Channel state information reporting method and device, electronic equipment, chip and medium

Abstract

The invention relates to a channel state information reporting method and device, electronic equipment, a chip and a medium, and the method comprises the steps: obtaining a plurality of historical channel coefficient matrixes of a target channel, and a preprocessing matrix corresponding to a current Doppler gear of the target channel; performing filtering processing on the plurality of historical channel coefficient matrixes according to the preprocessing matrix to obtain preprocessing data at the current time point; determining a predicted channel coefficient matrix at the current time point according to the preprocessed data and a channel coefficient matrix prediction model; and determining and reporting channel state information (CSI) in combination with the predicted channel coefficient matrix. Wherein prediction processing is performed on the channel coefficient matrix at the current time point in combination with the plurality of historical channel coefficient matrixes of the target channel, so that the reporting period of the CSI information can be shortened, the reporting frequency of the CSI information can be increased, the outdated condition of the CSI information can be avoided, and the channel throughput can be ensured.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a method, device, electronic device, chip and medium for reporting channel state information. Background Art

[0002] At present, the reporting process of channel state information (CSI) mainly includes: the terminal device receives the channel state information reference signal (CSI-RS) configured by the network device; measures and processes the CSI-RS signal to obtain the channel coefficient matrix, and then determines the CSI information and reports it.

[0003] In the above solution, the reporting period of CSI information is long, and it is easy for CSI information to be outdated, making it difficult to ensure channel throughput. Summary of the invention

[0004] The present disclosure provides a method, device, electronic device, chip and medium for reporting channel state information.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for reporting channel state information is provided, the method comprising: acquiring multiple historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler position of the target channel; filtering the multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at a current time point; determining a predicted channel coefficient matrix at the current time point according to the preprocessing data and a channel coefficient matrix prediction model; and determining and reporting channel state information CSI in combination with the predicted channel coefficient matrix.

[0006] In one embodiment of the present disclosure, the acquiring of multiple historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler position of the target channel includes: acquiring multiple historical channel coefficient matrices of the target channel; determining a current Doppler position of the target channel; and querying a matrix database according to the current Doppler position to acquire a preprocessing matrix corresponding to the current Doppler position in the matrix database.

[0007] In one embodiment of the present disclosure, the matrix database is determined in the following manner: determining the values ​​of Doppler-related parameters adapted to each Doppler gear; for each Doppler gear, determining the distribution function of the channel coefficient matrix correlation value in combination with the values ​​of the Doppler-related parameters adapted to the Doppler gear; generating a plurality of reference channel coefficient matrices according to the distribution function; determining a preprocessing matrix corresponding to the Doppler gear according to the plurality of reference channel coefficient matrices; and determining the matrix database according to the preprocessing matrices corresponding to each of the Doppler gears.

[0008] In one embodiment of the present disclosure, the Doppler-related parameter includes at least one of the following: Doppler frequency, and symbol time difference between channel coefficient matrices.

[0009] In one embodiment of the present disclosure, the distribution function includes at least one of the following: a Bessel function and an approximate sampling function.

[0010] In one embodiment of the present disclosure, after filtering the multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at the current time point, the method also includes: performing inverse fast Fourier transform processing on the preprocessing data to obtain first transformed data; setting a first value in the first transformed data that is less than or equal to a threshold value to zero to obtain processed data; performing fast Fourier transform processing on the processed data to obtain second transformed data; and updating the preprocessing data according to the second transformed data.

[0011] In one embodiment of the present disclosure, determining the predicted channel coefficient matrix at the current time point based on the preprocessed data and the channel coefficient matrix prediction model includes: obtaining channel characteristic parameters of the target channel; inputting the preprocessed data and the channel characteristic parameters into the channel coefficient matrix prediction model, and obtaining the predicted channel coefficient matrix output by the channel coefficient matrix prediction model.

[0012] In one embodiment of the present disclosure, the channel characteristic parameter includes at least one of the following: the current Doppler frequency offset, channel delay spread, and channel spatial correlation.

