Channel state feedback method and communication apparatus

Abstract

The present application provides a channel state feedback method and a communication apparatus. The channel state feedback method comprises: an encoder compressing M pieces of channel information to obtain a first sequence, wherein the M pieces of channel information are part or all of L pieces of measured channel information, L is less than or equal to N, N is the number of pieces of channel information determined on the basis of reference signals on K antenna port groups, N is a positive integer, and the first sequence consists of X joint feedback quantities corresponding to the N pieces of channel information; determining a second sequence on the basis of the first sequence, the second sequence consisting of Y joint feedback quantities corresponding to the M pieces of channel information; and sending the second sequence to a decoder. A terminal device can perform partial measurement and partial compression and feedback on a plurality of analog beams within the maximum analog beam range, avoiding the storage / switching overhead of AI models and reducing the complexity of implementation.

Description

Method and communication device for feeding back channel status

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on September 28, 2023, with application number 202311292016.X and invention name “Method and communication device for feedback channel status”, the entire contents of which are incorporated by reference into this application. Technical Field

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

[0003] In communication systems, network devices need to obtain downlink channel information fed back by terminal devices in order to perform precoding and other processing on downlink data. In current feedback mechanisms, network devices can send reference signals to terminal devices. The terminal devices determine the channel based on the reference signals and then provide corresponding channel state information (CSI). This information can include, for example, precoding information, the number of transmission streams supported by the channel (i.e., the channel rank), and a channel quality indicator (CQI). This CQI is used to provide feedback on the modulation coding scheme (MCS) recommended by the terminal device based on the current channel quality.

[0004] When reporting, for example in a multiple-input multiple-output (MIMO) system, in order to save the feedback overhead of the precoding matrix, the terminal device can compress the precoding information before reporting. For example, the network device / terminal device can design and train an autoencoder based on an AI model to perform compression in both the space-frequency domain and the frequency domain.

[0005] In high-frequency communication systems, network devices use different simulated beam weights to send reference signals. Terminal devices measure these signals individually and provide feedback on their measurement results. For terminal devices with different reporting capabilities, network devices must be configured with different AI models to support these capabilities, increasing storage and switching overhead for both network and terminal devices.

[0006] Therefore, there is an urgent need for a method for feeding back channel status in order to reduce the storage / switching overhead of network devices and terminal devices.

[0007] Summary of the Invention

[0008] The present application provides a method and a communication device for feeding back channel status, which can reduce the storage / switching overhead of network equipment and terminal equipment.

[0009] In a first aspect, a method for feeding back channel status is provided. The method may be executed by a terminal device, or may be executed by an encoder configured in the terminal device, which is not limited in this application.

[0010] The method includes: compressing M channel information to obtain a first sequence, where the M channel information is part or all of L measured channel information, L is less than or equal to N, N is the number of channel information determined based on reference signals on K antenna port groups, the first sequence is X joint feedback amounts corresponding to the N channel information, and M, L, and N are positive integers; determining a second sequence based on the first sequence, where the second sequence is Y joint feedback amounts corresponding to the M channel information; and sending the second sequence to a decoder.

[0011] Among them, N channel information corresponds to N analog beams, and N can also be understood as the maximum number of analog beams.

[0012] The N simulated beams are different from each other, and there is an overlapping area in the coverage range of the N simulated beams.

[0013] In the present application, the network device and the terminal device may predefine or predetermine the maximum number N of simulated beams for joint feedback and the maximum feedback overhead X.

[0014] It is understandable that the terminal device can measure L channel information and compress and feedback M of the channel information.

[0015] Based on the above technical solution, the terminal device can perform partial measurement and partial compression feedback on multiple simulated beams within the maximum simulated beam range, avoiding the storage / switching overhead of the AI ​​model and reducing the implementation complexity.

[0016] In combination with the first aspect, in certain implementations of the first aspect, when the first sequence or the second sequence is a sequence before quantization, the joint feedback amount is the length of the first sequence or the second sequence; or when the first sequence or the second sequence is a sequence after quantization, the joint feedback amount is the number of bits of the first sequence or the second sequence.

[0017] In combination with the first aspect, in one possible implementation method, the correspondence between the stacking positions of the N channel information at the encoder input end and the stacking positions of the M channel information at the encoder input end is determined; and Y joint feedback amounts corresponding to the M channel information are determined based on the correspondence.

[0018] In this technical solution, the beam position correspondence between the partial feedback amount and the full feedback amount at the encoder input end is defined. Based on this correspondence, the matrix of the partial feedback amount corresponding to M channel information can be determined from the matrix of the full feedback amount output from the encoder output end, thereby realizing the compression and feedback of partial channel information.

[0019] In combination with the first aspect, in a possible implementation, the first sequence includes N first features and O second features, the N first features corresponding to the N channel information, the first features indicate the specific features of the each channel information, and the O second features indicate the common features of the N channel information, and determining Y joint feedback amounts corresponding to the M channel information according to the corresponding relationship includes: determining M first features according to the corresponding relationship and the N first features, the M first features corresponding to the M channel information; determining P second features corresponding to the M channel information according to the corresponding relationship and the O second features, where P is less than or equal to O, and O and P are positive integers; and determining Y joint feedback amounts corresponding to the M channel information according to the M first features and the P second features.

[0020] In this technical solution, the common features and unique features of N channel information are defined separately, so that separable joint feedback can be achieved. Through partial feedback, the feedback of channel information under different numbers of simulated beams and different feedback overheads is supported.

[0021] In combination with the first aspect, in one possible implementation, the positions of the N first features in the first sequence correspond to the positions of the M first features in the second sequence, and the positions of the O second features in the first sequence correspond to the positions of the P second features in the second sequence.

[0022] In this technical solution, the corresponding relationship between different simulated beam characteristics and the position of the full feedback amount is defined.

[0023] In combination with the first aspect, in a possible implementation, the N first features and the O second features include a feature principal component and a feature supplementary component, the feature principal component is used to complete the reconstruction of the channel information, and the feature supplementary component is used to supplement the accuracy of the channel information.

[0024] In this technical solution, feedback of different accuracies is achieved through the principal component and supplementary component corresponding to the features, which increases the flexibility of the feedback.

[0025] In combination with the first aspect, in one possible implementation, each of the M first features includes the feature principal component, some or all of the M first features include the feature supplementary component, each of the P second features includes the feature principal component, and some or all of the P second features include the feature supplementary component.

[0026] In this technical solution, among the M channel information that are compressed and fed back, different channel information can be fed back with different precisions, thus achieving flexible feedback.

[0027] In combination with the first aspect, in one possible implementation, the characteristic principal component of each first feature among the N first features has a corresponding relationship with the characteristic principal components of some or all of the first features among the M first features; the supplementary component of each first feature among the N first features has a corresponding relationship with the supplementary components of some or all of the first features among the M first features; the characteristic principal component of each second feature among the O second features has a corresponding relationship with the characteristic principal component of each second feature among the P second features, and the characteristic supplementary component of each second feature among the O second features has a corresponding relationship with the characteristic supplementary component of each second feature among the P second features.

[0028] In this technical solution, among the M channel information of the compressed feedback, the features corresponding to each channel information have a corresponding relationship with the features corresponding to the N channel information.

[0029] With reference to the first aspect, in a possible implementation, accuracy levels corresponding to the M pieces of channel information are determined.

[0030] In combination with the first aspect, in a possible implementation, the M channel information or the accuracy level corresponding to the M channel information is determined by a network device, or the M channel information or the accuracy level corresponding to the M channel information is determined by a terminal device.

[0031] In combination with the first aspect, in one possible implementation, the M channel information or the accuracy level corresponding to the M channel information is determined by the network device based on the priority information of the L channel information; or, the M channel information or the accuracy level corresponding to the M channel information is determined by the terminal device based on the measurement results of the L channel information and / or the priority information of the L channel information.

[0032] In this technical solution, the feedback channel information and accuracy are determined according to the priority, ensuring the efficiency and reliability of multi-beam reporting.

[0033] In combination with the first aspect, in a possible implementation method, when the M channel information is determined by the network device, the correspondence between the M channel information and the L channel information is sent; when the precision categories corresponding to the M channel information are determined by the network device, the precision categories corresponding to the M channel information are sent.

[0034] In combination with the first aspect, in a possible implementation manner, the encoder is used in a terminal device.

[0035] In a second aspect, a method for feeding back channel status is provided. The method may be executed by a network device, or may be executed by a decoder configured in the network device, which is not limited in this application.

[0036] The method includes: receiving a second sequence, where the second sequence is Y joint feedback amounts corresponding to M channel information, where the M channel information is part or all of L measured channel information, where L is less than or equal to N, and N is the number of channel information determined based on reference signals on K antenna port groups, and N is a positive integer; determining a first sequence based on the second sequence, where the first sequence is X joint feedback amounts corresponding to the N channel information; and reconstructing the first sequence to obtain the M channel information.

[0037] Based on the above technical solution, the network device can reconstruct the joint feedback amount of part of the channel information fed back by the terminal device to obtain M channel information, avoiding the storage / switching overhead of the AI ​​model and reducing the implementation complexity.

[0038] In conjunction with the second aspect, in a possible implementation, when the first sequence or the second sequence is a sequence before quantization, the joint feedback amount is the length of the first sequence or the second sequence; or when the first sequence or the second sequence is a sequence after quantization, the joint feedback amount is the number of bits of the first sequence or the second sequence.

[0039] In combination with the second aspect, in one possible implementation, a correspondence between the stacking positions of the M channel information at the decoder input end and the stacking positions of the N channel information at the decoder input end is determined; and X joint feedback amounts corresponding to the N channel information are determined based on the correspondence.

[0040] In this technical solution, the beam position correspondence between the partial feedback amount and the full feedback amount at the decoder input end is defined. Based on this correspondence, the matrix input decoder of the full feedback amount can be determined according to the matrix of the partial feedback amount corresponding to the M channel information of the terminal device, thereby realizing the reconstruction of partial channel information.

[0041] With reference to the second aspect, in one possible implementation, the second sequence includes M first features and P second features, the M first features corresponding to the M channel information, the first features indicating a specific feature of each channel information, and the second features indicating a common feature of the M channel information. Determining X joint feedback amounts corresponding to the N channel information based on the corresponding relationship includes: determining N first features based on the corresponding relationship and the M first features, the N first features corresponding to the N channel information; determining O second features corresponding to the N channel information based on the corresponding relationship and the P second features, where P is less than or equal to O; and determining the X joint feedback amounts corresponding to the N channel information based on the N first features and the O second features.

[0042] In combination with the second aspect, in one possible implementation, the positions of the M first features in the second sequence correspond to the positions of the N first features in the first sequence, and the positions of the P second features in the second sequence correspond to the positions of the O second features in the first sequence.

[0043] In combination with the second aspect, in one possible implementation, the M first features and the P second features include a feature principal component and a feature supplementary component, the feature principal component is used to complete the reconstruction of the channel information, and the feature supplementary component is used to supplement the accuracy of the channel information.

[0044] In combination with the second aspect, in one possible implementation, each of the N first features includes the feature principal component, some or all of the N first features include the feature supplementary component, each of the O second features includes the feature principal component, and some or all of the O second features include the feature supplementary component.

[0045] In this technical solution, among the M channel information that are compressed and fed back, different channel information is fed back with different precisions. The decoder can reconstruct the channel information according to the different precisions to obtain channel information with different precisions.

[0046] In combination with the second aspect, in one possible implementation, the characteristic principal component of each first feature among the N first features has a corresponding relationship with the characteristic principal components of some or all of the first features among the M first features; the supplementary component of each first feature among the N first features has a corresponding relationship with the supplementary components of some or all of the first features among the M first features; the characteristic principal component of each second feature among the O second features has a corresponding relationship with the characteristic principal component of each second feature among the P second features, and the characteristic supplementary component of each second feature among the O second features has a corresponding relationship with the characteristic supplementary component of each second feature among the P second features.

[0047] In combination with the second aspect, in a possible implementation manner, accuracy levels corresponding to the M channel information are determined.

[0048] In combination with the second aspect, in a possible implementation, the M channel information or the accuracy level corresponding to the M channel information is determined by a network device, or the M channel information or the accuracy level corresponding to the M channel information is determined by a terminal device.

[0049] In combination with the second aspect, in one possible implementation, the M channel information or the accuracy level corresponding to the M channel information is determined by the network device based on the priority information of the L channel information; or, the M simulated beams or the accuracy level corresponding to the M simulated beams is determined by the terminal device based on the measurement results of the L channel information and / or the priority information of the L channel information.

[0050] In combination with the second aspect, in one possible implementation method, when the M channel information is determined by the terminal device, the correspondence between the M channel information and the L channel information is sent; when the precision category corresponding to the M channel information is determined by the terminal device, the precision category corresponding to the M channel information is sent.

[0051] In combination with the second aspect, in a possible implementation, the correspondence between the stacking positions of the M channel information at the decoder input end and the stacking positions of the N channel information at the decoder input end is determined; and the M channel information is determined based on the correspondence.

[0052] In this technical solution, the network device can determine the output matrix corresponding to M channel information from the output matrix of the full feedback amount according to the beam position correspondence between the partial feedback amount and the full feedback amount at the decoder side input end.

[0053] In conjunction with the second aspect, in a possible implementation, the decoder is used in a network device.

[0054] In a third aspect, a communication device is provided. The device may be a terminal device, or an encoder configured in the terminal device, which is not limited in this application.

[0055] The apparatus includes: a processing unit, configured to compress M channel information to obtain a first sequence, where the M channel information is part or all of L measured channel information, where L is less than or equal to N, N is the number of channel information determined based on reference signals on K antenna port groups, and N is a positive integer, and the first sequence is X joint feedback amounts corresponding to the N channel information; the processing unit is further configured to determine a second sequence based on the first sequence, where the second sequence is Y joint feedback amounts corresponding to the M channel information; and a transceiver unit is configured to send the second sequence to a decoder.

[0056] Among them, N channel information corresponds to N analog beams, and N can also be understood as the maximum number of analog beams.

[0057] The N simulated beams are different from each other, and there is an overlapping area in the coverage range of the N simulated beams.

[0058] In the present application, the network device and the terminal device may predefine or predetermine the maximum number N of simulated beams for joint feedback and the maximum feedback overhead X.

[0059] It is understandable that the terminal device can measure L channel information and compress and feedback M of the channel information.

[0060] Based on the above technical solution, the terminal device can perform partial measurement and partial compression feedback on multiple simulated beams within the maximum simulated beam range, avoiding the storage / switching overhead of the AI ​​model and reducing the implementation complexity.

