to indicate the AI CSI feedback encoder function #4. Another example, assume that the first interference signal strength obtained by the second device in step S510 is 8 dB. This indicates that the first interference signal strength belongs to the SINR range of 5 dB ≤ x < 10 dB. Correspondingly, it can be determined that the first encoder function is the AI CSI feedback encoder function #8. Then, the second device can carry the model ID and / or the model parameters of the AI CSI feedback encoder function #8 in the first indication information to indicate the AI CSI feedback encoder function #8.

[0253] It should be noted that the above Table 2 is only an example given for easy understanding and does not constitute a limitation on the technical solution of the present application.

[0254] Optionally, the present application does not limit the number of encoder functions in Table 2 (such as the AI CSI feedback encoder function), or rather, the present application does not limit the number of corresponding relationships between the encoder functions and the interference signal strength ranges in Table 2 (such as one row in the table).

[0255] For example, the AI CSI feedback encoder function #1 to the AI CSI feedback encoder function #4, and the AI CSI feedback encoder function #5 to the AI CSI feedback encoder function #9 in Table 2 can be separately formed into new tables; or, the AI CSI feedback encoder function #1 and the AI CSI feedback encoder function #2, and the AI CSI feedback encoder function #3 and the AICSI feedback encoder function #4 in Table 2 can also be separately formed into new tables; or, the AI CSI feedback encoder function #5 to the AI CSI feedback encoder function #7, and the AI CSI feedback encoder function #8 and the AI CSI feedback encoder function #9 in Table 2 can also be separately formed into new tables. That is, Table 2 can be split into multiple other tables for illustration. The present application does not limit this, nor does it limit the splitting method.

[0256] Optionally, the present application does not limit the value range of the interference signal strength in Table 2, or rather, the present application does not specifically limit the interval size corresponding to the interference signal strength range in Table 2. Among them, the interval sizes corresponding to multiple interference signal strength ranges can be the same (equally spaced) or different (unequally spaced). 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.

[0257] For example, the interval sizes of the interference signal strength ranges corresponding to AI CSI feedback encoder function #1 and AI CSI feedback encoder function #2 in Table 2 are the same, which is 10 dBm; the interval sizes of the interference signal strength ranges corresponding to AI CSI feedback encoder function #3 and AI CSI feedback encoder function #4 are the same, which is 5 dBm; the interval sizes of the interference signal strength ranges corresponding to AI CSI feedback encoder function #1 and AI CSI feedback encoder function #3 are different; the interval sizes of the interference signal strength ranges corresponding to AI CSI feedback encoder function #5, AI CSI feedback encoder function #6, and AI CSI feedback encoder function #9 are the same, which is 10 dB; the interval sizes of the interference signal strength ranges corresponding to AI CSI feedback encoder function #7 and AI CSI feedback encoder function #8 are the same, which is 5 dB; the interval sizes of the interference signal strength ranges corresponding to AI CSI feedback encoder function #1 and AI CSI feedback encoder function #4 are different, and the interval sizes of the interference signal strength ranges corresponding to AI CSI feedback encoder function #6 and AI CSI feedback encoder function #8 are different. The present application does not limit this.

[0258] Optionally, each AI CSI feedback encoder function may include one or more AI CSI feedback encoder models.

[0259] Optionally, the interference power ranges corresponding to AI CSI feedback encoder function #1 to AI CSI feedback encoder model #4 in Table 2 above may be replaced with the first range to the fourth range, and the SINR ranges corresponding to AI CSI feedback encoder model #5 to AI CSI feedback encoder model #9 may be replaced with the fifth range to the ninth range, etc. The present application does not limit this.

[0260] It should be noted that since the first encoder function matches the first decoder function, or rather, multiple first encoder functions correspond to multiple first decoder functions one by one, the mapping relationship between multiple decoder functions and multiple interference signal strength ranges can also be uniquely determined according to Table 2 above, as shown in Table 4 below.

[0261] Table 4

[0262] Decoder function Interference signal strength range AI CSI feedback decoder function #1 Interference power range: -20dBm ≤ x < -10dBm AI CSI feedback decoder function #2 Interference power range: -10dBm ≤ x < 0dBm AI CSI feedback decoder function #3 Interference power range: 0 dBm ≤ x < 5 dBm AI CSI feedback decoder function #4 Interference power range: 5 dBm ≤ x < 10 dBm AI CSI feedback decoder function #5 SINR range: -20dB ≤ x < -10dB AI CSI feedback decoder function #6 SINR range: -10dB ≤ x < 0dB AI CSI feedback decoder function #7 SINR range: 0 dB ≤ x < 5 dB AI CSI feedback decoder function #8 SINR range: 5 dB ≤ x < 10 dB AI CSI feedback decoder function #9 SINR range: 10 dB ≤ x < 20 dB

[0263] It should be noted that Table 4 is only an example given for easy understanding and does not constitute a limitation on the technical solution of this application. Optionally, the above Table 24 and Table 4 can be implemented independently or in combination. For example, the above Table 2 and Table 4 can be combined into one table, and this application does not make any limitations in this regard. For example, for a specific interference signal strength range, one or more rows in Table 2 can be presented in one table with the corresponding one or more rows in Table 4. For example, the mapping relationships of the first 4 rows in Table 2 and the first 4 rows in Table 4 can be combined into one table, and the mapping relationships of the last 5 rows in Table 2 and the last 5 rows in Table 4 can be combined into one table, and this application does not make any limitations in this regard.