[0013] In one embodiment of the present disclosure, the channel coefficient matrix prediction model is obtained by training in combination with multiple channel coefficient matrix sequences; the number of sample channel coefficient matrices in the channel coefficient matrix sequence is greater than the number of historical channel coefficient matrices used in channel coefficient matrix prediction.

[0014] According to the second aspect of the embodiment of the present disclosure, a channel state information reporting device is also provided, and the device includes: an acquisition module, used to acquire multiple historical channel coefficient matrices of a target channel, and a preprocessing matrix corresponding to the current Doppler gear of the target channel; a filtering processing module, used to filter the multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at a current time point; a determination module, used to determine the predicted channel coefficient matrix at the current time point according to the preprocessing data and a channel coefficient matrix prediction model; a reporting processing module, used to determine and report channel state information CSI in combination with the predicted channel coefficient matrix.

[0015] In one embodiment of the present disclosure, the acquisition module is specifically used to acquire multiple historical channel coefficient matrices of the target channel; determine the current Doppler gear of the target channel; query the matrix database according to the current Doppler gear, and acquire the preprocessing matrix corresponding to the current Doppler gear in the matrix database.

[0016] In one embodiment of the present disclosure, the matrix database is determined in the following manner: determining the values ​​of Doppler-related parameters adapted to each Doppler gear; for each Doppler gear, determining the distribution function of the channel coefficient matrix correlation value in combination with the values ​​of the Doppler-related parameters adapted to the Doppler gear; generating a plurality of reference channel coefficient matrices according to the distribution function; determining a preprocessing matrix corresponding to the Doppler gear according to the plurality of reference channel coefficient matrices; and determining the matrix database according to the preprocessing matrices corresponding to each of the Doppler gears.

[0017] In one embodiment of the present disclosure, the Doppler-related parameter includes at least one of the following: Doppler frequency, and symbol time difference between channel coefficient matrices.

[0018] In one embodiment of the present disclosure, the distribution function includes at least one of the following: a Bessel function and an approximate sampling function.

[0019] In one embodiment of the present disclosure, the device also includes: a first transformation module, a zeroing processing module, a second transformation module and an update processing module; the first transformation module is used to perform inverse fast Fourier transform processing on the pre-processed data to obtain first transformed data; the zeroing processing module is used to perform zero processing on a first value less than or equal to a threshold value in the first transformed data to obtain processed data; the second transformation module is used to perform fast Fourier transform processing on the processed data to obtain second transformed data; the update processing module is used to update the pre-processed data according to the second transformed data.

[0020] In one embodiment of the present disclosure, the determination module is specifically used to obtain channel characteristic parameters of the target channel; input the preprocessed data and the channel characteristic parameters into the channel coefficient matrix prediction model, and obtain the predicted channel coefficient matrix output by the channel coefficient matrix prediction model.

[0021] In one embodiment of the present disclosure, the channel characteristic parameter includes at least one of the following: the current Doppler frequency offset, channel delay spread, and channel spatial correlation.

[0022] In one embodiment of the present disclosure, the channel coefficient matrix prediction model is obtained by training in combination with multiple channel coefficient matrix sequences; the number of sample channel coefficient matrices in the channel coefficient matrix sequence is greater than the number of historical channel coefficient matrices used in channel coefficient matrix prediction.

[0023] According to a third aspect of an embodiment of the present disclosure, an electronic device is also provided, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to: implement the steps of the channel state information reporting method as described above.

[0024] According to a fourth aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is also provided. When instructions in the storage medium are executed by a processor, the processor is enabled to execute the channel state information reporting method as described above.

[0025] According to the fifth aspect of an embodiment of the present disclosure, a chip is also provided, comprising one or more interface circuits and one or more processors; the interface circuit is used to receive a signal, the signal comprising a computer instruction, and when the processor executes the computer instruction, the chip executes the channel state information reporting method as described above.