[0061] In combination with the third aspect, in certain implementations of the third aspect, when the first sequence or the second sequence is a sequence before quantization, the joint feedback amount is the length of the first sequence or the second sequence; or when the first sequence or the second sequence is a sequence after quantization, the joint feedback amount is the number of bits of the first sequence or the second sequence.

[0062] In combination with the third aspect, in one possible implementation, the processing unit is also used to determine the correspondence between the stacking positions of the N channel information at the encoder input end and the stacking positions of the M channel information at the encoder input end; the processing unit is also used to determine Y joint feedback amounts corresponding to the M channel information based on the correspondence.

[0063] In this technical solution, the beam position correspondence between the partial feedback amount and the full feedback amount at the encoder input end is defined. Based on this correspondence, the matrix of the partial feedback amount corresponding to M channel information can be determined from the matrix of the full feedback amount output from the encoder output end, thereby realizing the compression and feedback of partial channel information.

[0064] In combination with the third aspect, in a possible implementation, the first sequence includes N first features and O second features, the N first features correspond to the N channel information, the first features indicate the specific features of the each channel information, and the O second features indicate the common features of the N channel information, and determining Y joint feedback amounts corresponding to the M channel information according to the corresponding relationship includes: determining M first features according to the corresponding relationship and the N first features, the M first features corresponding to the M channel information; determining P second features corresponding to the M channel information according to the corresponding relationship and the O second features, where P is less than or equal to O; and determining Y joint feedback amounts corresponding to the M channel information according to the M first features and the P second features.

[0065] In this technical solution, the common features and unique features of N channel information are defined separately, so that separable joint feedback can be achieved. Through partial feedback, the feedback of channel information under different numbers of simulated beams and different feedback overheads is supported.

[0066] In combination with the third aspect, in one possible implementation, the positions of the N first features in the first sequence correspond to the positions of the M first features in the second sequence, and the positions of the O second features in the first sequence correspond to the positions of the P second features in the second sequence.

[0067] In this technical solution, the corresponding relationship between different simulated beam characteristics and the position of the full feedback amount is defined.

[0068] In combination with the third aspect, in one possible implementation, the N first features and the O second features include a feature principal component and a feature supplementary component, the feature principal component is used to complete the reconstruction of the channel information, and the feature supplementary component is used to supplement the accuracy of the channel information.

[0069] In this technical solution, feedback of different accuracies is achieved through the principal component and supplementary component corresponding to the features, which increases the flexibility of the feedback.

[0070] In combination with the third aspect, in one possible implementation, each of the M first features includes the feature principal component, some or all of the M first features include the feature supplementary component, each of the P second features includes the feature principal component, and some or all of the P second features include the feature supplementary component.

[0071] In this technical solution, among the M channel information that are compressed and fed back, different channel information can be fed back with different precisions, thus achieving flexible feedback.

[0072] In combination with the third aspect, in one possible implementation, the characteristic principal component of each first feature among the N first features has a corresponding relationship with the characteristic principal components of some or all of the first features among the M first features; the supplementary component of each first feature among the N first features has a corresponding relationship with the supplementary components of some or all of the first features among the M first features; the characteristic principal component of each second feature among the O second features has a corresponding relationship with the characteristic principal component of each second feature among the P second features, and the characteristic supplementary component of each second feature among the O second features has a corresponding relationship with the characteristic supplementary component of each second feature among the P second features.

[0073] In this technical solution, among the M channel information of the compressed feedback, the features corresponding to each channel information have a corresponding relationship with the features corresponding to the N channel information.

[0074] In combination with the third aspect, in a possible implementation manner, the processing unit is further configured to determine accuracy levels corresponding to the M channel information.

[0075] In combination with the third aspect, in one possible implementation, the M channel information or the accuracy level corresponding to the M channel information is determined by a network device, or the M channel information or the accuracy level corresponding to the M channel information is determined by a terminal device.

[0076] In combination with the third aspect, in one possible implementation, the M channel information or the accuracy level corresponding to the M channel information is determined by the network device based on the priority information of the L channel information; or, the M channel information or the accuracy level corresponding to the M channel information is determined by the terminal device based on the measurement results of the L channel information and / or the priority information of the L channel information.

[0077] In this technical solution, the feedback channel information and accuracy are determined according to the priority, ensuring the efficiency and reliability of multi-beam reporting.

[0078] In combination with the third aspect, in a possible implementation method, when the M channel information is determined by the network device, the transceiver unit is also used to send the correspondence between the M channel information and the L channel information; when the precision category corresponding to the M channel information is determined by the network device, the transceiver unit is also used to send the precision categories corresponding to the M channel information respectively.

[0079] In conjunction with the third aspect, in a possible implementation, the encoder is used in a terminal device.

[0080] In a fourth aspect, a communication device is provided. The device may be a network device, or a decoder configured in a network device, which is not limited in this application.

[0081] The method includes: a transceiver unit, configured to receive a second sequence, where the second sequence is Y joint feedback amounts corresponding to M channel information, where the M channel information is part or all of L measured channel information, where L is less than or equal to N, and N is the number of channel information determined based on reference signals on K antenna port groups, where N is a positive integer; a processing unit, configured to determine a first sequence based on the second sequence, where the first sequence is X joint feedback amounts corresponding to the N channel information; and the processing unit is further configured to reconstruct the first sequence to obtain the M channel information.

[0082] Based on the above technical solution, the network device can reconstruct the joint feedback amount of part of the channel information fed back by the terminal device to obtain M channel information, avoiding the storage / switching overhead of the AI ​​model and reducing the implementation complexity.

[0083] In conjunction with the fourth aspect, in a possible implementation, when the first sequence or the second sequence is a sequence before quantization, the joint feedback amount is the length of the first sequence or the second sequence; or when the first sequence or the second sequence is a sequence after quantization, the joint feedback amount is the number of bits of the first sequence or the second sequence.

[0084] In combination with the fourth aspect, in one possible implementation, the processing unit is further used to determine the correspondence between the stacking positions of the M channel information at the decoder input end and the stacking positions of the N channel information at the decoder input end; the processing unit is further used to determine X joint feedback amounts corresponding to the N channel information based on the correspondence.

[0085] In this technical solution, the beam position correspondence between the partial feedback amount and the full feedback amount at the decoder input end is defined. Based on this correspondence, the matrix input decoder of the full feedback amount can be determined according to the matrix of the partial feedback amount corresponding to the M channel information of the terminal device, thereby realizing the reconstruction of partial channel information.

[0086] In conjunction with the fourth aspect, in a possible implementation, the second sequence includes M first features and P second features, the M first features corresponding to the M channel information, the first feature indicates a specific feature of each channel information, and the second feature indicates a common feature of the M channel information. Determining X joint feedback amounts corresponding to the N channel information based on the corresponding relationship includes: determining N first features based on the corresponding relationship and the M first features, the N first features corresponding to the N channel information; determining O second features corresponding to the N channel information based on the corresponding relationship and the P second features, where P is less than or equal to O; and determining the X joint feedback amounts corresponding to the N channel information based on the N first features and the O second features.

[0087] In combination with the fourth aspect, in one possible implementation, the positions of the M first features in the second sequence correspond to the positions of the N first features in the first sequence, and the positions of the P second features in the second sequence correspond to the positions of the O second features in the first sequence.

[0088] In combination with the fourth aspect, in a possible implementation, the M first features and the P second features include a feature principal component and a feature supplementary component, the feature principal component is used to complete the reconstruction of the channel information, and the feature supplementary component is used to supplement the accuracy of the channel information.

[0089] In combination with the fourth aspect, in one possible implementation, each of the N first features includes the feature principal component, some or all of the N first features include the feature supplementary component, each of the O second features includes the feature principal component, and some or all of the O second features include the feature supplementary component.

[0090] In this technical solution, among the M channel information that are compressed and fed back, different channel information is fed back with different precisions. The decoder can reconstruct the channel information according to the different precisions to obtain channel information with different precisions.

[0091] In combination with the fourth aspect, in one possible implementation, the characteristic principal component of each first feature among the N first features has a corresponding relationship with the characteristic principal components of some or all of the first features among the M first features; the supplementary component of each first feature among the N first features has a corresponding relationship with the supplementary components of some or all of the first features among the M first features; the characteristic principal component of each second feature among the O second features has a corresponding relationship with the characteristic principal component of each second feature among the P second features, and the characteristic supplementary component of each second feature among the O second features has a corresponding relationship with the characteristic supplementary component of each second feature among the P second features.

[0092] In combination with the fourth aspect, in a possible implementation, the processing unit is further configured to determine accuracy levels corresponding to the M pieces of channel information.

[0093] In combination with the fourth aspect, in a possible implementation method, the M channel information or the accuracy level corresponding to the M channel information is determined by a network device, or the M channel information or the accuracy level corresponding to the M channel information is determined by a terminal device.

[0094] In combination with the fourth aspect, in one possible implementation, the M channel information or the accuracy level corresponding to the M channel information is determined by the network device based on the priority information of the L channel information; or, the M simulated beams or the accuracy level corresponding to the M simulated beams is determined by the terminal device based on the measurement results of the L channel information and / or the priority information of the L channel information.

[0095] In combination with the fourth aspect, in a possible implementation method, when the M channel information is determined by the terminal device, the transceiver unit is also used to send the correspondence between the M channel information and the L channel information; when the accuracy category corresponding to the M channel information is determined by the terminal device, the transceiver unit is also used to send the accuracy categories corresponding to the M channel information respectively.

[0096] In combination with the fourth aspect, in one possible implementation, the processing unit is further used to determine the correspondence between the stacking positions of the M channel information at the decoder input end and the stacking positions of the N channel information at the decoder input end; the processing unit is further used to determine the M channel information based on the correspondence.

[0097] In this technical solution, the network device can determine the output matrix corresponding to M channel information from the output matrix of the full feedback amount according to the beam position correspondence between the partial feedback amount and the full feedback amount at the decoder side input end.

[0098] In conjunction with the fourth aspect, in a possible implementation, the decoder is used in a network device.

[0099] In a fifth aspect, a communication device is provided, which is used to perform the method provided in the first or second aspect. Specifically, the communication device may include units and / or modules, such as a processing unit and / or a communication unit, for performing the method provided in any one of the above implementations of the first or second aspect.

[0100] In one implementation, the communication device includes a communication unit and a processing unit. The communication unit may be a transceiver or an input / output interface; the processing unit may be at least one processor. Alternatively, the transceiver may be a transceiver circuit. Alternatively, the input / output interface may be an input / output circuit.

[0101] In another implementation, the communication device is a chip, chip system, or circuit in a network device. When the NTN positioning device is a chip, chip system, or circuit in a network device, the communication unit may be an input / output interface, interface circuit, output circuit, input circuit, pin, or related circuit on the chip, chip system, or circuit; and the processing unit may be at least one processor, processing circuit, or logic circuit.

[0102] In the sixth aspect, a communication device is provided, including a processor and, optionally, a memory, wherein the processor is used to control the transceiver to send and receive signals, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the sending device executes the method in any possible implementation of the first aspect or the second aspect above.

[0103] Optionally, there are one or more processors and one or more memories.

[0104] Optionally, the memory may be integrated with the processor, or the memory may be provided separately from the processor.

[0105] Optionally, the first device further includes a transceiver, which may specifically be a transmitter (transmitter) and a receiver (receiver).

[0106] In the seventh aspect, a computer-readable storage medium is provided, which stores a computer program or code. When the computer program or code is run on a computer, the computer executes the method in any possible implementation of the first or second aspect above.

[0107] In the eighth aspect, a chip is provided, comprising at least one processor, which is coupled to a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program from the memory, so that a sending device equipped with the chip system executes the method in any possible implementation of the first or second aspect above.

[0108] The chip may include an input circuit or interface for sending information or data, and an output circuit or interface for receiving information or data.

[0109] In a ninth aspect, a computer program product is provided, which includes: a computer program code, which, when the computer program code is executed by a sending device, executes the method in any possible implementation of the first aspect or the second aspect.

[0110] The beneficial effects of the third to ninth aspects can refer to the beneficial effects of the first to second aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] FIG1 is a schematic diagram of a communication system applicable to an embodiment of the present application.

[0112] FIG2 is a schematic diagram of another communication system applicable to an embodiment of the present application.

[0113] FIG3 is a schematic diagram of a possible application framework applicable to an embodiment of the present application.

[0114] FIG4 is a schematic diagram of a possible application framework applicable to an embodiment of the present application.

[0115] FIG5 is a schematic diagram of CSI compression feedback applicable to an embodiment of the present application.

[0116] FIG6 is a schematic diagram of a hardware architecture 600 applicable to an embodiment of the present application.

[0117] FIG7 is a schematic flowchart of a method 700 for feeding back channel information applicable to an embodiment of the present application.

[0118] FIG8 is a schematic diagram of an AI model design applicable to an embodiment of the present application.

[0119] FIG9 is a schematic diagram of a stacking method of N simulated beam channel information at input and output ends applicable to an embodiment of the present application.

[0120] FIG10 is a schematic diagram of a detachable joint feedback applicable to an embodiment of the present application.

[0121] FIG11 is a schematic flowchart of a method 1100 for feeding back channel information applicable to an embodiment of the present application.

[0122] FIG12 is a schematic diagram of a compression feedback process applicable to an embodiment of the present application.

[0123] FIG13 is a structural block diagram of a communication device applicable to an embodiment of the present application.

[0124] FIG14 is a structural block diagram of a communication device applicable to an embodiment of the present application. DETAILED DESCRIPTION

[0125] The technical solution in this application will be described below with reference to the accompanying drawings.

[0126] The technical solutions provided in this application can be applied to various communication systems, such as: fifth generation (5G) or new radio (NR) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, etc. The technical solutions provided in this application can also be applied to future communication systems, such as the sixth generation mobile communication system. The technical solutions provided in this application can also be applied to device to device (D2D) communication, vehicle-to-everything (V2X) communication, machine to machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0127] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0128] First, a communication system applicable to this application is briefly introduced as follows.

[0129] FIG1 is a schematic diagram of a communication system applicable to the communication method of an embodiment of the present application. As shown in FIG1 , the communication system 100 may include at least one network device, such as the network device 110 shown in FIG1 ; the communication system 100 may also include at least one terminal device, such as the terminal device 120 and the terminal device 130 shown in FIG1 . The network device 110 and the terminal device (such as the terminal device 120 and the terminal device 130) can communicate via a wireless link. The communication devices in the communication system, for example, the network device 110 and the terminal device 120, can communicate via multi-antenna technology.