[0264] Optionally, the mapping relationship between the multiple encoder models shown in the above Table 1 and the multiple interference signal strength ranges, and the mapping relationship between the multiple encoder functions shown in Table 2 and the multiple interference signal strength ranges can be implemented independently or in combination. That is to say, the above Table 1 and Table 2 can be combined into one table, and this application does not make any limitations in this regard. For example, for a specific interference signal strength range, one or more rows in Table 1 can be presented in one table with the corresponding one or more rows in Table 2. For example, the mapping relationships of the first 4 rows in Table 1 and the first 4 rows in Table 2 can be combined into one table.

[0265] Similarly, the mapping relationship between the multiple decoder models shown in the above Table 2 and the multiple interference signal strength ranges, and the mapping relationship between the multiple decoder functions shown in Table 4 and the multiple interference signal strength ranges can be implemented independently or in combination. That is to say, the above Table 2 and Table 4 can be combined into one table, and this application does not make any limitations in this regard. For example, for a specific interference signal strength range, one or more rows in Table 2 can be presented in one table with the corresponding one or more rows in Table 4. For example, the mapping relationships of the first 4 rows in Table 2 and the first 4 rows in Table 4 can be combined into one table, and this application does not make any limitations in this regard.

[0266] Exemplarily, assume that the first interference signal strength obtained by the second device in step S510 is -8 dBm. According to the model library information shown in Table 1 or Table 2, the first interference signal strength belongs to the interference power range of -10 dBm ≤ x < 0 dBm. Then, the second device can determine that the first encoder model is AI CSI feedback encoder model #2, or the second device can determine that the first encoder function is AI CSI feedback encoder function #2. Further, in step S520, the second device can indicate the first encoder model or the first encoder function through the first indication information. For example, the second device can carry 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 to respectively indicate AI CSI feedback encoder model #2 and AI CSI feedback encoder function #2. Or, the second device can also carry one or more of the model parameters of AI CSI feedback encoder model #2 or the corresponding model parameters of AI CSI feedback encoder function #2 in the first indication information to respectively indicate AI CSI feedback encoder model #2 and AI CSI feedback encoder function #2.

[0267] That is to say, based on the first interference signal strength obtained in step S510 and the above model library information, the second device can determine the first encoder model or the first encoder function corresponding to the first interference signal strength, and indicate the first encoder model or the 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.

[0268] Optionally, for Tables 1 to 4 above, the mapping relationship between the encoder models and / or encoder functions shown in Table 1 and / or Table 3 and the interference signal strength range can be understood as the model library information on the terminal device side, which is used to determine the first encoder model and / or the first encoder function according to the first interference information (or the first interference signal strength). The decoder models and / or decoder functions shown in Table 2 and / or Table 4 can be understood as the model library information on the network device side, which is used to determine the first decoder model and / or the first decoder function according to the first interference information (or the first interference signal strength).

[0269] Based on the above steps S510 and S520, the first device and the second device have selected or matched the dual - end models or functions related to the first interference signal strength, such as the first encoder model and the first decoder model (which can be simply referred to as the dual - end model), or the first encoder function or the first decoder function (the models corresponding to the first encoder function and the first codec function can also be simply referred to as the dual - end model). Optionally, the first device can perform CSI feedback to the second device based on the dual - end model.

[0270] 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 can perform CSI feedback with the second device based on the dual - end model, thereby improving the CSI feedback performance.

[0271] Exemplarily, the second device sends the 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. Then, the first device can use the above - determined first encoder model or the model corresponding to the first encoder function to compress and quantize the first CSI to obtain the 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 above - determined first decoder model or the model corresponding to the first decoder function to decompress and de - quantize the feedback CSI to obtain the restored CSI (i.e., the second result).

[0272] 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 restored CSI (i.e., the second result).

[0273] It should be understood that the first encoder model and the first decoder model correspond to each other. The first encoder model and the first decoder model are usually co - trained and can be used in a matching manner. The two can be understood as a set of AI models. Similarly, the first encoder function and the first decoder function correspond to each other. For specific interpretations, reference can be made to the relevant descriptions above.

[0274] In the present application, the first CSI is related to the first interference signal strength. It can be understood that: after the second device obtains the first interference signal strength, it can send the first CSI - RS within the 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. Among them, the first time period should be as small as possible, such as within 10 ms, to ensure that the first encoder model or the model corresponding to the first encoder function corresponding to the first interference signal strength is applicable to compressing or quantizing the first CSI, thereby improving the CSI feedback performance.

[0275] Optionally, the first CSI is related to the first interference signal strength, and it can also be understood that: the first interference signal strength is obtained based on the measurement quantity of the first CSI - RS. For example, the first device measures the measurement quantity of the first CSI - RS to obtain the first CSI, where the first CSI includes the first interference information, and the first interference information is used to indicate the first interference signal strength.

[0276] Optionally, the first encoder model in the embodiments of the present application may be deployed on the first device side, and the first decoder model may be deployed on the second device side. For example, after the first device measures the first CSI, it uses the first encoder model to compress the first CSI to obtain the feedback CSI, and sends the feedback CSI to the second device. Correspondingly, after the second device obtains the feedback CSI, it uses the first decoder model to decompress the feedback CSI to obtain the restored CSI, so as to improve the CSI feedback performance.