[0026] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0027] The method comprises the following steps: obtaining a plurality of historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler position of the target channel; filtering the plurality of historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at a current time point; determining a predicted channel coefficient matrix at a current time point according to the preprocessing data and a channel coefficient matrix prediction model; determining and reporting channel state information CSI in combination with the predicted channel coefficient matrix; wherein, predicting the channel coefficient matrix at the current time point in combination with the plurality of historical channel coefficient matrices of the target channel can shorten the reporting period of CSI information, increase the reporting frequency of CSI information, avoid the situation where CSI information is out of date, and thus ensure the channel throughput.

[0028] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0030] Figure 1 This is a flow chart of a method for reporting channel state information according to an embodiment of the present disclosure;

[0031] Figure 2 This is a flow chart of a method for reporting channel state information according to another embodiment of the present disclosure;

[0032] Figure 3 This is a flow chart of a method for reporting channel state information according to another embodiment of the present disclosure;

[0033] Figure 4 A schematic diagram of the structure of a device for reporting channel state information according to an embodiment of the present disclosure;

[0034] Figure 5 is a structural block diagram of an electronic device according to an exemplary embodiment of the present disclosure;

[0035] Figure 6 The figure is a schematic diagram of the structure of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0038] At present, the reporting process of channel state information (CSI) mainly includes: the terminal device receives the channel state information reference signal (CSI-RS) configured by the network device; measures and processes the CSI-RS signal to obtain the channel coefficient matrix, and then determines the CSI information and reports it.

[0039] In the above solution, the reporting period of CSI information is long, and it is easy for CSI information to be outdated, making it difficult to ensure channel throughput.

[0040] Figure 1 The present invention is a flowchart of a method for reporting channel state information according to an embodiment of the present invention. It should be noted that the method for reporting channel state information according to the present embodiment can be applied to a channel state information reporting device, which can be configured in an electronic device or chip so that the electronic device or chip can perform the channel state information reporting function.

[0041] Among them, the electronic device can be any device with computing capabilities, such as a personal computer (PC), a mobile terminal, a terminal device, a server, etc. The mobile terminal can be, for example, a vehicle-mounted device, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and other hardware devices with various operating systems, touch screens and / or display screens.

[0042] In addition, the reporting device of the channel state information may also be software in the electronic device, etc. The software may be, for example, software for reporting the channel state information, etc. In the following embodiments, the execution subject is taken as an example of a terminal device.

[0043] like Figure 1 As shown, the method comprises the following steps:

[0044] Step 101: Acquire multiple historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler level of the target channel.

[0045] In the embodiment of the present disclosure, the process of the terminal device executing step 101 may, for example, be to obtain multiple historical channel coefficient matrices of the target channel; determine the current Doppler position of the target channel; query the matrix database according to the current Doppler position, and obtain the preprocessing matrix corresponding to the current Doppler position in the matrix database.

[0046] The target channel may be a channel that needs to perform CSI reporting at other time points in addition to performing CSI reporting in combination with a CSI-RS channel.

[0047] In the embodiment of the present disclosure, the interval symbol times between the historical symbol times of multiple historical channel coefficient matrices may be the same or different.

[0048] In one example, when the interval symbol times between the historical symbol times of multiple historical channel coefficient matrices are different, the terminal device can determine the current Doppler gear in combination with the current Doppler frequency. Different Doppler gears can correspond to different Doppler frequencies.

[0049] In another example, when the interval symbol time between the historical symbol times of multiple historical channel coefficient matrices is the same, the terminal device can determine the current Doppler gear by combining the interval symbol time between the historical symbol times of the multiple historical channel coefficient matrices and the current Doppler frequency. Different Doppler gears can correspond to different Doppler frequencies and / or different interval symbol times.

[0050] In the embodiment of the present disclosure, the matrix database may include multiple pre-processing matrices, wherein different pre-processing matrices may correspond to different Doppler gears.

[0051] Among them, the matrix database is queried in combination with the current Doppler position of the target channel to obtain the preprocessing matrix; and multiple historical channel coefficient matrices are filtered based on the preprocessing matrix, which can reduce the processing amount when obtaining the preprocessing matrix, thereby further improving the efficiency of determining the CSI information.

[0052] Step 102: Filter multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at the current time point.