[0130] It should be understood that FIG1 is only a simplified schematic diagram for ease of understanding, and the communication system may further include other network devices or other terminal devices, which are not shown in FIG1 .

[0131] It should also be understood that the communication system 100 shown in Figure 1 is only an example of an application scenario of an embodiment of the present application. The present application can also be applied to communication between any two devices, for example, communication between terminal devices, and communication between network devices.

[0132] Figure 2 is a schematic diagram of another communication system applicable to an embodiment of the present application. Compared to the communication system 100 shown in Figure 1, the communication system 200 shown in Figure 2 also includes an AI network element 140. The AI ​​network element 140 is used to perform AI-related operations, such as constructing a training dataset or training an AI model.

[0133] In one possible implementation, the network device 110 may send data related to the training of the AI ​​model to the AI ​​network element 140, which constructs a training data set and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. The AI ​​network element 140 may send the results of the operations related to the AI ​​model to the network device 110, and forward them to the terminal device through the network device 110. For example, the results of the operations related to the AI ​​model may include at least one of the following: an AI model that has completed training, an evaluation result or a test result of the model, etc. Exemplarily, a portion of the trained AI model may be deployed on the network device 110, and another portion may be deployed on the terminal device. Alternatively, the trained AI model may be deployed on the network device 110. Alternatively, the trained AI model may be deployed on the terminal device.

[0134] It should be understood that Figure 2 illustrates only the example of a direct connection between AI network element 140 and network device 110. In other scenarios, AI network element 140 may also be connected to a terminal device. Alternatively, AI network element 140 may be connected to both network device 110 and a terminal device simultaneously. Alternatively, AI network element 140 may be connected to network device 110 through a third-party network element. This embodiment of the present application does not limit the connection relationship between the AI ​​network element and other network elements.

[0135] The AI ​​network element 140 may also be provided as a module in a network device and / or a terminal device, for example, in the network device 110 or the terminal device shown in FIG1 .

[0136] It should be noted that Figures 1 and 2 are simplified schematic diagrams for ease of understanding. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 1 and 2. In actual applications, the communication system may include multiple network devices and 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.

[0137] In the embodiment of the present application, the network device is an access device that the terminal device accesses to the mobile communication system in a wireless manner, for example, including an access network (AN) device, such as a base station. The network device may also refer to a device that communicates with the terminal device at the air interface. The network device may include an evolved Node B (also referred to as eNB or e-NodeB) in an LTE system or an advanced long term evolution (LTE-A); the network device may also include a next generation node B (gNB) in a 5G NR system; or, the network device may also include an access node in a wireless fidelity (Wi-Fi) system; or the network device may be a relay station, an on-board device, and a future evolved public land mobile network (PLMN) device, a device in a D2D network, a device in a machine to machine (M2M) network, a device in an Internet of Things (IoT) network, or a network device in a PLMN network. The embodiments of the present application do not limit the specific technology and specific device form adopted by the network device.

[0138] In addition, the base station in the embodiment of the present application may include a centralized unit (CU) and a distributed unit (DU), and multiple DUs can be centrally controlled by one CU. CU and DU can be divided according to the protocol layer functions of the wireless network they possess, for example, the functions of the packet data convergence protocol (PDCP) layer and above protocol layers are set in the CU, and the protocol layers below the PDCP, such as the radio link control (RLC) layer and the medium access control (MAC) layer, are set in the DU. It should be noted that this division of the protocol layer is only an example, and it can also be divided in other protocol layers. The radio frequency device can be remote and not placed in the DU, or it can be integrated in the DU, or partially remote and partially integrated in the DU. The embodiment of the present application does not impose any restrictions. In addition, in some embodiments, the control plane (CP) and user plane (UP) of the CU can be separated and divided into different entities for implementation, namely the control plane CU entity (CU-CP entity) and the user plane CU entity (CU-UP entity). In this network architecture, the signaling generated by the CU can be sent to the terminal device through the DU, or the signaling generated by the UE can be sent to the CU through the DU. The DU can directly encapsulate the signaling through the protocol layer and transparently transmit it to the UE or CU without parsing it. In this network architecture, the CU is divided into a network device on the radio access network (RAN) side. In addition, the CU can also be divided as a network device on the core network (CN) side. This application does not limit this.

[0139] The access network device may also be a server, etc. For example, the network device in vehicle-to-everything (V2X) technology may be a roadside unit (RSU). The following description uses a base station as an example of an access network device. A base station can communicate with a terminal device, or it can communicate with the terminal device through a relay station. A terminal device can communicate with multiple base stations using different access technologies.

[0140] In the embodiments of the present application, core network equipment is used to implement functions such as mobility management, data processing, session management, policy and billing. The names of the devices implementing core network functions in systems with different access technologies may be different, and the embodiments of the present application are not limited to this. Taking the 5G system as an example, the core network equipment includes: access and mobility management function (AMF), session management function (SMF), policy control function (PCF) or user plane function (UPF), etc.

[0141] In an embodiment of the present application, a terminal device is a device with wireless transceiver functions, which can send signals to a network device or receive signals from a network device. The terminal device may include user equipment (UE), sometimes also referred to as a terminal, an access station, a UE station, a remote station, a wireless communication device, or a user device, etc. The terminal device is used to connect people, objects, machines, etc., and can be widely used in various scenarios, such as but not limited to the following scenarios: cellular communication, D2D, V2X, machine-to-machine / machine-type communications (M2M / MTC), Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, self-driving, remote medical, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, drones, robots and other scenarios. For example, the terminal device can be a mobile phone, a tablet computer, a computer with wireless transceiver function, a VR terminal, an AR terminal, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a smart speaker in an IoT network, a wireless terminal device in telemedicine, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, or a wireless terminal device in a smart home, etc. As an example and not a limitation, in the embodiments of the present application, the terminal device can also be a wearable device. Wearable devices can also be called wearable smart devices or smart wearable devices, etc., which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing and shoes. The various terminal devices introduced above, if located on a vehicle (for example, placed in a vehicle or installed in a vehicle), can be considered as vehicle-mounted terminal devices, which are also called on-board units (OBUs). The terminal device of the present application can also be a vehicle-mounted module, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit that is built into the vehicle as one or more components or units. The vehicle can implement the method of the present application through the built-in vehicle-mounted module, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit.

[0142] In the embodiments of the present application, the communication device for implementing the network device function may be a network device, or may be a device capable of supporting the network device to implement the function, such as a chip system, which may be installed in the network device. In the technical solutions provided in the embodiments of the present application, the technical solutions provided in the embodiments of the present application are described by taking the network device as an example of the device for implementing the network device function being a network device.

[0143] The technical solutions provided in the embodiments of the present application can be applied to the fourth generation mobile communication technology (4G) system, 5G system, NTN system, vehicle to everything (V2X), long-term evolution - vehicle network (LTE-vehicle, LTE-V), vehicle to vehicle (V2V), vehicle network, machine type communication (MTC), Internet of things (IoT), long-term evolution - machine to machine (LTE-machine to machine, LTE-M), machine to machine (M2M), Internet of Things, or future mobile communication systems.

[0144] Network devices and terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons and satellites in the air. The embodiments of this application do not limit the scenarios in which network devices and terminal devices are located. In addition, terminal devices and network devices can be hardware devices, or they can be software functions running on dedicated hardware, software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities including dedicated or general-purpose hardware devices and software functions. This application does not limit the specific forms of terminal devices and network devices.

[0145] In addition, in order to support AI technology in wireless networks, AI nodes may also be introduced into the network.

[0146] Optionally, the AI ​​node can be deployed in one or more of the following locations in the communication system: access network equipment, terminal equipment, or core network equipment. Alternatively, the AI ​​node can be deployed separately, for example, in a location other than any of the above devices, such as a host or cloud server in an over-the-top (OTT) system. The AI ​​node can communicate with other devices in the communication system, such as one or more of the following: network equipment, terminal equipment, or core network elements.

[0147] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on function, such as different AI nodes are responsible for different functions.

[0148] It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to implement different functions, or they can be network elements in hardware devices, or they can be software functions running on dedicated hardware, or they can be virtualized functions instantiated on a platform (for example, a cloud platform). This application does not limit the specific form of the above-mentioned AI nodes.

[0149] An AI node can be an AI network element or an AI module.

[0150] Figure 3 is a schematic diagram of a possible application framework in a communication system. As shown in Figure 3, network elements in the communication system are connected through interfaces (such as NG, Xn) or air interfaces. One or more AI modules are provided in one or more devices of these network element nodes, such as core network equipment, access network nodes (RAN nodes), terminals or OAM (for the sake of clarity, only one is shown in Figure 3). The access network node can be a separate RAN node, or it can include multiple RAN nodes, for example, including CU and DU. The CU and / or DU can also be provided with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are provided in the CU-CP and / or CU-UP.

[0151] The AI ​​module is used to implement the corresponding AI function. The AI ​​modules deployed in different network elements may be the same or different. The model of the AI ​​module can implement different functions according to different parameter configurations. The model of the AI ​​module can be configured based on one or more of the following parameters: structural parameters (such as the number of neural network layers, the width of the neural network, the connection relationship between layers, the weight of the neuron, the activation function of the neuron, or at least one of the bias in the activation function), input parameters (such as the type of input parameters and / or the dimension of the input parameters), or output parameters (such as the type of output parameters and / or the dimension of the output parameters). Among them, the bias in the activation function can also be called the bias of the neural network.

[0152] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or on the same node or device.

[0153] Figure 4 is a schematic diagram of a possible application framework in a communication system. As shown in Figure 4, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI ​​module shown in Figure 3, which is used to implement AI-related functions. The RIC includes a near-real-time RIC (near-real time RIC, near-RT RIC) and a non-real-time RIC (non-real time RIC, Non-RT RIC). Among them, the non-real-time RIC mainly processes non-real-time information, such as data that is not sensitive to delay, and the delay of this data can be in the order of seconds. The real-time RIC mainly processes near-real-time information, such as data that is relatively sensitive to delay, and the delay of this data is in the order of tens of milliseconds.

[0154] The near real-time RIC is used for model training and reasoning. For example, it is used to train an AI model and use the AI ​​model for reasoning. The near real-time RIC can obtain network-side and / or terminal-side information from a RAN node (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or a terminal. This information can be used as training data or reasoning data. Optionally, the near real-time RIC can deliver the reasoning result to the RAN node and / or the terminal. Optionally, the reasoning result can be exchanged between the CU and the DU, and / or between the DU and the RU. For example, the near real-time RIC delivers the reasoning result to the DU, and the DU sends it to the RU.

[0155] The non-real-time RIC is also used for model training and reasoning. For example, it is used to train an AI model and use the model for reasoning. The non-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (such as CU, CU-CP, CU-UP, DU and / or RU) and / or terminals. This information can be used as training data or reasoning data, and the reasoning results can be submitted to the RAN node and / or terminal. Optionally, the reasoning results can be exchanged between the CU and the DU, and / or between the DU and the RU. For example, the non-real-time RIC submits the reasoning results to the DU, and the DU sends it to the RU.

[0156] The near real-time RIC and non-real-time RIC may also be separately configured as a network element. Optionally, the near real-time RIC and non-real-time RIC may also be part of other devices. For example, the near real-time RIC is configured in a RAN node (e.g., a CU or DU), while the non-real-time RIC is configured in an OAM, a cloud server, a core network device, or other network device.

[0157] In order to facilitate understanding of the technical solutions of the embodiments of the present application, the terms in the embodiments of the present application are explained below.

[0158] 1. Artificial Intelligence

[0159] Artificial intelligence can be understood as the ability of machines to learn, accumulate experience, and solve problems that humans can solve through experience, such as natural language understanding, image recognition, and chess. It can also be understood as the intelligence exhibited by machines created by humans. Generally, artificial intelligence refers to the technology that represents human intelligence through computer programs. The goals of artificial intelligence include understanding intelligence by constructing computer programs that can perform symbolic reasoning or deduction.

[0160] 2. Machine learning:

[0161] Machine learning is an implementation of artificial intelligence. It's a method that empowers machines to learn, enabling them to perform tasks that are impossible to accomplish through direct programming. In practical terms, machine learning involves training models using data and then using these models to make predictions. There are many machine learning methods, such as neural networks (NNs), decision trees, and support vector machines. Machine learning theory primarily involves the design and analysis of algorithms that enable computers to learn automatically. Machine learning algorithms automatically analyze data to identify patterns and use these patterns to make predictions about unknown data.

[0162] 3. Neural Networks

[0163] Neural networks are a specific embodiment of machine learning. They are mathematical models that mimic the behavioral characteristics of animal neural networks and process information. The concept behind neural networks is derived from the neuronal structure of the brain. Each neuron performs a weighted sum operation on its input values, applying the result of this weighted summation to an activation function to generate an output.

[0164] Neural networks generally comprise a multi-layer structure, with each layer comprising one or more logical decision units, referred to as neurons. Increasing the depth and / or width of a neural network can enhance its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can be understood as the number of layers it comprises, and the number of neurons in each layer can be referred to as the width of that layer. In one possible implementation, a neural network comprises an input layer and an output layer. The input layer of the neural network processes the input through neurons and then passes the result to the output layer, which then obtains the output of the neural network. In another possible implementation, a neural network comprises an input layer, a hidden layer, and an output layer. The input layer of the neural network processes the input through neurons and then passes the result to an intermediate hidden layer. The hidden layer then passes the calculation result to the output layer or an adjacent hidden layer, which then obtains the output of the neural network. A neural network can comprise one or more sequentially connected hidden layers, without limitation.

[0165] During neural network training, a loss function can be defined. This function measures the difference between the model's predicted value and the actual value. During neural network training, the loss function describes the gap or discrepancy between the neural network's output and the ideal target value. Neural network training involves adjusting neural network parameters to ensure that the loss function's value is below a threshold or meets the target requirement. Neural network parameters can include at least one of the following: the number of neural network layers, their width, neuron weights, and parameters in the neuron activation function.

[0166] 4. AI Model

[0167] An AI model is an algorithm or computer program that can implement AI functions. The AI ​​model characterizes the mapping relationship between the input and output of the model, or 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)=ax2+b is a quadratic function model, which can be regarded as an AI model. a and b are parameters of the AI ​​model, and a and b can be obtained through machine learning training. For example, 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 (SVM), Bayesian networks, Q learning models or other machine learning (ML) models.