[0277] Optionally, the first encoder model or the first decoder model in the embodiments of the present application may also be deployed on the third device side or the 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, an OTT, or a cloud server, etc. For example, after the first device measures the first CSI, it may send the first CSI to the third device, and the third device uses the first encoder model to compress the first CSI to obtain the feedback CSI, and sends the feedback CSI to the first device, and then the first device sends the feedback CSI to the second device. Correspondingly, after the second device obtains the feedback CSI, it may send the feedback CSI to the fourth device, and the fourth device uses the first decoder model to decompress the feedback CSI to obtain the restored CSI, and sends the restored CSI to the second device, so as to improve the CSI feedback performance.

[0278] Case 2: The first device is a network device and the second device is a terminal device.

[0279] S510, the second device obtains the first interference signal strength.

[0280] The meaning of the first interference signal strength may refer to the relevant description above and will not be elaborated here.

[0281] 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. Correspondingly, the second device receives the configuration information from the first device and performs channel measurement on the first reference signal according to the configuration information to obtain a channel measurement result, and the channel measurement result includes first interference information, and the first interference information is used to characterize the first interference signal strength. That is, the first interference information is obtained based on channel measurement of the first reference signal.

[0282] Among them, the configuration information is used to indicate a first reference signal. 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 time-domain behavior of CSI measurement (resourceType). The first reference signal includes one or more of the following: CSI-RS, ZP CSI-RS, or CSI-IM. Regarding the interpretation of the first reference signal, the association relationship between the first interference information and the first interference signal strength can refer to the above relevant description.

[0283] Optionally, the second device obtains the first interference signal strength from the first device. It can be that the first device actively indicates the first interference signal strength to the second device, or the first device indicates the first interference signal strength to the second device according to the request message of the second device. For example, before performing the above step S510, the second device sends a request message to the first device, and the request message is used to request the first device to send the configuration information. Correspondingly, after receiving the request message from the second device, the first device triggers to send the 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.

[0284] In the second example, the second device can obtain the first interference signal strength locally. For example, the second device can obtain the first interference signal strength by performing interference prediction or sensing operations. For example, the second device outputs the analysis result in the form defined by the specification through artificial intelligence and big data analysis. The specific implementation method can refer to the above relevant description and will not be elaborated here.

[0285] Based on the above example, the second device obtains the first interference signal strength. Optionally, the second device can 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 can 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). Further, the first device can then determine the first decoder model that matches the first encoder model, or can determine the first decoder function that matches the corresponding first encoder function, for CSI feedback between the first device and the second device. 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 refer to the above relevant description and will not be explained here.

[0286] S520, the second device sends the first indication information to the first device. Correspondingly, the first device receives the first indication information from the second device.

[0287] 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 the CSI. Alternatively, the first indication information can be used to indicate the first encoder or the first decoder, and the specific meaning can be referred to the relevant description above.

[0288] Exemplarily, the first indication information includes the identifier of the first encoder model and / or the model parameters of the first encoder model; or, the first indication information includes the identifier of the first encoder function and / or the model parameters corresponding to the first encoder function, and the specific interpretation can be referred to the relevant description above.

[0289] Optionally, before performing the above step S520, the second device obtains the model library information.

[0290] Among them, the meaning of the model library information and the manifestation form of the model library information can be referred to the above relevant description, and will not be described here.

[0291] 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 the model training network element, the model storage network element or the model inference network element, such as OAM, OTT, or cloud server, etc., and the specific implementation method can be referred to the above relevant description.

[0292] In the 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. The specific interpretation can be referred to the above relevant description. After performing the above step S520, the first device can determine the first encoder model or the first encoder function selected by the second device according to the received first indication information, and then can determine the first decoder model matching the first encoder model, or can determine the first decoder function corresponding to the first encoder function. Optionally, the first device can send the 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 selected or matched by the first device. At this time, the first device and the second device complete the selection or matching of the dual-end models, and the two can perform CSI feedback based on the dual-end models subsequently.

[0293] 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 models, the first device can perform CSI feedback with the second device based on the dual-end models, thereby improving the CSI feedback performance.

[0294] In one example, the first device sends the first CSI-RS to the 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 the first CSI. Then, the second device can use the above-determined first encoder model or the model corresponding to the first encoder function to perform compression and quantization processing on the first CSI to obtain the feedback CSI (i.e., the first result), and send the feedback CSI to the second device. After receiving the feedback CSI from the second device, the first device can use the above-determined first decoder model or the model corresponding to the first decoder function to perform decompression and quantization processing on the feedback CSI to obtain the recovered CSI (i.e., the second result).

[0295] 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).

[0296] In this application, the first CSI is related to the first interference signal strength. For specific interpretations, reference can be made to the above-related descriptions and will not be elaborated here.

[0297] Optionally, the first encoder model in the embodiments of this application can be deployed on the first device side, and the first decoder model can be deployed on the second device side. Alternatively, 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 an OAM, an OTT, or a cloud server, etc. This application does not make any limitations in this regard.

[0298] In summary, the second device obtains the first interference signal strength and indicates the first encoder model or the 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 considering the first interference signal strength for CSI feedback, in order to improve the CSI feedback performance.