[0053] In the disclosed embodiment, it is assumed that the multiple historical channel coefficient matrices are historical channel coefficient matrices at symbol times in a plurality of consecutive time slots. That is, in a plurality of consecutive time slots, a historical channel coefficient matrix is ​​determined at a symbol time in each time slot. The current time point may be a symbol time in a time slot after a plurality of consecutive time slots. Based on the above assumption, a formula for determining the preprocessed data at the current time point may be as shown in the following formula (1).

[0054]

[0055] Among them, x 0 (t),x 1 (t),...,x N-1 (t) represents the historical channel coefficient matrix at symbol time in N consecutive time slots. bessel represents the preprocessing matrix. Represents preprocessed data.

[0056] Step 103: Determine the predicted channel coefficient matrix at the current time point according to the preprocessed data and the channel coefficient matrix prediction model.

[0057] In the embodiment of the present disclosure, the process of the terminal device executing step 103 may, for example, be to obtain channel characteristic parameters of the target channel; input the preprocessed data and the channel characteristic parameters into the channel coefficient matrix prediction model, and obtain the predicted channel coefficient matrix output by the channel coefficient matrix prediction model.

[0058] In the embodiment of the present disclosure, the channel characteristic parameters may include at least one of the following: current Doppler frequency deviation, channel delay spread, and channel spatial correlation. The current Doppler frequency deviation may refer to the Doppler frequency deviation at the current time point. Doppler frequency deviation refers to the change in phase and frequency caused by the propagation path difference when the mobile station moves in a certain direction at a constant speed.

[0059] The channel delay spread refers to the difference in signal arrival time due to multipath propagation, and the channel spatial correlation refers to the correlation between channel values ​​corresponding to different transmitting antennas.

[0060] Among them, the channel coefficient matrix prediction model has a large number of parameters and a high accuracy. Inputting the preprocessed data and channel characteristic parameters into the channel coefficient matrix prediction model and obtaining the predicted channel coefficient matrix output by the channel coefficient matrix prediction model can improve the accuracy of the predicted channel coefficient matrix determined, thereby further improving the accuracy of the reported CSI information and further improving the channel throughput.

[0061] In the disclosed embodiment, the channel coefficient matrix prediction model is obtained by training a plurality of channel coefficient matrix sequences; the number of sample channel coefficient matrices in the channel coefficient matrix sequence is greater than the number of historical channel coefficient matrices used in the channel coefficient matrix prediction.

[0062] Specifically, the channel coefficient matrix prediction model can be obtained by combining multiple channel coefficient matrix sequences and related channel characteristic parameter training. N+1 channel coefficient matrices in the channel coefficient matrix sequence can be obtained; the first N channel coefficient matrices and related channel characteristic parameters are input into the initial channel coefficient matrix prediction model to obtain the N+1th predicted channel coefficient matrix output by the channel coefficient matrix prediction model; the loss function value is determined based on the N+1th predicted channel coefficient matrix, the N+1th channel coefficient matrix and the loss function of the channel coefficient matrix prediction model; the channel coefficient matrix prediction model is parameter adjusted based on the loss function value to achieve training.

[0063] Among them, the channel coefficient matrix prediction model can be obtained by combining multiple channel coefficient matrix sequences and related channel characteristic parameter training. The multiple channel coefficient matrices in the channel coefficient matrix sequence can be multiple continuous channel coefficient matrices of the target channel or other channels, which can reflect the change trend of the channel coefficient matrix, so that the channel coefficient matrix prediction model can learn this change trend, thereby further improving the prediction accuracy of the channel coefficient matrix prediction model.

[0064] Step 104: determine and report channel state information CSI in combination with the predicted channel coefficient matrix.

[0065] In the disclosed embodiment, the terminal device may determine CSI information in combination with the predicted channel coefficient matrix, and report the determined CSI information.

[0066] The CSI information may include at least one of the following parameters: a predicted channel coefficient matrix, a channel quality indicator (Channel Quality Indicator, CQI), a rank indicator (Rank Indicator, RI), and a precoding matrix indicator (Precoding Matrix Indicator, PMI).