[0168] AI model design primarily includes a data collection phase (e.g., collecting training data and / or inference data), a model training phase, and a model inference phase. It can also include an inference result application phase. In the aforementioned data collection phase, a data source is used to provide training data sets 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. Learning the AI ​​model through the model training node is equivalent to using the training data to learn the mapping relationship between the AI ​​model's input and output. In the model inference phase, the AI ​​model, trained in the model training phase, performs inference based on the inference data provided by the data source to obtain an inference result. This phase can also be understood as inputting the inference data into the AI ​​model and obtaining an output from the AI ​​model, which is the inference result. The inference result can indicate configuration parameters used (executed) by the execution object and / or the operations performed by the execution object. In the inference result application phase, the inference result is published. For example, the inference result can be centrally planned by an actor entity, such as an actor entity that sends the inference result to one or more actors (e.g., core network devices, access network devices, or terminal devices) for execution. For example, the execution entity can also provide feedback on the performance of the AI ​​model to the data source, facilitating subsequent update and training of the AI ​​model.

[0169] It is understood that the AI ​​model can be implemented as a hardware circuit, software, or a combination of software and hardware, without limitation. Non-limiting examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application, or software application.

[0170] 5. Two-end model:

[0171] The two-end model can also be called a bilateral model, collaborative model, dual model, or two-side model. A two-end model is a model composed of multiple sub-models. The sub-models that make up the model must match each other. These sub-models can be deployed on different nodes.

[0172] The embodiments of the present application relate to an encoder for compressing CSI and a decoder for recovering compressed CSI. The encoder and decoder are used in combination, and it can be understood that the encoder and decoder are matching AI models. An encoder may include one or more AI models, and the decoder matched with the encoder also includes one or more AI models. The number of AI models included in the matching encoder and decoder is the same and corresponds one to one.

[0173] In one possible design, a matched set of encoders and decoders can be specifically two parts of the same auto-encoder (AE), as shown in Figure 5. An AE model, in which the encoder and decoder are deployed on different nodes, is a typical bilateral model. The encoder and decoder of an AE model are typically trained together and used in pairs. The encoder processes the input V to produce the processed output z, and the decoder decodes the encoder output z into the desired output V'.

[0174] An autoencoder is a type of neural network that performs unsupervised learning. Its characteristic is that it uses input data as labeled data. Therefore, an autoencoder can also be understood as a self-supervised learning neural network. Autoencoders can be used for both data compression and recovery. For example, the encoder in an autoencoder can compress (encode) data A to produce data B; the decoder in the autoencoder can decompress (decode) data B to recover data A. Alternatively, the decoder can be understood as the inverse operation of the encoder.

[0175] For example, the AI ​​model in the embodiments of the present application may include an encoder and a decoder. The encoder and decoder are used in combination, and it can be understood that the encoder and decoder are a matching AI model. The encoder and decoder can be deployed on terminal devices and network devices respectively.

[0176] Alternatively, the AI ​​model in the embodiment of the present application may be a single-end model, which may be deployed on a terminal device or a network device.

[0177] 6. Channel status information:

[0178] In communication systems (e.g., LTE or NR), network equipment needs to determine the resource, MCS, and precoding configurations for scheduling downlink data channels of terminal devices based on CSI. It can be understood that CSI is a type of channel information that can reflect channel characteristics and channel quality.

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

[0180] Taking FDD communication scenarios as an example, in FDD communication scenarios, because uplink and downlink channels are not reciprocal or cannot be guaranteed, network equipment typically transmits a downlink reference signal to the terminal device. The terminal device performs channel and interference measurements based on the received downlink reference signal to estimate the downlink CSI. The terminal device generates a CSI report based on a protocol predefined method or a network device configuration method and feeds it back to the network device to obtain the downlink CSI.

[0181] Exemplarily, the 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). The signal to interference plus noise ratio may also be referred to as the signal to interference plus noise ratio.

[0182] The RI indicates the number of downlink transmission layers recommended by the terminal device, the CQI indicates the modulation and coding scheme supported by the current channel conditions as determined by the terminal device, and the PMI indicates the precoding recommended by the terminal device. The number of precoding layers indicated by the PMI corresponds to the RI.

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

[0184] The introduction of AI technology into wireless communication networks has resulted in a CSI feedback method based on AI models. Terminal devices use AI models to compress and feedback CSI, and network equipment uses AI models to recover the compressed CSI. AI-based CSI feedback transmits a sequence (such as a bit sequence), resulting in lower overhead than traditional CSI feedback.

[0185] Taking Figure 5 as an example, Figure 5 shows a schematic diagram of CSI compression feedback according to an embodiment of the present application. The encoder in Figure 5 can be a CSI generator, and the decoder can be a CSI reconstructor. The encoder can be deployed in a terminal device, and the decoder can be deployed in a network device. The terminal device can use the encoder to generate CSI feedback information z from the original CSI information V. The terminal device reports a CSI report, which can include the CSI feedback information z. The network device can reconstruct the CSI information through the decoder, that is, obtain CSI recovery information V'.

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

[0187] The following further illustrates the training process and reasoning process of the AI ​​model in the embodiments of the present application.

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

[0189] In the field of wireless communications, the true CSI may be high-precision CSI.

[0190] The specific training process is as follows: the model training node uses the encoder to process the channel information, that is, the training sample, to obtain CSI feedback information, and uses the decoder to process the feedback information to obtain the recovered channel information, that is, the CSI recovery information. Then calculate the difference between the CSI recovery information and the corresponding sample label, that is, the value of the loss function, and update the parameters of the encoder and decoder according to the value of the loss function, so that the difference between the recovered channel information and the corresponding sample label is minimized, that is, minimize the loss function. Exemplarily, the loss function can be the minimum mean square error (MSE) or cosine similarity. Repeat the above operations to obtain an encoder and decoder that meet the target requirements. The above model training node can be a terminal device, a network device, or other network elements with AI functions in a communication system.

[0191] It should be understood that the above description uses the AI ​​model for CSI compression as an example. The AI ​​model can also be used in other scenarios in CSI feedback. For example, the AI ​​model can be used for CSI prediction, that is, predicting channel information at one or more future moments based on channel information measured at one or more historical moments. The embodiments of this application do not limit the specific use of the AI ​​model in CSI feedback scenarios.

[0192] 7. Antenna port:

[0193] An antenna port is a logical concept and does not directly correspond to a physical antenna. An antenna port is typically associated with a reference signal and can be understood as a transceiver interface on the channel through which the reference signal travels. For low-frequency systems, an antenna port may correspond to one or more antenna elements, which jointly transmit reference signals. The receiver can treat them as a whole without distinguishing between the elements. For high-frequency systems, an antenna port may correspond to a beam. Similarly, the receiver only needs to treat the beam as an interface, without distinguishing between individual elements.

[0194] 8. Port Group:

[0195] The port group refers to the antenna port group / reference signal port group. A port group can contain multiple reference signals. In the present invention, the concept of port group refers to multiple reference signals sent by the base station using the same analog weight / analog beam. Therefore, in the present invention, the base station can use one or more reference signal port groups to measure the channel information under an analog beam.

[0196] 9. Beam:

[0197] A beam is a communication resource. A beam can be wide, narrow, or other types of beams. Beam formation can be achieved through beamforming or other techniques. Beamforming techniques can include digital beamforming, analog beamforming, or hybrid digital / analog beamforming. Different beams can be considered different resources. Different beams can transmit the same or different information. Optionally, multiple beams with the same or similar communication characteristics can be considered a single beam. A beam can include one or more antenna ports for transmitting data channels, control channels, and sounding signals. For example, a transmit beam refers to the distribution of signal strength in different spatial directions after a signal is transmitted by an antenna, while a receive beam refers to the distribution of signal strength in different spatial directions of a wireless signal received by an antenna. It is understood that the one or more antenna ports that form a beam can also be considered an antenna port set. Beams can also be represented in the protocol as spatial filters.

[0198] 10. Reference signal:

[0199] According to the Long Term Evolution (LTE) / NR protocol, at the physical layer, uplink communication includes the transmission of uplink physical channels and uplink signals. Uplink physical channels include the random access channel (PRACH), the physical uplink control channel (PUCCH), and the physical uplink shared channel (PUSCH). Uplink signals include the channel sounding signal (SRS), the uplink control channel demodulation reference signal (PUCCH-DMRS), the uplink data channel demodulation reference signal (PUSCH-DMRS), the uplink phase noise tracking reference signal (PTRS), and the uplink positioning RS. Downlink communication includes the transmission of downlink physical channels and downlink signals. The downlink physical channels include the physical broadcast channel (PBCH), the physical downlink control channel (PDCCH), the physical downlink shared channel (PDSCH), etc. The downlink signals include the primary synchronization signal (PSS) / secondary synchronization signal (SSS), the downlink control channel demodulation reference signal PDCCH-DMRS, the downlink data channel demodulation reference signal PDSCH-DMRS, the phase noise tracking signal PTRS, the channel status information reference signal (CSI-RS), the cell reference signal (CRS), the time / frequency tracking reference signal (TRS), the LTE / NR positioning signal (positioning RS), etc.

[0200] 11. Precoding and codebook:

[0201] Multiple-input, multiple-output (MIMO) technology increases system capacity and improves throughput. The mathematical expression is y = Hx + n, where y is the received signal, H is the MIMO channel, x is the transmitted signal, and n is the noise. In a communication system with multiple antennas, the signals from multiple transmitting antennas are superimposed on any receiving antenna. Therefore, the method used by the transmitter to transmit signals affects system performance, and recovering the transmitted signal at the receiver is often complex. In this context, precoding is used to reduce system overhead and maximize MIMO system capacity, while also reducing the complexity of the receiver's implementation of mitigating inter-channel interference. In this case, the mathematical expression is y = Hx + n, where P is the precoding matrix (or vector). To simplify implementation complexity, P can be selected from a predefined set of matrices (or vectors), known as a codebook. This method is also known as a codebook-based transmission method. If the transmitter has full information about H, P can be automatically determined by the transmitter. This method is also known as a non-codebook (NCB) transmission method.

[0202] It should be understood that, in this application, indication includes direct indication (also known as explicit indication) and implicit indication. Direct indication of information A refers to including information A; implicit indication of information A refers to indicating information A through the correspondence between information A and information B and the direct indication of information B. The correspondence between information A and information B can be predefined, pre-stored, pre-burned, or pre-configured.

[0203] It should be understood that, in this application, information C is used to determine information D, which includes both information D being determined solely based on information C and information D being determined based on information C and other information. Furthermore, information C can also be used to determine information D indirectly, for example, where information D is determined based on information E, and information E is determined based on information C.

[0204] In addition, in each embodiment of the present application, "network element A sends information A to network element B" can be understood as the destination end of the information A or the intermediate network element in the transmission path between the destination end and the network element B, which may include directly or indirectly sending information to network element B. "Network element B receives information A from network element A" can be understood as the source end of the information A or the intermediate network element in the transmission path between the source end and the network element A, which may include directly or indirectly receiving information from network element A. The information may be processed as necessary between the source end and the destination end of the information transmission, such as format changes, but the destination end can understand the valid information from the source end. Similar expressions in this application can be understood similarly and will not be elaborated here.

[0205] Exemplarily, the network device may be a core network device, an access network node (RAN node) or one or more devices in OAM as shown in FIG1 . For example, the AI ​​module may be the RIC shown in FIG4 , such as a near real-time RIC or a non-real-time RIC. For example, the near real-time RIC is set in the RAN node (e.g., CU, DU), while the non-real-time RIC is set in the OAM, in the cloud server, in the core network device, or in other network devices. The RIC can be obtained by obtaining a subset from multiple terminal devices from a RAN node (e.g., CU, CU-CP, CU-UP, DU and / or RU), reorganizing it into a training data set #2, and training based on the training data set #2.

[0206] For example, the near real-time RIC and the non-real-time RIC may also be separately configured as a network element, and the network device may be the near real-time RIC or the non-real-time RIC.

[0207] Currently, in higher-frequency communication systems, network equipment (and terminals in some frequency bands) typically use large-scale array antennas (e.g., 500-1000 antenna elements) to counteract path loss caused by higher frequency bands through higher array gain, thereby improving coverage. From the perspective of network equipment implementation, even with large arrays, different frequency bands and array sizes use different array weighting methods (i.e., beamforming methods). Based on the beamforming implementation scheme, these can be roughly divided into the following three categories:

[0208] The first type: digital beamforming (DBF). Each antenna element or group of antenna elements is directly connected to a digital channel. Because each antenna signal is directly converted to the digital domain, subsequent array weighting is performed in the digital domain, hence the name digital beamforming. The high degree of freedom of signal processing in the digital domain allows for complex signal processing methods. Therefore, DBF architectures offer superior performance at the same array scale. On the other hand, due to the high power consumption and cost of analog-to-digital converters (ADCs / DACs), especially at large bandwidths, DBF is more expensive at the same array scale.

[0209] The second type: analog beamforming (ABF). Each antenna element or group of antenna elements is connected to an analog phase shifter. Multiple antenna elements are combined in the analog domain and then passed through a digital-to-analog / analog-to-digital converter. Compared to DBF, ABF only has one digital-to-analog / analog-to-digital converter for the entire array. The ABF architecture has the advantages of low cost and power consumption. However, the phase shifter settings in the analog domain determine the beam direction after beamforming. Since the signals are directly combined electronically in the analog domain, they cannot be weighted using digital signal processing like DBF. ABF requires pre-configured phase shifter settings during transmission and reception (that is, to point the analog beam toward the target terminal). This process requires beam scanning during the link establishment phase, which introduces additional latency. Furthermore, if the analog beam is obstructed or moves, causing misalignment, the system link quality will rapidly degrade, even to the point of termination. Therefore, ABF may not offer the same communication reliability as DBF.

[0210] The third type: hybrid beamforming (HBF), is an intermediate form between ABF and DBF. HBF has a certain number of digital ports to support digital beamforming, and each digital port drives an ABF subarray. Compared to ABF, for the same array size, each digital channel drives a smaller analog subarray, resulting in a wider beam, improved reliability, and reduced beam scanning overhead. The ratio of digital ports to analog phase shifters in HBF varies depending on the frequency and system design requirements. For example, high-frequency bands have a small number of digital ports (4 to 16), and each digital channel corresponds to a larger number of analog phase shifters (16 to 32), closer to ABF. Low-frequency band systems, on the other hand, have more digital ports (32 to 128) and fewer analog phase shifters (2 to 10) per digital channel.