[0299] Next, in combination with Figure 6 and Figure 7 , taking the first device as the terminal device and the second device as the network device as an example, the selection or matching of the first encoder model triggered by the second device (such as a gNB) will be described. Among them, Figure 6 and Figure 7 can be regarded as a further refinement of Case 1 in Figure 5 , Figure 6The first interference information is obtained by a first device (such as a UE) through channel measurement of a first reference signal. In contrast, Figure 7 the first interference information is retrieved or predicted locally / cloud by a second device (such as a gNB). By obtaining the first interference information, a first encoder model is determined, and then the selection or matching of the first encoder model and the first decoder is achieved.

[0300] Figure 6 It is a schematic flowchart of a communication method 600 provided by an embodiment of the present application. As Figure 6 shown, this method can be executed by a first device (such as a UE) and a second device (such as a gNB), including the following multiple steps. Optionally, the first device can also be a chip, a chip system, or a circuit in a terminal device, or a functional module / device in a terminal device that can call and execute a program. The second device can also be a chip, a chip system, or a circuit in a network device, or a functional module / device in 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) 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 OAM, OTT, or a cloud server, etc. The present application does not limit this. Optionally, the first device can include a terminal device and an AI entity, and / or the second device can include a network device and an AI entity, which is not limited herein.

[0301] S610, optionally, the UE sends model library information to the gNB. Correspondingly, the gNB receives the model library information from the UE.

[0302] Among them, the model library information is used to indicate the mapping relationship between multiple encoder models and multiple interference signal strength ranges. For specific interpretations, reference can be made to the relevant descriptions of the above method 500.

[0303] S620, the gNB sends configuration information to the UE. Correspondingly, the UE receives the configuration information from the gNB.

[0304] Among them, the configuration information is used to indicate channel measurement of the first reference signal. For specific interpretations of the first reference signal and the configuration information and the specific implementation manner of channel measurement, reference can be made to the relevant descriptions of the above method 500.

[0305] S630, the UE performs channel measurement on the first reference signal to obtain the first interference information.

[0306] Exemplarily, the first interference information is used to indicate the first interference signal strength. For example, the first interference information can be a specific interference power value, such as 8 dBm.

[0307] S640, the UE sends the first interference information to the gNB. Correspondingly, the gNB receives the first interference information from the UE.

[0308] S650, the gNB determines the first encoder model according to the first interference information.

[0309] Optionally, if the above step S610 is executed, it means that 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 according to the received first interference information, and can determine the corresponding first encoder model according to the model library information obtained in step S610. For example, assuming that the interference power value reported by the UE is 8 dBm, the gNB can select the AI CSI feedback encoder model #3 (i.e., the first encoder model) from Table 1 above for subsequent CSI feedback.

[0310] Optionally, if the above step S610 is not executed, it means that the gNB does not know the model library information of the UE. At this time, the gNB can determine the corresponding first decoder model, such as the AI CSI feedback decoder model #3, from the model library information of the gNB in Table 3 above according to the received first interference information, and then determine the corresponding first encoder model (for example, the AI CSI feedback encoder model #3) based on the corresponding relationship between the first encoder model and the first decoder model (that is, the two are dual - end models). The corresponding relationship between the first encoder model and the first decoder model can be predefined, configured or pre - configured. This application does not limit this.

[0311] Exemplarily, the AI CSI feedback encoder model #3 and the AI CSI feedback decoder model #3 can be the encoder and decoder as shown in Figure 3 For example, the AI CSI feedback encoder model #3 and the AI CSI feedback decoder model #3 in the embodiments of this application can be used for CSI compression processing and CSI decompression processing respectively.

[0312] S660, the gNB sends the first indication information to the UE. Correspondingly, the UE receives the first indication information from the gNB.

[0313] Among them, the first indication information is used to indicate the first encoder model, such as the AI CSI feedback encoder model #3.

[0314] For example, the first indication information carries the model ID of the AI CSI feedback encoder model #3 and / or the model parameters of the AI CSI feedback encoder model #3. For specific interpretations, reference can be made to the relevant descriptions of the above method 500. Further, after receiving the first indication information, the UE can select the AI CSI feedback encoder model #3 from the local model library according to the model ID of the AI CSI feedback encoder model #3 and / or the model parameters of the AI CSI feedback encoder model #3.

[0315] Optionally, the UE can send feedback information to the gNB to indicate that the UE has successfully selected the AI CSI feedback encoder model #3. That is, for the scenario of AI CSI feedback, the dual - end models have been identified and paired between the UE and the gNB, such as the AI CSI feedback encoder model #3 and the AI CSI feedback decoder model #3.

[0316] Based on the above steps, after the UE and the gNB complete the selection or matching of the dual - end models, the UE can perform CSI feedback with the gNB based on the dual - end models. For example, refer to the following steps S670 to S690.

[0317] S670, the gNB sends the first CSI - RS to the UE. Correspondingly, the UE receives the first CSI - RS from the gNB.

[0318] S680, the UE measures the first CSI - RS to obtain the first CSI.

[0319] S690, the UE sends the first result to the gNB. Correspondingly, the gNB receives the first result from the UE.

[0320] In one example, if 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 through the first encoder model to obtain the 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 through the first decoder model to obtain the restored CSI (i.e., the second result) to complete the CSI feedback.