[0067] In the method for reporting channel state information of the embodiment of the present disclosure, multiple historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler position of the target channel are obtained; the multiple historical channel coefficient matrices are filtered according to the preprocessing matrix to obtain preprocessing data at a current time point; the predicted channel coefficient matrix at the current time point is determined according to the preprocessing data and a channel coefficient matrix prediction model; and channel state information CSI is determined and reported in combination with the predicted channel coefficient matrix; wherein, predicting the channel coefficient matrix at the current time point in combination with the multiple historical channel coefficient matrices of the target channel can shorten the reporting period of CSI information, increase the reporting frequency of CSI information, avoid outdated CSI information, and thus ensure channel throughput.

[0068] Figure 2 This is a flow chart of a method for reporting channel state information of another embodiment of the present disclosure. It should be noted that the method for reporting channel state information of this embodiment can be applied to a channel state information reporting device, which can be configured in an electronic device or chip so that the electronic device or chip can perform the channel state information reporting function.

[0069] Among them, the electronic device can be any device with computing capabilities, such as a personal computer (PC), a mobile terminal, a terminal device, a server, etc. The mobile terminal can be, for example, a vehicle-mounted device, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and other hardware devices with various operating systems, touch screens and / or display screens.

[0070] In addition, the reporting device of the channel state information may also be software in the electronic device, etc. The software may be, for example, software for reporting the channel state information, etc. In the following embodiments, the execution subject is taken as an example of a terminal device.

[0071] like Figure 2 As shown, the method comprises the following steps:

[0072] Step 201, determining the values ​​of Doppler related parameters adapted to each Doppler gear.

[0073] In the embodiment of the present disclosure, the Doppler related parameters may include at least one of the following: Doppler frequency, symbol time difference between channel coefficient matrices, wherein different Doppler frequencies and / or different symbol time differences correspond to different Doppler gears.

[0074] Step 202: for each Doppler gear, determine the distribution function of the channel coefficient matrix correlation value in combination with the value of the Doppler related parameter adapted to the Doppler gear.

[0075] In the embodiment of the present disclosure, the channel coefficient matrix correlation value refers to the degree of mutual correlation between the elements in the channel coefficient matrix. This degree of correlation reflects the regularity or similarity of channel changes in different dimensions such as time, frequency, and space.

[0076] The distribution function may include at least one of the following: a Bessel function, an approximate sampling function. The Bessel function may be expressed as J 0 (2πf d Δt). Among them, f d represents the Doppler frequency; Δt represents the symbol time difference between the channel coefficient matrices. The approximate sampling function can be expressed as sinc(2πf d Δt).

[0077] Among them, the setting of multiple distribution functions enables the terminal device to flexibly select a distribution function according to actual needs for determining and processing the preprocessing matrix.

[0078] Step 203: Generate multiple reference channel coefficient matrices according to the distribution function.

[0079] Step 204: Determine a preprocessing matrix corresponding to the Doppler level according to a plurality of reference channel coefficient matrices.

[0080] In the embodiment of the present disclosure, it should be noted that the number of generated reference channel coefficient matrices may be greater than the number of historical channel coefficient matrices used in channel coefficient matrix prediction, that is, the number of historical channel coefficient matrices in step 206. For example, if the number of historical channel coefficient matrices used in channel coefficient matrix prediction is N, the number of reference channel coefficient matrices may be N+1.

[0081] In the embodiment of the present disclosure, among the generated multiple reference channel coefficient matrices, the symbol time intervals between adjacent reference channel coefficient matrices may be the same, which is the symbol time difference in the distribution function.

[0082] It is assumed that the number of the multiple reference channel coefficient matrices is N+1, corresponding to the channel coefficient matrices at symbol times in consecutive N+1 time slots. Based on the above assumption, the formula for determining the preprocessing matrix can be shown in the following formula (2), for example.

[0083] G bessel =x N (t)[x 0 (t)x 1 (t)...x N-1 (t)] T |x 0 (t)x 1 (t)...x N-1 (t) -2 (2)

[0084] Among them, G bessel represents the preprocessing matrix; x N (t) represents the N+1th reference channel coefficient matrix; x 0 (t)x 1 (t)...x N-1 (t) represents the first N reference channel coefficient matrices.