[0211] Both HBF and ABF architectures use simulated beams. Signal quality is optimized when the simulated beam is aligned with the communication target. The direction of the simulated beam (determined by the beam weight) must be configured before transmission or reception. The process by which network equipment selects the simulated beam for a particular terminal device is called beam training or beam scanning. Beam scanning typically involves the network device sending reference signals using different simulated beam weights. The terminal device then measures the reference signals and provides feedback to help the network equipment determine the beam with the best quality.

[0212] It should be understood that the HBF architecture has both digital ports and analog phase shifter arrays.

[0213] FIG6 shows a schematic diagram of a hardware architecture 600 .

[0214] Architecture 600 illustrates the hardware structure of a network device (HBF). The HBF architecture of a terminal device is similar to that of a network device (HBF). The following description uses the network device (HBF) as an example. This architecture includes a processor 610, a memory 620, and a signal transceiver unit 630. The signal transceiver unit includes a transmitter 631, a receiver 632, and an antenna 633. The transmitter and receiver receive and transmit signals via the antenna. In the antenna 633 module, each box represents a digital channel, with F in the box representing the digital precoding weight. A phase shifter (circle with an oblique arrow) represents an analog channel, connecting one or multiple arrays. In practice, a single phase shifter can control multiple arrays, or phase shifters can be cross-connected to arrays.

[0215] Therefore, during communication, the HBF architecture has multiple analog beams to choose from. Under each analog beam, the weighted values ​​and MCS of the digital ports used to serve a terminal need to be fed back using CSI information. This means that the network device needs to instruct the terminal device to measure the CSI information under each analog beam separately and then provide feedback. This process can cause the following problems: First, when the terminal device provides feedback, it may support different feedback overheads. For the network device, different AI models need to be configured to support different reporting capabilities. The terminal device uses different AI models for compression, which increases the storage overhead or switching overhead of the network device and the terminal device.

[0216] Furthermore, in the prior art, under the HBF structure, the channel information corresponding to multiple analog beams needs to be measured and fed back independently. When there are N analog beams, the feedback overhead needs to be increased N times. In fact, the coverage areas of multiple analog beams will overlap to a certain extent, or the channel information between multiple analog beams is correlated. The prior art does not consider the correlation of channels between analog beams, resulting in large feedback overhead and low efficiency. Therefore, the solution of the present application requires joint feedback for the channel information corresponding to multiple analog beams. In this case, the AI ​​model needs to support joint compression under different analog beams. According to the solution of the prior art, multiple different AI models are required to support different analog beam compressions, which greatly increases the complexity of implementation and protocol design. At the same time, the storage of multiple AI models will also lead to an increase in the storage cost of terminal devices and network devices, and switching between multiple AI models will also increase the switching overhead of network devices and terminal devices.

[0217] In view of this, an embodiment of the present application provides a method and a communication device for feeding back channel status, which can reduce the storage / switching overhead of network equipment and terminal equipment.

[0218] Next, a method for feeding back channel status provided by an embodiment of the present application is described. The embodiment provided by the present application can be applied to the communication systems shown in FIG1 and FIG2 above, without limitation.

[0219] In the following embodiments, as examples, the encoder is a terminal device and the decoder is a network device; or the encoder is a terminal device and the decoder is another terminal device; or the encoder is a network device and the decoder is another network device; or the encoder is a network device and the decoder is a terminal device, without limitation. It is understood that the encoder mentioned below can be replaced by an encoding device, and the decoder can be replaced by a decoding device. In addition, it is also understood that the encoder mentioned below can include circuits other than the AI ​​model, such as processing circuits, storage circuits, transceiver circuits, etc.

[0220] In the following embodiments, the encoder can deploy multiple AI models, allowing the encoder to perform encoding based on the deployed AI models, such as compressing channel information. The decoder can also deploy multiple AI models, allowing the decoder to perform decoding based on the deployed AI models, such as recovering channel information from compressed information. For simplicity, the AI ​​model deployed by the encoder is referred to as the encoder's AI model, and the AI ​​model deployed by the decoder is referred to as the decoder's AI model.

[0221] Figure 7 is a schematic flow chart of a method 700 for feedback of channel status provided by an embodiment of the present application. For ease of description, method 700 is exemplified by taking an encoder and a decoder as the execution subjects of the interaction. It can be understood that the encoder can be a terminal device or a component of a terminal device (such as a chip or circuit), the decoder can be a network device or a component of a network device (such as a chip or circuit), or the encoder or decoder can also be an AI node or a component of an AI node (such as a chip or circuit), without limitation. The method 700 shown in Figure 7 may include the following steps.

[0222] 710. The encoder compresses M channel information to obtain a first sequence.

[0223] Among them, the M channel information is part or all of the L channel information measured by the terminal device, the L channel information corresponds to the L simulated beams, L is less than or equal to N, N is the number of channel information determined based on the reference signal on the antenna port group, and N is a positive integer.

[0224] The N channel information corresponds to N simulated beams, and N can also be understood as the maximum number of simulated beams.

[0225] In the present application, the network device and the terminal device may predefine or predetermine the maximum number N of simulated beams for joint feedback and the maximum feedback overhead X.

[0226] The maximum number N of simulated beams may be determined by the network device. For example, the network device may configure the total number of simulated beams.

[0227] In a possible implementation, the N simulated beams are different from each other, and there is an overlapping area in the coverage range of the N simulated beams.

[0228] For example, the coverage of some of the N simulated beams overlaps. For example, N is 5, including beam A, beam B, beam C, beam D, and beam E, where the coverage of beam A and beam B overlaps.

[0229] For example, the coverage areas of the N simulated beams all overlap. For example, N is 5, including beam A, beam B, beam C, beam D, and beam E, wherein the coverage areas of the five beams all overlap.

[0230] The N simulated beams correspond one-to-one to the N channel information. In other words, the N channel information is the channel information of the N simulated beams. For example, the channel information can be channel state information (CSI). For details, please refer to the description of CSI above and will not be repeated here.

[0231] In one possible implementation, the N channel information corresponds to N sequence numbers, and these N sequence numbers correspond one-to-one with the N simulated beams. In other words, the N simulated beams are associated with the N channel information through these N sequence numbers. For example, if N is 3, and the three simulated beams are beam A, beam B, and beam C, beam A corresponds to sequence number 1, beam B corresponds to sequence number 2, and beam C corresponds to sequence number 3. At the same time, channel information A corresponds to sequence number 1, channel information B corresponds to sequence number 2, and channel information C corresponds to sequence number 3. Therefore, channel information A corresponds to beam A, channel information B corresponds to beam B, and channel information C corresponds to beam C.

[0232] In other words, the N channel information corresponds to K port groups. The port group may be a CSI-RS port group.

[0233] For example, the K port groups are used to determine K groups of channel information, and the K groups of channel information and N groups of combination coefficients are used to determine N groups of channel information, where N is greater than or equal to K and K is a positive integer.

[0234] Specifically, the following uses the precoding matrix as an example of channel information, which can be divided into the following two cases:

[0235] Case 1: N is equal to K. That is, there are K port groups, which are used to determine N=K groups of channel coefficients.

[0236] For example, a network device configures N port groups for a terminal, each corresponding to an analog beam. These K port groups are used to measure the channel coefficients {H1, H2, H3, ...} for N different analog beams (with analog weights {w1, w2, w3, ...}) in the network device (e.g., base station) arrays A1, A2, D1, and D2 (number of horizontal and vertical arrays, number of digital ports). The terminal then calculates the precoding matrices {v1, v2, v3, ...} for these N different analog beams using SVD decomposition.

[0237] Case 2: N is greater than K. That is, K port groups are used to determine N groups of channel coefficients.

[0238] For example, as shown in FIG11 , K port groups are used to obtain channel coefficients (or channel responses) of K port groups, which are respectively denoted as h1, h2, h3, ..., h k Based on the channel information of K port groups and the channel combination coefficient The channel coefficients under N simulated beams can be obtained The terminal then performs SVD decomposition to calculate the precoding matrices {v1, v2, v3, ...} for N different simulated beams.

[0239] That is, when the N channel information are N precoding matrices, the N channel information are decomposed according to the channel coefficients of N analog beams, or the N channel information are decomposed according to the channel information of K port groups and the channel combination coefficients.

[0240] In this application, the terminal device can measure L channel information and compress and feedback M of the channel information.

[0241] Next, a method of determining M pieces of channel information from L pieces of channel information is exemplified.

[0242] In the present application, the network device or terminal device may determine to report M channel information and corresponding feedback overhead Y, where the M channel information corresponds to M simulated beams.

[0243] In one implementation, the network device or the terminal device may determine to feed back M channel information and corresponding feedback overhead Y according to the priority information.

[0244] The priority information may be the priorities corresponding to the L simulated beams.

[0245] Exemplarily, the network device determines M channel information and corresponding feedback overhead Y according to the priorities corresponding to the L simulated beams.

[0246] In this case, the network device may indicate to the terminal device the correspondence between the M channel information and the N channel information.

[0247] Exemplarily, the terminal report can determine M channel information and corresponding feedback overhead Y based on the priorities corresponding to L simulated beams configured based on the network device and the measurement results of the L simulated beams.

[0248] In this case, the terminal device may indicate the correspondence between the M channel information and the N channel information to the network device.

[0249] The measurement results of the L channel information include but are not limited to CQI, RSRP measurement, etc.

[0250] In the present application, it is determined to perform joint compression feedback on M channel information to obtain a first sequence.

[0251] In the present application, the first sequence is a compression result of M channel information, and the first sequence is X joint feedback amounts corresponding to N channel information.

[0252] It should be understood that the first sequence is a joint feedback amount of N channel information dimensions. In other words, the first sequence can indicate N channel information. In other words, the first sequence includes N channel information.

[0253] The first sequence may be a sequence before quantization. For example, the first sequence is a one-dimensional real / complex number sequence.

[0254] The first sequence may be a quantized sequence. For example, the first sequence includes X bits.

[0255] In the following embodiments, the joint feedback amount is exemplified by taking bit overhead as an example, but is not limited in any way.

[0256] First of all, it should be noted that the input and output results of the AI ​​model are fixed, so the input end of the encoder is fixed to a precoding matrix of N simulated beam dimensions. Similarly, the output end of the encoder outputs an output matrix of N simulated beam dimensions.

[0257] The following is a detailed description of the input of the encoder and the output of the decoder in this application in conjunction with the AI ​​model design schematic shown in Figure 8.

[0258] As shown in Figure 8, the input of the encoder is the channel information of N beam dimensions. After compression by the encoder, a joint feedback amount of X bits of overhead is obtained. After the joint feedback amount is input into the decoder, the channel information of N beam dimensions is reconstructed.

[0259] It should be noted that the stacking method of channel information can be predefined at the input and output ends of the encoder and the input and output ends of the decoder, or in other words, the stacking method of input matrices corresponding to different analog beams can be predefined.

[0260] The stacking method can be understood as inputting the analog beams corresponding to the channel information to be compressed into an input matrix of N analog beam dimensions in a certain order or arrangement. In other words, the input / output of the encoder or decoder requires partial input or output. Following a certain arrangement or position rule can ensure the correspondence between the input and output, thereby ensuring the performance of joint compression and reconstruction.

[0261] For example, at the input end of the encoder, M simulated beam precoding matrices are filled into the corresponding beam positions of the encoder input matrix. The remaining unfilled positions can be filled into the input matrix by inputting a fixed amount. The input matrix with N beam dimensions is obtained and sent to the encoder for joint compression to obtain the full feedback amount with X bits.

[0262] Exemplarily, as shown in FIG8 , at the input end of the encoder, N channel information define the logical sequence number Beam1-Beam N and are bound to the positions of the N analog beams in the physical sense. The input end of the encoder defines the stacking arrangement of the N analog beam channel information; then, the N channel information is input into the encoder, and the output end of the decoder defines the stacking arrangement of the N analog beam channel information.

[0263] Figure 9 shows a schematic diagram of the stacking of N simulated beam channel information at the input and output ends, wherein the arrangement order of the N simulated beams (from 1 to N) is not limited.

[0264] 720. Determine a second sequence based on the first sequence.

[0265] The second sequence is Y joint feedback quantities corresponding to M channel information.

[0266] It should be understood that the second sequence is a joint feedback amount of M channel information dimensions. In other words, the second sequence can indicate M channel information. In other words, the first sequence includes M channel information.

[0267] The second sequence may be a sequence before quantization. For example, the second sequence may be a one-dimensional real number or complex number sequence.

[0268] The second sequence may be a quantized sequence. For example, the second sequence includes Y bits.

[0269] In one implementation, the correspondence between the stacking positions of N channel information at the encoder input end and the stacking positions of M channel information at the encoder input end is determined according to the first sequence, and Y joint feedback amounts corresponding to the M channel information are determined according to the correspondence.

[0270] For example, based on the relationship between the position corresponding to the input matrix under the N-beam dimension of the encoder input end and the corresponding beam position of the M analog beam precoding matrices filled into the N-beam input matrix, the positions of the M analog beam precoding matrices filled into the N-beam input matrix are determined, or in other words, the remaining unfilled positions are excluded to obtain Y partial joint feedback amounts.

[0271] In this application, the first sequence is the joint feedback amount of channel information in N dimensions, and the second sequence is the joint feedback amount of channel information in M ​​dimensions. The contents of the first sequence and the second sequence are described below with reference to FIG9 .

[0272] FIG10 shows a schematic diagram of a detachable joint feedback provided in an embodiment of the present application.

[0273] As shown in Figure 10, the N beam joint feedback amount consists of N+1 parts, where the first N parts {X1, X2, ..., X N} represent the specific characteristics of N simulated beams, and the N+1 part X com Represents the common characteristics of the N simulated beams. If the terminal needs to report the channel information of some beams, it only needs to report the feedback corresponding to the unique characteristics of the partial beams and the common characteristics of the N simulated beams.

[0274] In the present application, the first sequence includes X bits of joint feedback corresponding to N beams. The X bits of joint feedback are composed of first features and second features corresponding to N channel information. In other words, the first sequence includes N first features and O second features. The first feature indicates a specific feature of each channel information, and the second feature indicates a common feature of the N channel information.

[0275] Among them, specific features refer to features unique to each channel information, and common features refer to features shared by multiple channel information. Specific features or common features are merely example terms and are not limited in this application.