[0321] In another example, if the first encoder model is deployed in network element #1 and the first decoder model is deployed in network element #2, the UE can send the first CSI obtained by measuring the first CSI-RS to network element #1. Network element #1 compresses the first CSI through the first encoder model to obtain the feedback CSI (i.e., the first result), and sends the first result to the UE. Then the UE sends the first result to the gNB. Correspondingly, after receiving the first result, the gNB sends the first result to network element #2. Network element #2 decompresses the first result through the first decoder model to obtain the restored CSI (i.e., the second result), and sends the second result to the gNB to complete the CSI feedback.

[0322] According to the above solution, the gNB triggers the measurement process of the first interference information (or the first interference signal strength) to obtain the first interference information, determines the first encoder model based on the obtained first interference information, and indicates the first encoder model to the UE by sending the first indication information, so as to implement the selection and pairing of the first encoder model and the first decoder model for CSI feedback. That is, the dual-end models are selected or matched considering the first interference signal strength in order to improve the CSI feedback performance.

[0323] Figure 7 It is a schematic flowchart of the communication method 700 provided by an embodiment of the present application. As Figure 7 shown, this method can be executed by a first device (such as a UE) and a second device (such as a gNB), and includes the following multiple steps. Optionally, the first device can also be a chip, a chip system or a circuit in the terminal device, or a functional module / device in the terminal device that can call and execute the program. The second device can also be a chip, a chip system or a circuit in the network device, or a functional module / device in the network device that can call and execute the program. Optionally, the first device or the second device can also be an AI entity (also called an AI network element) or a chip or storage device for the AI entity, such as a model training network element, a model storage network element, or a model inference network element, such as OAM, OTT, or a cloud server, etc. The present application does not limit this. Optionally, the first device can include the terminal device and the AI entity, and / or the second device can include the network device and the AI entity, which is not limited here.

[0324] S710. Optionally, the UE sends model library information to the gNB. Correspondingly, the gNB receives the model library information from the UE.

[0325] Among them, the content and interpretation of the model library information can refer to the relevant description of step S610 of the above method 600.

[0326] S720. The gNB obtains the first interference information through local / cloud retrieval or prediction.

[0327] Among them, the content and interpretation of the first interference information, and the specific implementation of retrieving or predicting the first interference information locally / cloud can refer to the relevant description of the above method 500.

[0328] S730. The gNB determines the first encoder model according to the first interference information.

[0329] Optionally, if the above step S710 is executed, it indicates that 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 according to the first interference information obtained by retrieval or prediction inference, and determine the corresponding first encoder model according to the model library information in the above Table 1, such as the AI CSI feedback encoder model #3.

[0330] Optionally, if the above step S710 is not executed, it indicates that the gNB does not know the model library information of the UE. At this time, the gNB determines the corresponding first decoder model according to the first interference information obtained by retrieval or prediction inference and from the model library information in the above Table 3, such as the AI CSI feedback decoder model #3, and then determines the corresponding first encoder model, such as the AI CSI feedback encoder model #3, based on the corresponding relationship between the first encoder model and the first decoder model (that is, the two are dual - end models). Among them, the corresponding relationship between the first encoder model and the first decoder model can be predefined, configured or pre - configured, and this application does not limit this.

[0331] Based on the above steps, after the UE and the gNB complete the selection or matching of the dual - end models, the UE and the gNB can perform CSI feedback based on the dual - end models. For example, refer to the following steps S740 to S770.

[0332] S740. The gNB sends the first indication information to the UE. Correspondingly, the UE receives the first indication information from the gNB.

[0333] S750. The gNB sends the first CSI - RS to the UE. Correspondingly, the UE receives the first CSI - RS from the gNB.

[0334] S760. The UE measures the first CSI - RS to obtain the first CSI.

[0335] S770. The UE sends the first result to the gNB. Correspondingly, the gNB receives the first result from the UE.

[0336] Among them, the specific implementation of steps S740 to S770 can refer to the relevant description of steps S660 - S690 of the above method 600.

[0337] According to the above solution, the gNB triggers the execution of local (or cloud) retrieval or prediction to obtain the first interference information, determines the first encoder model based on the obtained first interference information, and indicates the first encoder model to the UE by sending the first indication information, thereby realizing the selection and pairing of the first encoder model and the first decoder model for CSI feedback. That is, the dual-end model is selected or matched considering the first interference signal strength, with the expectation of improving the CSI feedback performance.

[0338] Next, in combination with Figure 8 and Figure 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 a UE) will be described. Among them, Figure 8 and Figure 9 can be regarded as Figure 5 a further refinement of case 2 in Figure 8 . The first interference information in Figure 9 is obtained by the second device (such as a UE) through channel measurement of the first reference signal. In contrast, Figure 9 the first interference information is obtained by local interference prediction or perception processing of the second device (such as a 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.

[0339] Figure 8 is a schematic flowchart of the communication method 800 provided in an embodiment of the present application. As Figure 8 shown, this method can be executed by the second device (such as a UE) and the first device (such as a gNB), and includes the following multiple steps. Optionally, the second device can also be a chip, chip system, or circuit in the terminal device, or a functional module / device in the terminal device that can call and execute the program. The first device can also be a chip, chip system, or circuit in the network device, or a functional module / device in the network device that can call and execute the program. Optionally, the first device or the second device can also 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 OAM, OTT, or a cloud server, etc. The present application does not limit this. Optionally, the first device can include a network device and an AI entity; optionally, the second device can include a terminal device and an AI entity, which is not limited here.