[0085] Step 205: Determine a matrix database according to the preprocessing matrix corresponding to each Doppler gear.

[0086] In the disclosed embodiment, the terminal device may combine and process each Doppler gear and the corresponding preprocessing matrix to obtain a matrix database.

[0087] Step 206: Acquire multiple historical channel coefficient matrices of the target channel and the current Doppler level of the target channel.

[0088] Step 207: query the matrix database according to the current Doppler gear position to obtain the preprocessing matrix corresponding to the current Doppler gear position in the matrix database.

[0089] Step 208: Filter the multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at the current time point.

[0090] Step 209: Determine the predicted channel coefficient matrix at the current time point according to the preprocessed data and the channel coefficient matrix prediction model.

[0091] Step 210: Determine and report channel state information CSI in combination with the predicted channel coefficient matrix.

[0092] It should be noted that the detailed description of steps 206 to 210 can be found in Figure 1 Steps 101 to 104 in the illustrated embodiment will not be described in detail herein.

[0093] In the method for reporting channel state information of the embodiment of the present disclosure, the numerical values ​​of Doppler related parameters adapted to each Doppler gear are determined; for each Doppler gear, the distribution function of the channel coefficient matrix correlation value is determined in combination with the numerical values ​​of the Doppler related parameters adapted to the Doppler gear; according to the distribution function, multiple reference channel coefficient matrices are generated; according to the multiple reference channel coefficient matrices, a preprocessing matrix corresponding to the Doppler gear is determined; according to the preprocessing matrix corresponding to each Doppler gear, a matrix database is determined; multiple historical channel coefficient matrices of the target channel and the current Doppler gear of the target channel are obtained; according to the current Doppler gear, the matrix database is queried to obtain the preprocessing matrix corresponding to the current Doppler gear in the matrix database; according to the preprocessing matrix, multiple historical channel coefficient matrices are filtered. Obtain preprocessing data at the current time point; determine the predicted channel coefficient matrix at the current time point based on the preprocessing data and the channel coefficient matrix prediction model; determine and report channel state information CSI in combination with the predicted channel coefficient matrix; wherein, determine the distribution function of the channel coefficient matrix correlation value in combination with the value of the Doppler related parameter adapted to the Doppler gear position; and then generate multiple reference channel coefficient matrices for preprocessing matrix generation processing, so that the terminal device can directly obtain the preprocessing matrix in combination with the current Doppler gear position query, and perform channel coefficient matrix prediction processing, and can determine the preprocessing matrix in advance, thereby shortening the prediction time of the channel coefficient matrix, improving the prediction efficiency of the coefficient matrix, and then improving the reporting speed of CSI information, and further avoiding the situation where CSI information is out of date.

[0094] Figure 3This is a flow chart of a method for reporting channel state information of another embodiment of the present disclosure. It should be noted that the method for reporting channel state information of this embodiment can be applied to a channel state information reporting device, which can be configured in an electronic device or chip so that the electronic device or chip can perform the channel state information reporting function.

[0095] Among them, the electronic device can be any device with computing capabilities, such as a personal computer (PC), a mobile terminal, a terminal device, a server, etc. The mobile terminal can be, for example, a vehicle-mounted device, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and other hardware devices with various operating systems, touch screens and / or display screens.

[0096] In addition, the reporting device of the channel state information may also be software in the electronic device, etc. The software may be, for example, software for reporting the channel state information, etc. In the following embodiments, the execution subject is taken as an example of a terminal device.

[0097] like Figure 3 As shown, the method comprises the following steps:

[0098] Step 301: Acquire multiple historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler level of the target channel.

[0099] Step 302: Filter multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at the current time point.

[0100] Step 303: Perform inverse fast Fourier transform processing on the pre-processed data to obtain first transformed data.

[0101] In the embodiment of the present disclosure, the inverse fast Fourier transform formula for the preprocessed data can be shown as the following formula (3).

[0102]

[0103] in, Indicates preprocessed data; IFFT indicates inverse fast Fourier transform processing; Represents the first transformed data.