[0276] The second sequence includes Y bits of joint feedback corresponding to M beams. The Y bits of joint feedback are composed of the first and second features corresponding to the M channel information. In other words, the first sequence includes M first features and P second features. The first feature indicates a specific feature of each channel information, and the second feature indicates a common feature of the M channel information. P is less than or equal to 0, and O can be 1.

[0277] In one possible implementation, M first features are determined from N first features based on the correspondence between the stacking positions of N channel information at the encoder input end and the stacking positions of M channel information at the encoder input end, P second features corresponding to the M channel information are determined from O second features based on the correspondence between the stacking positions of N channel information at the encoder input end and the stacking positions of M channel information at the encoder input end, and a second sequence is determined based on the M first features and the P second features.

[0278] Among them, the positions of the N first features in the first sequence correspond to the positions of the M first features in the second sequence, and the positions of the O second features in the first sequence correspond to the positions of the P second features in the second sequence.

[0279] In other words, when M simulated beams are filled into the input matrix of N simulated beam dimensions, they are filled in a certain regular order, so the M simulated beams have a corresponding relationship in the input matrix of N simulated beam dimensions. Correspondingly, the first features and second features corresponding to the M beams also have a corresponding relationship with the first features and second features of the N beam dimensions.

[0280] In one possible implementation, the N first features and the O second features include a feature principal component and a feature supplementary component, wherein the feature principal component is used to complete the reconstruction of the channel information, and the feature supplementary component is used to supplement the accuracy of the channel information.

[0281] Therein, the feature supplement components for a first feature or a second feature may include multiple feature supplement components, and feature supplement components with different feedback numbers indicate different feedback accuracies.

[0282] In one possible implementation, each of the M first features includes a feature principal component, some or all of the M first features include feature supplementary components, each of the P second features includes a feature principal component, and some or all of the P second features include feature supplementary components.

[0283] It can be understood that the accuracies corresponding to the M channel information fed back can all be high precision, that is, the main component and supplementary component of the first feature and the main component and supplementary component of the second feature corresponding to the M channel information are fed back, or part of it can be high precision, that is, the main component and supplementary component of the first feature and the main component and supplementary component of the second feature corresponding to part of the channel information are fed back, and the other part of the channel information only feeds back the corresponding main component of the first feature and the main component of the second feature. This application does not limit this.

[0284] Furthermore, high-precision feedback can also feed back different numbers of supplementary components for different channel information to achieve feedback at different precision levels.

[0285] For example, for the same channel information, the more supplementary components fed back, the higher the accuracy.

[0286] It should be noted that the accuracy levels of different channel information feedback may be the same or different, and this application does not limit this.

[0287] For example, in Figure 10, the feedback corresponding to each feature consists of the feature's principal component and Q supplementary components (Q ≥ 1), supporting feedback at Q+1 levels of accuracy. The principal component can independently reconstruct the channel information for that portion, while the supplementary component serves as a high-precision supplement to that portion of the channel information.

[0288] It should be understood that if the terminal supports high-precision reporting, it can simultaneously feedback the main component of the feature and q supplementary components (Q ≥ q ≥ 1); if the terminal only supports low-precision reporting, it can feedback only the main component of the feature to complete the reconstruction of the channel information. This feedback amount design supports the terminal reporting different beam characteristics with different accuracies.

[0289] In one implementation, the characteristic principal component of each first feature among the N first features has a corresponding relationship with the characteristic principal components of some or all of the first features among the M first features; the supplementary component of each first feature among the N first features has a corresponding relationship with the supplementary components of some or all of the first features among the M first features; the characteristic principal component of each second feature among the O second features has a corresponding relationship with the characteristic principal component of each second feature among the P second features, and the characteristic supplementary component of each second feature among the O second features has a corresponding relationship with the characteristic supplementary component of each second feature among the P second features.

[0290] The positions of the N first features in the first sequence correspond to the positions of the M first features in the second sequence, and the positions of the O second features in the first sequence correspond to the positions of the P second features in the second sequence. Accordingly, for the features (first features and second features) in the first sequence and the features (first features and second features) in the second sequence that have a corresponding relationship, the feature principal components and feature supplementary components included in each of them also have a corresponding relationship.

[0291] In the present application, the accuracy level corresponding to the M channel information can be determined by the network device or by the terminal device.

[0292] Exemplarily, the network device may determine the accuracy categories corresponding to the M channel information according to the priorities of the L channel information.

[0293] Exemplarily, the terminal device may determine the accuracy categories corresponding to the M channel information based on measurement results of the L channel information and / or priority information of the L channel information.

[0294] It should be understood that when the encoder reports different characteristics of M simulated beams with different accuracies, the terminal may give priority to reporting the unique characteristic main components and common characteristic main components of the M simulated beams to ensure that the decoder can complete the reconstruction of the complete channel information of the M simulated beams; then, the encoder can report the supplementary components of the simulated beams based on the different priorities of the simulated beams to achieve feedback with higher accuracy.

[0295] 730. The encoder sends the second sequence to the decoder.

[0296] Accordingly, the decoder receives the second sequence.

[0297] 740. The decoder determines the first sequence according to the second sequence.

[0298] It should be understood that the input and output of the encoder and decoder are matrices with a fixed N beam dimension, that is, at the input of the decoder, the joint feedback amount of M channel information needs to be restored to the joint feedback amount corresponding to N channel information.

[0299] In a possible implementation, a correspondence between stacking positions of M channel information at a decoder input and stacking positions of N channel information at a decoder input is determined, and X joint feedback amounts corresponding to the N channel information are determined based on the correspondence.

[0300] For example, based on the relationship between the position corresponding to the input matrix under the N-beam dimension at the encoder input end and the corresponding beam positions of the M analog beam precoding matrices filled into the N-beam input matrix, the corresponding relationship between the stacking positions of the M channel information at the decoder input end and the stacking positions of the N channel information is determined, the M analog beam coding matrices are filled into the positions of the N-beam input matrix, and the beam positions corresponding to the non-feedback portion are filled with a fixed amount, thereby obtaining X full feedback amounts.

[0301] In one possible implementation, N first features and O second features are determined based on the correspondence between the stacking positions of the N channel information at the encoder input end and the stacking positions of the M channel information at the encoder input end, and X joint feedback amounts corresponding to the N channel information are determined based on the M first features and the P second features.

[0302] Among them, the positions of the N first features in the first sequence correspond to the positions of the M first features in the second sequence, and the positions of the O second features in the first sequence correspond to the positions of the P second features in the second sequence.

[0303] In other words, when M simulated beams are filled into the input matrix of N simulated beam dimensions, they are filled in a certain regular order, so the M simulated beams have a corresponding relationship in the input matrix of N simulated beam dimensions. Correspondingly, the first features and second features corresponding to the M beams also have a corresponding relationship with the first features and second features of the N beam dimensions.

[0304] In one possible implementation, the M first features and the P second features include a feature principal component and a feature supplementary component, wherein the feature principal component is used to complete the reconstruction of the channel information, and the feature supplementary component is used to supplement the accuracy of the channel information.

[0305] Therein, the feature supplement components for a first feature or a second feature may include multiple feature supplement components, and feature supplement components with different feedback numbers indicate different feedback accuracies.

[0306] In one possible implementation, each of the N first features includes a feature principal component, some or all of the N first features include a feature supplementary component, each of the O second features includes the feature principal component, and some or all of the O second features include the feature supplementary component.

[0307] It can be understood that the accuracies corresponding to the M channel information actually fed back can all be high precision, that is, the main component and supplementary component of the first feature and the main component and supplementary component of the second feature corresponding to the M channel information are fed back, or part of it can be high precision, that is, the main component and supplementary component of the first feature and the main component and supplementary component of the second feature corresponding to part of the channel information are fed back, and the other part of the channel information only feeds back the corresponding main component of the first feature and the main component of the second feature. This application does not limit this.

[0308] In one implementation, the characteristic principal component of each first feature among the N first features has a corresponding relationship with the characteristic principal components of some or all of the first features among the M first features; the supplementary component of each first feature among the N first features has a corresponding relationship with the supplementary components of some or all of the first features among the M first features; the characteristic principal component of each second feature among the O second features has a corresponding relationship with the characteristic principal component of each second feature among the P second features, and the characteristic supplementary component of each second feature among the O second features has a corresponding relationship with the characteristic supplementary component of each second feature among the P second features.

[0309] The positions of the N first features in the first sequence correspond to the positions of the M first features in the second sequence, and the positions of the O second features in the first sequence correspond to the positions of the P second features in the second sequence. Accordingly, for the features (first features and second features) in the first sequence and the features (first features and second features) in the second sequence that have a corresponding relationship, the feature principal components and feature supplementary components included in each of them also have a corresponding relationship.

[0310] In this application, the accuracy levels corresponding to the M channel information reported can be the same or different, and this application does not limit this.

[0311] In this application, the accuracy level corresponding to the reported M channel information can be determined by the network device or the terminal device.

[0312] For the reporting and determination of the accuracy level, please refer to the detailed description of the encoder side, which will not be repeated here.

[0313] It should be understood that when the encoder reports different characteristics of M simulated beams with different accuracies, the terminal can give priority to reporting the unique characteristic main components and common characteristic main components of the M simulated beams, and the decoder completes the reconstruction of the complete channel information of the M simulated beams based on the reported unique characteristic main components and common characteristic main components of the M simulated beams; then, the encoder reports the supplementary components of the simulated beams based on the different priorities of the simulated beams, and the decoder can further reconstruct the corresponding channel information based on the supplementary components corresponding to the M simulated beams, thereby achieving higher-precision reconstruction of channel information.

[0314] 750. The decoder reconstructs the first sequence to obtain M channel information.

[0315] The matrix of N simulated beam dimensions corresponding to the first sequence is input to the decoder, and the decoder reconstructs the first sequence to obtain an output matrix of N simulated beam dimensions.

[0316] Furthermore, the decoder selects information on beam positions corresponding to the M channel information from the output matrix based on the correspondence between the stacking positions of the M channel information at the decoder input end and the stacking positions of the N channel information at the decoder input end, thereby obtaining an output matrix of the M channel information.

[0317] In other words, the decoder reconstructs the input matrix of N analog beam dimensions and obtains an output matrix corresponding to N analog beam dimensions. The input matrix of N analog beam dimensions includes the input matrix of M analog beams and NM fixed quantities. Therefore, in order to obtain the output matrix of M analog beams, it is necessary to cut out M channel information based on the correspondence between the stacking position of the M channel information at the decoder input end and the stacking position of the N channel information at the decoder input end. In other words, the fixed amount of channel information is eliminated according to the correspondence, so as to obtain the channel information corresponding to the M analog beams.

[0318] According to this technical solution, the AI ​​model can be used to jointly compress the channel information of N simulated beams of fixed dimension and feed it back to the decoder. The decoder uses the AI ​​model to reconstruct the channel information of N simulated beams. The encoder can carry M channel information through the input matrix corresponding to the N simulated beams of fixed dimension, thereby realizing the compression and feedback of partial channel information, avoiding the storage / switching overhead of multiple AI models, and reducing the complexity of implementation. In addition, the priority of partial feedback is defined to ensure the efficiency and reliability of multi-beam reporting. Furthermore, for the M channel information of compressed feedback, the accuracy of the corresponding channel information can be fed back through the characteristic main component and characteristic supplementary component corresponding to each channel information, thereby realizing different simulation beams can be reported with different accuracy, increasing the flexibility of the feedback mechanism.

[0319] The feedback method proposed in this application is described in detail below in conjunction with multi-beam channel information measurement reporting.

[0320] Figure 11 is a schematic flow chart of a method for feeding back channel status provided by an embodiment of the present application. For ease of description, method 1100 is exemplarily illustrated by taking a network device and a terminal device as the execution subjects of the interaction. The encoder and decoder can be deployed on the terminal device and the network device, respectively. Among them, the encoder or decoder can also be an AI node or a component of an AI node (such as a chip or circuit), without limitation. The method 1100 shown in Figure 11 may include the following steps.

[0321] 1110. The network device sends configuration information to the terminal device.

[0322] The configuration information may include reference signal configuration information and channel information reporting configuration information.

[0323] The reference signal configuration information may include information related to reference signal port grouping and information related to reference signal resource grouping.

[0324] Information related to the reference signal port grouping, for example, the number of reference signal port groups K, the number of ports in each group is PCSI-RS,k , k=0,1,...,K-1. For example, the number of ports in each port group is exactly the same, for example, the number of ports in each port group is P CSI-RS .

[0325] Information related to the reference signal resource grouping, for example, the number of reference signal resource groups K, the number of reference signal resources and / or ports in each group P respectively CSI-RS,k ,k=0,1,...,K-1, illustratively, the total number of ports in each resource group is exactly the same, for example, the total number of ports in each resource group is P CSI-RS .

[0326] Exemplarily, the reference signal resource grouping may be CSI-RS port group #1, CSI-RS port group #2, ..., CSI-RS port group #K.

[0327] In this embodiment, the reference signal port group is used as an example to describe the reference signal. Similarly, this solution is also applicable to the scenario of reference signal resource grouping. This application does not limit this.

[0328] The channel information reporting configuration information may include the content of reporting and measurement and the number of reporting and measurement.

[0329] Exemplarily, the number of reports and measurements may be the number L of measured channel information, where L is less than or equal to N.

[0330] It should be noted that in this embodiment, N simulated beams are used as the maximum number of simulated beams for joint feedback, and X joint overhead is used as the maximum feedback amount for joint feedback. The determination of the maximum number of simulated beams and the maximum joint feedback amount can refer to step 710 in method 700, and this application does not limit this.

[0331] In a possible implementation, the network device determines the number M of simulated beams and the feedback overhead Y that need to be reported.

[0332] Exemplarily, the network device determines the reported number of simulated beams M and the feedback overhead Y based on the priority of the joint feedback of each simulated beam. In this case, L can be equal to M, and the network device can send the measured correspondence between M simulated beams and N simulated beams and the feedback overhead Y to the terminal device.

[0333] In another possible implementation, the terminal device determines the reported number M of simulated beams and the feedback overhead Y.

[0334] For example, the terminal device measures L channel information and determines M reported analog beams and feedback overhead Y based on the priority of joint feedback (for example, based on CQI / RSRP measurement). In this case, L is less than or equal to the maximum number of analog beams N, and M is less than or equal to L. The terminal device can report the correspondence between the M analog beams and the N analog beams determined by itself, as well as their feedback overhead Y, to the network device.

[0335] The configuration information may also include a CSI-RS transmission method and / or a relationship between a CSI-RS port group and multi-analog beam channel information (see 1120 below for details).