[0340] S810. Optionally, the UE sends a request message to the gNB. Correspondingly, the gNB receives the request message from the UE.

[0341] Among them, the request message is used to request the gNB to send configuration information to the UE.

[0342] S820, the gNB sends configuration information to the UE. Correspondingly, the UE receives the configuration information from the gNB.

[0343] S830, the UE performs channel measurement on the first reference signal to obtain first interference information.

[0344] Among them, for the content, interpretation, and specific implementation methods of the configuration information and the first interference information involved in the above steps S820 and S830, reference can be made to the relevant descriptions in steps S620 - S630 of the above method 600.

[0345] S840, the UE determines a first encoder model according to the first interference information.

[0346] In one example, the UE can determine the first encoder model according to 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 can determine the corresponding first encoder model from Table 1 above, such as the AI CSI feedback encoder model #3, for subsequent CSI feedback. Among them, for the meaning and manifestation form of the model library information, reference can be made to the relevant descriptions of the above method 500, which will not be elaborated here.

[0347] S850, the UE sends first indication information to the gNB. Correspondingly, the gNB receives the first indication information from the UE.

[0348] Among them, for the content and interpretation of the first indication information, reference can be made to the relevant descriptions of the above method 500.

[0349] In one example, after receiving the first indication information, the gNB can determine the first encoder model according to the model ID of the first encoder model and / or the model parameters of the first encoder model carried in the first indication information, such as the AI CSI feedback encoder model #3, and then correspondingly select a first decoder model from the local model library information, such as the AI CSI feedback decoder model #3.

[0350] Optionally, the gNB can send feedback information to the UE to indicate that the gNB has successfully selected the AI CSI feedback decoder model #3. That is, for the scenario of AI CSI feedback, the double - end models between the UE and the gNB have been identified and paired, such as the AI CSI feedback encoder model #3 and the AI CSI feedback decoder model #3.

[0351] Based on the above steps, after the UE and the gNB complete the selection or matching of the double - end models, the UE and the gNB can perform CSI feedback based on the double - end models. For example, refer to the following steps S860 to S880.

[0352] S860, the gNB sends the first CSI-RS to the UE. Correspondingly, the UE receives the first CSI-RS from the gNB.

[0353] S870, the UE measures the first CSI-RS to obtain the first CSI.

[0354] S880, the UE sends the first result to the gNB. Correspondingly, the gNB receives the first result from the UE.

[0355] Among them, the specific implementation manners of steps S860 to S880 may refer to the relevant descriptions of steps S670 - S690 of the above method 600.

[0356] According to the above solution, the UE triggers the measurement process of the first interference information (or the first interference signal strength) to obtain the first interference information, determines the first encoder model according to the obtained first interference information, and indicates the first encoder model to the gNB by sending the first indication information, so as to implement the selection and pairing of the first encoder model and the first decoder model for CSI feedback. That is, considering the first interference signal strength, the dual - end models are selected or matched to improve the CSI feedback performance.

[0357] Figure 9 It is a schematic flowchart of the communication method 900 provided by the embodiments of the present application. As Figure 9 shown, this method can be executed by a second device (such as a UE) and a first device (such as a gNB), and includes the following multiple steps. Optionally, the second device can also be a chip, a chip system or a circuit in the terminal device, or a functional module / device in the terminal device that can call and execute a program. The first device can also be a chip, a chip system or a circuit in the network device, or a functional module / device in the network device that can call and execute a program. Optionally, the first device or the second device can also 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 OAM, OTT, or a cloud server, etc. The present application does not limit this. Optionally, the first device can include a terminal device and an AI entity; optionally, the second device can include a network device and an AI entity, which are not limited herein.

[0358] S910, the UE performs interference prediction or perception processing to obtain the first interference information.

[0359] Among them, the content and its interpretation included in the first interference information may refer to the relevant descriptions of the above method 500.

[0360] S920, optionally, the UE sends the first interference information to the gNB. Correspondingly, the gNB receives the first interference information from the UE.

[0361] It should be understood that after receiving the first interference information from the UE, the gNB can know that the first encoder model determined by the UE in the subsequent steps S930 - S940 or the first encoder model indicated by the first indication information is associated with the first interference information. Or rather, the gNB determines that the first encoder model subsequently indicated by the UE is determined according to the first interference information. The specific meaning of the association between the first encoder model and the first interference information can refer to the relevant description of the above method 500 and will not be elaborated here.

[0362] S930, the UE determines a first encoder model according to the first interference information.

[0363] S940, the UE sends the first indication information to the gNB. Correspondingly, the gNB receives the first indication information from the UE.

[0364] S950, the gNB sends the first CSI-RS to the UE. Correspondingly, the UE receives the first CSI-RS from the gNB.

[0365] S960, the UE measures the first CSI-RS to obtain a first CSI.

[0366] S970, the UE sends a first result to the gNB. Correspondingly, the gNB receives the first result from the UE.

[0367] Among them, the content, meaning, and specific implementation method of the first indication information involved in the above steps S930 to S970 can refer to the relevant description of steps S840 - S880 of the above method 800.