[0336] The configuration information may also include other content, which is not limited in this application.

[0337] 1120. The network device sends a reference signal to the terminal device.

[0338] The network device sends a reference signal to the terminal device through the resources configured by the reference signal configuration information.

[0339] In the following embodiments, the reference signal takes CSI-RS as an example, which is not limited in this application.

[0340] The CSI-RS is sent on the resources configured by the resource configuration information.

[0341] In a possible implementation, different CSI-RS port groups are sent using a time division method, that is, different CSI-RS port groups are sent on different time domain resources (eg, time slots or OFDM symbols).

[0342] The CSI-RS port group is sent in a time-division manner, which facilitates the transmission of multiple reference signals based on different analog beams under the HBF architecture and realizes the measurement of channel information.

[0343] In another possible implementation, different CSI-RS port groups may be sent using a frequency division method, i.e., different CSI-RS port groups are sent on different frequency domain resources (e.g., component carriers, resource blocks, or different subcarriers). For example, CSI-RS port group #1 is sent based on a first simulated beam, and CSI-RS port group #2 is sent based on a second simulated beam.

[0344] The CSI-RS port group is sent in a frequency division manner, which enables network devices to quickly scan channel information.

[0345] In another possible implementation, different CSI-RS port groups can be sent in a code division manner. For example, CSI-RS port group #1 is sent based on a first analog beam; and CSI-RS port group #2 is sent based on a second analog beam orthogonal to the first analog beam.

[0346] The CSI-RS port group is sent in a code division manner, which can be used for network devices in the HBF architecture to quickly scan channel information.

[0347] It should be noted that the feedback of channel information has a timeliness requirement. When multiple channel information is fed back jointly, the multiple channel information to be compressed must meet the timeliness requirement, thereby ensuring the performance of the joint compression.

[0348] In a possible implementation, the time-frequency ranges of multiple CSI-RS port groups may be limited.

[0349] For example, network devices are configured with CSI-RS port groups #1 to K respectively. Multiple port groups need to be limited to the same frequency domain (but can still be located in different resource units of the same resource block) and occupy the same bandwidth; and multiple port groups are located in the same time slot or adjacent time slots, and the time slot interval of multiple CSI-RS port groups does not exceed t.

[0350] 1130. The terminal device measures L simulated beam channel information.

[0351] The terminal device receives K CSI-RS port groups, reports configuration information based on the CSI-RS and channel information, and obtains the precoding matrix under L simulated beams.

[0352] Specifically, the following uses the precoding matrix as an example of channel information, which can be divided into the following two cases:

[0353] Case 1: L is equal to K. That is, there are K port groups, which are used to determine L=K groups of channel coefficients.

[0354] For example, a network device configures L port groups for a terminal, each corresponding to an analog beam. These K port groups are used to measure the channel coefficients {H1, H2, H3, ...} for L different analog beams (with analog weights {w1, w2, w3, ...}) in the network device (e.g., base station) arrays A1, A2, D1, D2 (number of horizontal and vertical arrays, number of digital ports). The terminal then calculates the precoding matrices {v1, v2, v3, ...} for the L different analog beams using SVD decomposition.

[0355] Case 2: L is greater than K. That is, K port groups are used to determine L groups of channel coefficients.

[0356] For example, K port groups are used to obtain channel coefficients (or channel responses) of K port groups, which are denoted as h1, h2, h3, ..., h k Based on the channel information of K port groups and the channel combination coefficient The channel coefficients under L simulated beams can be obtained The terminal device then calculates the precoding matrices {v1, v2, v3, ...} for L different simulated beams through SVD decomposition.

[0357] That is, when the L channel information are L precoding matrices, the L channel information are obtained by decomposing the channel coefficients of the L analog beams, or the L channel information are obtained by decomposing the channel information of the K port groups and the channel combination coefficients.

[0358] In this application, the terminal device can measure L channel information and compress and feedback M of the channel information.

[0359] It should be noted that, in case 2, for the HBF architecture (or analog beamforming architecture), more channel information can be obtained with fewer reference signals. For example, the base station can use K groups of orthogonal analog weights {W1, W2, W3, ...} and simultaneously send K CSI-RS port groups through different antenna groups, thereby obtaining the channel information corresponding to the K analog ports; and in the terminal device, through the weighting between the analog port channels (i.e., (It can be equivalent to a simulated beam), so that the channel information of L>K simulated beams can be obtained. In this way, the terminal device measures the encrypted beam channel information.

[0360] 1140. The terminal device jointly compresses the channel information of the M analog beams to obtain a precoding matrix of the M analog beams.

[0361] Specifically, the terminal device can determine the channel information of M simulated beams from the measured L simulated beam channel information, that is, select the channel information of M simulated beams to be reported, and calculate the precoding matrix of the M simulated beams through SVD.

[0362] The channel information of the M simulated beams may be determined from the channel information of the L simulated beams in the following exemplary manner.

[0363] In one possible implementation, the reported M simulated beams and their feedback overhead Y may be determined by the network device based on the priorities of the L simulated beams. In this case, the network device may indicate the correspondence between the M channel information and the N channel information to the terminal device.

[0364] In one possible implementation, the terminal device report can determine M channel information and corresponding feedback overhead Y based on the priorities corresponding to L simulated beams configured based on the network device and the measurement results of the L simulated beams.

[0365] In this case, the terminal device may indicate the correspondence between the M channel information and the N channel information to the network device.

[0366] The measurement results of the L channel information include but are not limited to CQI, RSRP measurement, etc.

[0367] The terminal device jointly compresses the M analog beams to obtain channel information of the M analog beams.

[0368] Figure 12 shows a schematic diagram of a compressed feedback process. As shown in Figure 12, at the encoder input, the precoding matrices for M simulated beams need to be filled into the corresponding beam positions of the encoder's input matrix with N beam dimensions. When M is less than N, the remaining unfilled positions can be filled with a fixed amount to obtain an input matrix with N simulated beam dimensions. This input matrix is ​​then fed into the encoder for joint compression, resulting in a full feedback amount with X bits of overhead.

[0369] At the input end of the encoder, the beam positions are filled according to the stacking arrangement of the defined N simulated beam channel information, for example, the N simulated beams are arranged according to the stacking arrangement shown in FIG8 .

[0370] 1150. The terminal device sends a joint feedback amount of M channel information.

[0371] The encoder output end outputs an output matrix under N analog beam dimensions and X feedback quantities. The terminal device intercepts Y joint feedback quantities corresponding to M analog beams from the output matrix under the N analog beam dimensions according to the correspondence between the N analog beams and the stacking positions of the M analog beams at the encoder input end, and feeds back the matrix corresponding to the M analog beams to the decoder, so that partial reporting can be performed.

[0372] It can be understood that, when the M channel information and the feedback overhead Y are determined by the terminal device according to the priority information and the measurement result, the terminal device can also send the correspondence between the M channel information and the N channel information.

[0373] 1160. The network device jointly reconstructs the channel information of the M simulated beams to obtain M channel information.

[0374] As shown in Figure 12, the network reports the partial beam joint feedback amount received under Y bits of overhead, and based on the corresponding relationship between the stacking positions of N analog beams and M analog beams, fills the corresponding beam positions of the non-feedback portion with a fixed amount to obtain the full feedback amount under X bits of overhead. This is input to the decoder for joint reconstruction to obtain the output matrix in the N-beam dimension.

[0375] Furthermore, the network device selects information of corresponding beam positions from the output matrix according to the correspondence between the stacking positions of the N simulated beams and the M simulated beams to obtain the precoding matrix of the reported M simulated beams.

[0376] It should be noted that, in step S1150, the terminal device may also determine the feedback accuracy corresponding to the M simulated beams. The feedback overhead corresponding to the M simulated beams is expressed as Z = {Z1, Z2, ..., Z m}, where Z includes m feedback overheads corresponding to M simulated beams, and the m feedback overheads correspond to m feedback accuracies.

[0377] It can be understood that the feedback accuracy is configured by the network device to the terminal device based on the priority information of the L simulated beams, or it can be determined by the terminal device based on the measurement results and priority information.

[0378] After the terminal device obtains the X-bit joint feedback amount, based on the feedback overhead Z={Z1,Z2,…,Z m} and the correspondence between different precision characteristics and feedback amount positions, a Z-bit partial feedback amount is determined from the X-bit joint feedback amount, where the Z-bit partial feedback amount may include m low-precision feedback amounts, or may include m high-precision feedback amounts, or may be a part of low-precision feedback and the other part of high-precision feedback amounts, which is not limited in this application.

[0379] That is, the terminal device can provide feedback of different accuracies for different simulated beams, thereby achieving flexible feedback.

[0380] Assume that the Z-bit partial feedback includes m low-precision feedback quantities, and send the Z-bit feedback quantity to the decoder. At the input end of the decoder, according to the feedback overhead Z = {Z1, Z2, ..., Z m} and the correspondence between different precision features and feedback quantity positions, the low-precision joint feedback quantity is padded with a fixed amount at the corresponding supplementary component position to obtain X full feedback quantities and input them into the decoder for joint reconstruction to obtain the low-precision precoding matrix of M analog beams.

[0381] In this technical solution, the channel information of the partial beam is input on the encoder side according to the correspondence between the beam positions of the partial feedback amount and the full feedback amount, thereby realizing compression and feedback of the partial channel information, avoiding the storage / switching overhead of multiple AI models, and reducing the complexity of implementation; in addition, the priority of partial feedback is defined to ensure the efficiency and reliability of multi-beam reporting; further, for the M channel information of compressed feedback, the accuracy of the corresponding channel information can be fed back through the characteristic principal component and characteristic supplementary component corresponding to each channel information, so that different simulated beams can be reported with different accuracies, increasing the flexibility of the feedback mechanism.

[0382] It should be understood that other possible implementations of the embodiments of the present application are similar to the above-mentioned method 700 or method 1100. Please refer to the description in method 700 or method 1100 and will not be repeated here.

[0383] It should be understood that the size of the serial numbers of the above processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0384] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the interaction between various network elements. It can be understood that each network element, such as a transmitting end device or a receiving end device, includes a hardware structure and / or software module corresponding to the execution of each function in order to implement the above functions. Those skilled in the art should be aware that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0385] In the embodiment of the present application, the functional modules of the transmitting device or the receiving device can be divided according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation. The following is an example of dividing each functional module according to each function.

[0386] The method provided in the embodiment of the present application is described in detail above in conjunction with Figures 7 to 12. Below, the apparatus provided in the embodiment of the present application is described in detail in conjunction with Figures 13 and 14. It should be understood that the description of the apparatus embodiment corresponds to the description of the method embodiment. Therefore, for matters not described in detail, please refer to the method embodiment above. For the sake of brevity, it will not be repeated here.

[0387] FIG13 is a schematic structural diagram of a communication device provided in an embodiment of the present application.

[0388] The device 1300 includes a transceiver unit 1310 and a processing unit 1320 , wherein the transceiver unit 1310 can be used to implement corresponding communication functions, and the processing unit 1320 can be used to perform data processing.

[0389] Optionally, the transceiver unit 1310 may also be referred to as a communication interface or communication unit, and may include a transmitting unit and / or a receiving unit. The transceiver unit 1310 may be a transceiver (including a transmitter and / or a receiver), an input / output interface (including an input and / or output interface), a pin, or a circuit. The transceiver unit 1310 may be configured to perform the transmitting and / or receiving steps in the above-described method embodiments.

[0390] Optionally, the processing unit 1320 may be a processor (may include one or more), a processing circuit with processor functions, etc., and may be used to execute other steps except sending and receiving in the above method embodiment.

[0391] Optionally, the apparatus 1300 further includes a storage unit, which may be a memory, an internal storage unit (e.g., a register, a cache, etc.), an external storage unit (e.g., a read-only memory, a random access memory, etc.), etc. The storage unit is used to store instructions, and the processing unit 1320 executes the instructions stored in the storage unit to cause the communication apparatus to perform the above method.

[0392] In one design, the apparatus 1300 may be configured to perform the actions performed by the encoder in the above method embodiments. For example, the apparatus 1300 may be configured to perform the actions performed by the encoder in the above method 700 or method 1100. In this case, the apparatus 1300 may be a component of the encoder, the transceiver unit 1310 may be configured to perform the transceiver-related operations of the encoder in the above method embodiments, and the processing unit 1320 may be configured to perform the processing-related operations of the encoder in the above method embodiments.

[0393] For example, the processing unit 1320 is configured to compress M channel information to obtain a first sequence, where the M channel information is part or all of L measured channel information, where L is less than or equal to N, N is the number of channel information determined based on reference signals on K antenna port groups, and N is a positive integer. The first sequence is X joint feedback amounts corresponding to the N channel information. The processing unit 1320 is further configured to determine a second sequence based on the first sequence, where the second sequence is Y joint feedback amounts corresponding to the M channel information. The transceiver unit 1310 is configured to send the second sequence to the decoder.

[0394] Optionally, the processing unit 1320 is further used to determine the correspondence between the stacking positions of the N channel information at the encoder input end and the stacking positions of the M channel information at the encoder input end; the processing unit is also used to determine Y joint feedback amounts corresponding to the M channel information based on the correspondence.

[0395] Optionally, the processing unit 1320 is further configured to determine accuracy levels corresponding to the M pieces of channel information.

[0396] It should be understood that the transceiver unit 1310 and the processing unit 1320 may also perform other operations performed by the encoder in any of the above methods 700 or 1100, which are not described in detail here.

[0397] In one design, the apparatus 1300 may be configured to perform the actions performed by the decoder in the above method embodiments. For example, the apparatus 1300 may be configured to perform the actions performed by the decoder in the above method 700 or method 1100. In this case, the apparatus 1300 may be a component of a decoder, the transceiver unit 1310 may be configured to perform the transceiver-related operations on the decoder side in the above method embodiments, and the processing unit 1320 may be configured to perform the processing-related operations on the decoder side in the above method embodiments.

[0398] For example, the transceiver unit 1310 is configured to receive a second sequence, where the second sequence is Y joint feedback amounts corresponding to M channel information, where the M channel information is part or all of L measured channel information, where L is less than or equal to N, and N is the number of channel information determined based on reference signals on K antenna port groups, where N is a positive integer. The processing unit 1320 is configured to determine a first sequence based on the second sequence, where the first sequence is X joint feedback amounts corresponding to the N channel information. The processing unit 1320 is further configured to reconstruct the first sequence to obtain the M channel information.

[0399] Optionally, the processing unit 1320 is further used to determine the correspondence between the stacking positions of the M channel information at the decoder input end and the stacking positions of the N channel information at the decoder input end; the processing unit is further used to determine X joint feedback amounts corresponding to the N channel information based on the correspondence.

[0400] Optionally, the processing unit 1320 is further configured to determine accuracy levels corresponding to the M pieces of channel information.

[0401] Optionally, the processing unit 1320 is further used to determine the correspondence between the stacking position of the M channel information at the decoder input end and the stacking position of the N channel information at the decoder input end; the processing unit 1320 is further used to determine the M channel information based on the correspondence.

[0402] It should be understood that the transceiver unit 1310 and the processing unit 1320 can also perform other operations performed by the terminal device in any of the above methods 700 or 1100, which are not described in detail here.

[0403] It should also be understood that the device 1300 here is embodied in the form of a functional unit. The term "unit" here can refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit and / or other suitable components that support the described functions. In an optional example, those skilled in the art will understand that the device 1300 can be specifically a network device in the above-mentioned embodiment, and can be used to execute the various processes and / or steps corresponding to the network device in the above-mentioned method embodiments. To avoid repetition, they will not be described here.

[0404] The apparatus 1300 of each of the above-mentioned solutions has the function of implementing the corresponding steps performed by the device in the above-mentioned method, or the apparatus 1300 of each of the above-mentioned solutions has the function of implementing the corresponding steps performed by the network device in the above-mentioned method. The function can be implemented by hardware, or the corresponding software can be implemented by hardware. The hardware or software includes one or more modules corresponding to the above-mentioned functions; for example, the transceiver unit can be replaced by a transceiver (for example, the sending unit in the transceiver unit can be replaced by a transmitter, and the receiving unit in the transceiver unit can be replaced by a receiver), and other units, such as the processing unit, can be replaced by a processor to respectively perform the sending and receiving operations and related processing operations in each method embodiment.

[0405] In addition, the transceiver unit 1310 may also be a transceiver circuit (for example, may include a receiving circuit and a transmitting circuit), and the processing unit may be a processing circuit.

[0406] It should be noted that the apparatus in FIG13 may be a network element or device in the aforementioned embodiment, or may be a chip or chip system, such as a system on chip (SoC). The transceiver unit may be an input / output circuit or a communication interface; the processing unit may be a processor, microprocessor, or integrated circuit integrated on the chip. This is not limited here.

[0407] Figure 14 is a schematic diagram of a communication architecture provided in an embodiment of the present application. The communication device 1400 shown in Figure 14 includes a processor 1410, a memory 1420, and a transceiver 1430. The processor 1410 is coupled to the memory 1420 and is configured to execute instructions stored in the memory 1420 to control the transceiver 1430 to transmit and / or receive signals.

[0408] It should be understood that the processor 1410 and memory 1420 described above can be combined into a single processing device, with the processor 1410 configured to execute program code stored in the memory 1420 to implement the aforementioned functions. In a specific implementation, the memory 1420 can also be integrated into the processor 1410 or independent of the processor 1410. It should be understood that the processor 1410 can also correspond to the various processing units in the aforementioned communication device, and the transceiver 1430 can correspond to the various receiving units and transmitting units in the aforementioned communication device.

[0409] It should also be understood that the transceiver 1430 may include a receiver (or receiver) and a transmitter (or transmitter). The transceiver may further include an antenna, and the number of antennas may be one or more. The transceiver may also be a communication interface or interface circuit.

[0410] Specifically, the communication device 1400 may correspond to the terminal device in the method 700 or the method 1100 according to the embodiment of the present application. The communication device 1400 may include the units of the method performed by the encoder in the method 700 or the method 1100. It should be understood that the specific process of each unit performing the above-mentioned corresponding steps has been described in detail in the above-mentioned method embodiments and will not be repeated here for the sake of brevity.

[0411] When the communication device 1400 is a chip, the chip includes an interface unit and a processing unit, wherein the interface unit may be an input / output circuit or a communication interface; and the processing unit may be a processor, microprocessor, or integrated circuit integrated on the chip.

[0412] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0413] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by hardware integrated logic circuits in the processor or software instructions. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0414] The present application also provides a computer-readable medium having a computer program stored thereon, which implements the functions of any of the above method embodiments when executed by a computer.

[0415] The present application also provides a computer program product, which implements the functions of any of the above method embodiments when executed by a computer.

[0416] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0417] In the embodiments of this application, words such as "exemplary" and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete way.

[0418] It should be understood that references to "embodiments" throughout this specification mean that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Therefore, various embodiments throughout this specification do not necessarily refer to the same embodiment. Furthermore, these particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0419] It should be understood that in the various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. The names of all nodes and messages in this application are merely names set by this application for the convenience of description. The names in the actual network may be different. It should not be understood that this application limits the names of various nodes and messages. On the contrary, any name with the same or similar function as the node or message used in this application is regarded as the method or equivalent replacement of this application, and is within the scope of protection of this application.

[0420] It should also be understood that in this application, "when", "if" and "if" all mean that the UE or base station will take corresponding measures under certain objective circumstances. It does not limit the time, and does not require the UE or base station to take judgment actions when implementing it, nor does it mean that there are other limitations.

[0421] Additionally, the terms "system" and "network" are often used interchangeably. The term "and / or" is simply used to describe an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0422] As used herein, the term "at least one of" or "at least one of" refers to all or any combination of the listed items. For example, "at least one of A, B, and C" can mean: A alone, B alone, C alone, A and B together, B and C together, and A, B, and C together. As used herein, "at least one" means one or more. "A plurality" means two or more.

[0423] It should be understood that in each embodiment of the present application, the terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0424] It should be understood that in various embodiments of the present application, the first, second, and various numerical numbers are merely distinctions for ease of description and are not intended to limit the scope of the embodiments of the present application.

[0425] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0426] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0427] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0428] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0429] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0430] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0431] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for feeding back channel status, characterized in that: Applications for encoders include: Compressing M channel information to obtain a first sequence, where the M channel information is part or all of L measured channel information, L is less than or equal to N, N is the number of channel information determined based on reference signals on K antenna port groups, the first sequence is X joint feedback amounts corresponding to the N channel information, and M, L, and N are positive integers; Determine a second sequence according to the first sequence, where the second sequence is Y joint feedback amounts corresponding to the M channel information; The second sequence is sent to a decoder.

2. The method according to claim 1, characterized in that When the first sequence or the second sequence is a sequence before quantization, the joint feedback amount is the length of the first sequence or the second sequence, or when the first sequence or the second sequence is a sequence after quantization, the joint feedback amount is the number of bits of the first sequence or the second sequence.

3. The method according to claim 1 or 2, characterized in that: The determining the second sequence according to the first sequence comprises: Determine a corresponding relationship between a stacking position of the N channel information at the encoder input end and a stacking position of the M channel information at the encoder input end; Determine Y joint feedback amounts corresponding to the M channel information according to the corresponding relationship.

4. The method according to claim 3, characterized in that The first sequence includes N first features and O second features, the N first features correspond to the N channel information, the first feature indicates a specific feature of each channel information, the O second features indicate a common feature of the N channel information, and determining Y joint feedback amounts corresponding to the M channel information according to the corresponding relationship includes: Determine, according to the corresponding relationship and the N first features, M first features corresponding to the M channel information; Determine P second features corresponding to the M channel information according to the corresponding relationship and the O second features, where P is less than or equal to O, and O and P are positive integers; Determine Y joint feedback amounts corresponding to the M channel information according to the M first features and the P second features.

5. The method according to claim 4, characterized in that The positions of the N first features in the first sequence correspond to the positions of the M first features in the second sequence, and the positions of the O second features in the first sequence correspond to the positions of the P second features in the second sequence.

6. The method according to claim 5, characterized in that The N first features and the O second features include a feature principal component and a feature supplement component, the feature principal component is used to complete the reconstruction of the channel information, and the feature supplement component is used to supplement the accuracy of the channel information.

7. The method according to claim 6, characterized in that Each of the M first features includes the feature principal component, some or all of the M first features include the feature supplementary component, each of the P second features includes the feature principal component, and some or all of the P second features include the feature supplementary component.

8. The method according to claim 7, characterized in that The characteristic principal component of each of the N first features has a corresponding relationship with the characteristic principal components of some or all of the M first features; the supplementary component of each of the N first features has a corresponding relationship with the supplementary components of some or all of the M first features; the characteristic principal component of each of the O second features has a corresponding relationship with the characteristic principal component of each of the P second features, and the characteristic supplementary component of each of the O second features has a corresponding relationship with the characteristic supplementary component of each of the P second features.

9. The method according to any one of claims 1 to 8, characterized in that The method further comprises: Determine the precision levels corresponding to the M channel information.

10. The method according to any one of claims 1 to 9, characterized in that The M channel information or the accuracy level corresponding to the M channel information is determined by a network device, or the M channel information or the accuracy level corresponding to the M channel information is determined by a terminal device.

11. The method according to claim 10, characterized in that The M channel information or the accuracy level corresponding to the M channel information is determined by the network device based on the priority information of the L channel information; or, the M channel information or the accuracy level corresponding to the M channel information is determined by the terminal device based on the measurement results of the L channel information and / or the priority information of the L channel information.

12. The method according to claim 11, characterized in that The method further comprises: When the M channel information is determined by the network device, sending a correspondence between the M channel information and the L channel information; When the precision categories corresponding to the M channel information are determined by the network device, the precision categories respectively corresponding to the M channel information are sent.

13. The method according to any one of claims 1 to 12, characterized in that The encoder is used in terminal equipment.

14. A method for feeding back channel status, characterized in that: Applicable to decoders, including: receiving a second sequence, where the second sequence is Y joint feedback amounts corresponding to M channel information, where the M channel information is part or all of L measured channel information, where L is less than or equal to N, where N is the number of channel information determined based on reference signals on K antenna port groups, and M, L, and N are positive integers; Determine a first sequence according to the second sequence, where the first sequence is X joint feedback amounts corresponding to the N channel information; The first sequence is reconstructed to obtain the M channel information.

15. The method according to claim 14, characterized in that When the first sequence or the second sequence is a sequence before quantization, the joint feedback amount is the length of the first sequence or the second sequence, or when the first sequence or the second sequence is a sequence after quantization, the joint feedback amount is the number of bits of the first sequence or the second sequence.

16. The method according to claim 14 or 15, characterized in that The determining the first sequence according to the second sequence comprises: Determine a corresponding relationship between a stacking position of the M channel information at the decoder input end and a stacking position of the N channel information at the decoder input end; X joint feedback amounts corresponding to the N channel information are determined according to the corresponding relationship.

17. The method according to claim 16, characterized in that The second sequence includes M first features and P second features, the M first features correspond to the M channel information, the first feature indicates a specific feature of each channel information, and the second feature indicates a common feature of the M channel information, and determining X joint feedback amounts corresponding to the N channel information according to the corresponding relationship includes: Determine N first features corresponding to N channel information according to the corresponding relationship and the M first features; Determine O second features corresponding to the N channel information according to the corresponding relationship and the P second features, where P is less than or equal to O, and O and P are positive integers; Determine X joint feedback amounts corresponding to the N channel information according to the N first features and the O second features.

18. The method according to claim 17, characterized in that The positions of the M first features in the second sequence correspond to the positions of the N first features in the first sequence, and the positions of the P second features in the second sequence correspond to the positions of the O second features in the first sequence.

19. The method according to claim 18, characterized in that The M first features and the P second features include feature principal components and feature supplementary components, the feature principal components are used to complete the reconstruction of the channel information, and the feature supplementary components are used to supplement the accuracy of the channel information.

20. The method according to claim 19, characterized in that Each of the N first features includes the feature principal component, some or all of the N first features include the feature supplementary component, each of the O second features includes the feature principal component, and some or all of the O second features include the feature supplementary component.

21. The method according to claim 20, characterized in that The characteristic principal component of each of the N first features has a corresponding relationship with the characteristic principal components of some or all of the M first features; the supplementary component of each of the N first features has a corresponding relationship with the supplementary components of some or all of the M first features; the characteristic principal component of each of the O second features has a corresponding relationship with the characteristic principal component of each of the P second features, and the characteristic supplementary component of each of the O second features has a corresponding relationship with the characteristic supplementary component of each of the P second features.

22. The method according to any one of claims 14 to 21, characterized in that The method further comprises: Determine the precision levels corresponding to the M channel information.

23. The method according to any one of claims 14 to 22, characterized in that The M channel information or the accuracy level corresponding to the M channel information is determined by a network device, or the M channel information or the accuracy level corresponding to the M channel information is determined by a terminal device.

24. The method according to claim 23, characterized in that The M channel information or the accuracy level corresponding to the M channel information is determined by the network device based on the priority information of the L channel information; or, the M simulation beams or the accuracy level corresponding to the M simulation beams is determined by the terminal device based on the measurement results of the L channel information and / or the priority information of the L channel information.

25. The method according to claim 24, characterized in that The method further comprises: When the M channel information is determined by the terminal device, sending a correspondence between the M channel information and the L channel information; When the precision categories corresponding to the M channel information are determined by the terminal device, the precision categories respectively corresponding to the M channel information are sent.

26. The method according to any one of claims 14 to 25, characterized in that The reconstructing the first sequence to obtain the M channel information includes: Determine a corresponding relationship between a stacking position of the M channel information at the decoder input end and a stacking position of the N channel information at the decoder input end; The M channel information is determined according to the corresponding relationship.

27. The method according to any one of claims 14 to 26, characterized in that The decoder is used in a network device.

28. A communication device, characterized in that: Comprising means for performing the method of any one of claims 1 to 13 or 14 to 27.

29. A communication device, characterized in that: The device comprises a processor coupled to a memory, the memory being used to store a computer program or instructions, and the processor being used to execute the computer program or instructions in the memory, so that the device performs the method as claimed in any one of claims 1 to 13, or performs the method as claimed in any one of claims 14 to 27.

30. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction, and when the computer program or instruction is executed on a computer, the computer executes the method as claimed in any one of claims 1 to 13; or, the method as claimed in any one of claims 14 to 27.

31. A chip system, characterized in that: include: A processor, configured to call and run a computer program from a memory, so that a communication device equipped with the chip system executes the method described in any one of claims 1 to 13, or executes the method described in any one of claims 14 to 27.

32. A computer program product, characterized in that When the computer program product is run on a computer, the computer is enabled to execute the steps of the method according to any one of claims 1 to 13, or to execute the steps of the method according to any one of claims 14 to 27.

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