[0368] According to the above solution, the UE triggers the execution of interference measurement or sensing to obtain the first interference information, determines the first encoder model according to the obtained first interference information, and indicates the first encoder model to the gNB by sending the first indication information, so as to realize the selection and pairing of the first encoder model and the first decoder model for CSI feedback. That is, the dual-end model is selected or matched considering the first interference signal strength in order to improve the CSI feedback performance.

[0369] It should be noted that the above Figures 6 to 9 mainly takes the selection or matching of the first encoder model (and / or the first decoder model) as an example for illustration. It should be understood that the specific implementation method 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 above first encoder model (and / or the first decoder model) and will not be elaborated here.

[0370] Optionally, in the embodiments 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 correspondingly determine the first encoder model and / or the first encoder function. Among them, the first encoder function can correspond to one or more first encoder models.

[0371] Above, in combination with Figures 1 to 9 The method provided in the embodiments of the present application has been described in detail. Next, in combination with Figures 10 to 11 The device provided in the embodiments of the present application will be described in detail. It should be understood that the description of the device embodiments corresponds to the description of the method embodiments. Therefore, for the content not described in detail, reference can be made to the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0372] Figure 10 FIG. is a schematic diagram of a communication device 1000 provided in the embodiments of the present application. As Figure 10 shown, 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 combination with a terminal device and capable of implementing the 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 combination with a network device and capable of implementing the method executed by the network device, such as a chip, a chip system, or a circuit.

[0373] Among them, the communication module can also be referred to as a transceiver module, a transceiver, a transceiver, or a transceiver device, etc. The processing module can also be referred to as a processor, a processing board, a processing unit, or a processing device, etc. Optionally, the communication module is used to perform the sending operation and receiving operation of the terminal device or the network device in the above method. The device for implementing the receiving function in the communication module can be regarded as a receiving unit, and the device for implementing the sending function in the communication module can be regarded as a sending unit, that is, the communication module includes a receiving unit and a sending unit.

[0374] When the communication device 1000 is applied to a terminal device, the processing module 1001 can be used to implement the processing function of the first device in the above embodiments, and the communication module 1002 can be used to implement the transceiver function of the first device in the above embodiments.

[0375] 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.

[0376] In addition, it should be noted that the foregoing communication module and / or processing module may be implemented by a virtual module. For example, the processing module may be implemented by a software functional unit or a virtual device, and the communication module may be implemented by a software function or a virtual device. Alternatively, the processing module or the communication module may also be implemented by a physical device. For example, if the device is implemented by a chip / circuit (such as an integrated circuit or a logic circuit, etc.). The communication module may be an input / output circuit and / or a communication interface, performing an input operation (corresponding to the foregoing receiving operation) and an output operation (corresponding to the foregoing sending operation); the processing module is an integrated processor or a microprocessor or a circuit (such as an integrated circuit or a logic circuit, etc.).

[0377] The division of modules in this application is illustrative, merely a logical function division. In actual implementation, there may be other division methods. In addition, in each example of this application, each functional module may be integrated in a processor, may exist alone physically, or two or more modules may be integrated in one module. The above integrated module may be implemented in the form of hardware or in the form of a software functional module.

[0378] Figure 11 It is a schematic diagram of another communication device 1100 provided by an embodiment of the present application. As Figure 11 shown, optionally, the communication device 1100 may be the foregoing first device or second device, or a chip or a chip system for the foregoing first device or second device. Optionally, the communication device 1100 may be the foregoing terminal device or network device, or a chip or a chip system for the foregoing terminal device or network device. Optionally, in this application, the chip system may be composed of chips, or may include chips and other discrete devices.

[0379] The communication device 1100 can be used to implement the functions of any device (such as the first device or the second device) in the communication system described in the foregoing 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 inside the device, or the memory may be integrated with the processor, or the memory may also be located outside the device. For example, the communication device 1100 may further include at least one memory 1120. The memory 1120 stores the necessary computer programs, computer programs or instructions and / or data in implementing any of the foregoing examples; the processing circuit 1110 may execute the computer programs stored in the memory 1120 to complete the methods in any of the foregoing examples.

[0380] The communication device 1100 may further include a transceiver circuit 1130. The communication device 1100 can interact 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, transmit 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, or a transceiver, or a communication interface, which is not limited herein.

[0381] Among them, the processing circuit 1110 can be one or more processors, or all or part of the processing circuits in one or more processors. The processing circuit 1110 is an integrated processor, a microprocessor, an integrated circuit, or a logic circuit, etc. The processor can determine the output information according to the input information.

[0382] The coupling in this application is an indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, and is used for information interaction between devices, units, or modules. The processing circuit 1110 may cooperate with the memory 1120 and the transceiver circuit 1130. The specific connection medium between the processing circuit 1110, the memory 1120, and the transceiver circuit 1130 is not limited in this application.

[0383] Optionally, as Figure 11 shown, the processing circuit 1110, the memory 1120, and the transceiver circuit 1130 are interconnected through a bus 1140. Optionally, the bus can include types of buses such as an address bus, a data bus, and a control bus. In addition, for ease of representation, Figure 11 one bus 1140 is shown, but it does not mean that there is only one bus or one type of bus.

[0384] It should be understood that the processor mentioned in the embodiments of the present application may be the following device or a part of the circuit for processing functions in the following devices: a central processing unit (CPU), or it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0385] 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 ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM). For example, the RAM may be used as an external cache. By way of example and not limitation, the RAM includes the following various forms: static random access memory (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0386] 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) may be integrated in the processor.

[0387] It should also be noted that the memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0388] In the embodiments of the present application, the methods described in the above embodiments may be executed by a terminal device and a network device, or may be executed by a chip, a chip system or a circuit of the terminal device and the network device, and the chip, the chip system or the circuit may be installed in the terminal device and the network device.

[0389] The embodiments of the present application provide a computer-readable storage medium, on which computer instructions for implementing the methods executed by the devices (such as terminal devices, or network devices) in the above method embodiments are stored.

[0390] For example, when the computer program is executed by a computer, the computer can implement the methods executed by the devices (such as terminal devices, or network devices, etc.) in the above method embodiments.

[0391] The embodiments of the present application provide a computer program product, including instructions, which when executed by a computer, implement the methods executed by the devices (such as terminal devices, or network devices (or positioning devices, etc.)) in the above method embodiments.

[0392] The embodiments of the present application provide a communication system, which includes the terminal devices and / or network devices in the above embodiments. For example, the system includes the terminal devices and / or network devices in the above embodiments. For another example, the system includes the terminal devices and / or network devices in the above embodiments.

[0393] The explanations and beneficial effects of the relevant content in any of the above provided devices may refer to the corresponding method embodiments provided above, and will not be elaborated here.

[0394] To facilitate the understanding of the above embodiments provided by the present application, the following points are explained:

[0395] In the present application, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced mutually, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0396] In this application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. In the written description of this application, the character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (item)" or a similar expression thereof refers to any combination of these items, including any combination of a single item or plural items. For example, at least one (item) of a, b, and c can represent: a, or b, or c, or a and b, or a and c, or b and c, or a, b, and c. Where a, b, and c can each be single or multiple.

[0397] In this application, "first", "second", and various numerical designations are for the convenience of description and do not limit the scope of the embodiments of this application. For example, to distinguish different messages, etc., rather than for describing a specific order or sequence. It should be understood that the objects described in this way can be interchanged under appropriate circumstances so as to be able to describe the solutions other than the embodiments of this application.

[0398] In this application, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0399] In this application, "used to indicate" can include being used to directly indicate and being used to indirectly indicate. When describing that a certain indication information is used to indicate A, it can include that the indication information directly indicates A or indirectly indicates A, and does not mean that the indication information must carry A. Among them, directly indicating information A means including the information A; implicitly indicating information A means indicating information A through the correspondence relationship between information A and information B and the direct indication information B. Among them, the correspondence relationship between information A and information B can be predefined, pre-stored, pre-burned, or pre-configured.

[0400] In this application, information C is used to determine information D, which includes both the case where information D is determined only based on information C and the case where it is determined based on information C and other information. In addition, when information C is used for the determination of information D, there can also be an indirect determination situation. For example, information D is determined based on information E, and information E is determined based on information C.

[0401] In this application, "Network Element A sends Information A to Network Element B" can be understood as the destination of Information A or an intermediate network element in the transmission path between the destination and the source of Information A is Network Element B, which may include directly or indirectly sending Information A to Network Element B. "Network Element B receives Information A from Network Element A" can be understood as the source of Information A or an intermediate network element in the transmission path between the source and the destination of Information A is Network Element A, which may include directly or indirectly receiving Information A from Network Element A. Necessary processing may be performed on the information between the source and the destination of the information transmission, such as format conversion, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be elaborated here.

[0402] It can be understood that some optional features in the embodiments of this application may, in some scenarios, not depend on other features, and may also, in some scenarios, be combined with other features, without limitation.

[0403] 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" can be understood as the destination of Information A or an intermediate network element in the transmission path between the destination and the source of Information A is Network Element B, which may include directly or indirectly sending Information A to Network Element B. "Network Element B receives Information A from Network Element A" can be understood as the source of Information A or an intermediate network element in the transmission path between the source and the destination of Information A is Network Element A, which may include directly or indirectly receiving Information A from Network Element A. Necessary processing may be performed on the information between the source and the destination of the information transmission, such as format conversion, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be elaborated here.

[0404] It can also be understood that in some of the above embodiments, the AI model used for positioning is mainly taken as an example for illustrative purposes. It can be understood that the above AI model can also be used for other purposes.

[0405] It can also be understood that the solutions in the embodiments of this application can be reasonably combined and used, and the explanations or descriptions of the various terms that appear in the embodiments can be referred to or explained with each other in the various embodiments, without limitation.

[0406] It can also be understood that in the above method embodiments, the methods and operations implemented by the terminal device or the positioning device can also be implemented by components (such as chips or circuits) that can be part of the terminal device or the positioning device, without limitation.

[0407] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0408] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0409] In 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 illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0410] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0411] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0412] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0413] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A communication method, characterized in that, Applied to a first device, including: 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, wherein Before sending the configuration information to the second device, the method further includes: Receiving 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: Sending 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: Sending a first CSI-RS to the second device; Receiving 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, 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, 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, Applied to the second device, it includes: Obtaining the first interference signal strength; Sending 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. A communication device, characterized in that, Including a module for executing the method according to any one of claims 1-11, or a module for executing the method according to claim 12.

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