[0104] It should be noted that the inverse fast Fourier transform process is a transformation process from frequency domain data to time domain data, that is, each frequency component in the data is converted into a time domain signal.

[0105] Step 304: performing zeroing processing on the first values ​​in the first transformed data that are less than or equal to the threshold value to obtain processed data.

[0106] In the embodiment of the present disclosure, as an alternative to step 304, the terminal device may delete the first value in the first transformed data that is less than or equal to the threshold value.

[0107] In the embodiment of the present disclosure, the formula for performing the first value zeroing processing on the first transformed data may be as shown in the following formula (4).

[0108]

[0109] Wherein, TH represents a threshold value; Find represents searching for a value greater than the threshold value in the first transformed data, that is, setting the first value less than or equal to the threshold value to zero or deleting it; Represents processed data.

[0110] Step 305: Perform fast Fourier transform processing on the processed data to obtain second transformed data.

[0111] Among them, setting the first value in the first transformed data to zero or deleting it can reduce the amount of data in the first transformed data, and the amount of data in the second transformed data can be reduced after fast Fourier transform processing.

[0112] It should be noted that the fast Fourier transform process is a transformation process from time domain data to frequency domain data, that is, each time domain signal in the data is converted into a frequency domain component. The first value in the first transformed data is set to zero or deleted, etc., which can reduce the number of time domain signals in the time domain data, and then reduce the number of frequency domain components in the frequency domain data, that is, reduce the data volume of the second transformed data.

[0113] Step 306: update the pre-processed data according to the second transformed data.

[0114] In the disclosed embodiment, the terminal device may use the second transformed data as the updated pre-processed data. The amount of data in the second transformed data is reduced, and thus the amount of data in the pre-processed data is reduced, so that after the pre-processed data is input into the channel coefficient matrix prediction model, the amount of processing of the pre-processed data by the channel coefficient matrix prediction model can be reduced, and the accuracy of the determined predicted channel coefficient matrix can be improved.

[0115] Step 307: Determine the predicted channel coefficient matrix at the current time point according to the preprocessed data and the channel coefficient matrix prediction model.

[0116] In the embodiment of the present disclosure, the process of the terminal device executing step 307 may, for example, be to obtain channel characteristic parameters of the target channel; input the preprocessed data and the channel characteristic parameters into the channel coefficient matrix prediction model, and obtain the predicted channel coefficient matrix output by the channel coefficient matrix prediction model.

[0117] The channel characteristic parameters may include at least one of the following: current Doppler frequency deviation, channel delay spread, and channel spatial correlation.

[0118] In the disclosed embodiment, the channel coefficient matrix prediction model may include an input layer, a hidden layer, and an output layer. The data inputted by the input layer may include preprocessed data, current Doppler frequency deviation, channel delay spread, and channel spatial correlation. The data outputted by the output layer may be the predicted channel coefficient matrix at the current time point.

[0119] The hidden layer may be provided with an activation function and a loss function. The activation function may include at least one of the following: a logical function Sigmoid, a hyperbolic tangent function Tanh, a linear rectifier function ReLU, etc. The Sigmoid function can map a real number to the interval (0,1) and can be used for binary classification. The Tanh function is a symmetric function centered at zero. The ReLU function refers to a nonlinear function represented by a ramp function and its variants.

[0120] The loss function may include at least one of the following: a mean square error function, a cross entropy function, etc.

[0121] Step 308: Determine and report channel state information CSI in combination with the predicted channel coefficient matrix.

[0122] It should be noted that the detailed description of steps 301 to 302, and steps 307 to 308 can be found in Figure 1 Steps 101 to 104 in the illustrated embodiment will not be described in detail herein.

[0123] In the method for reporting channel state information of the embodiment of the present disclosure, multiple historical channel coefficient matrices of the target channel and a preprocessing matrix corresponding to the current Doppler gear of the target channel are obtained; the multiple historical channel coefficient matrices are filtered according to the preprocessing matrix to obtain preprocessing data at the current time point; the preprocessing data is subjected to inverse fast Fourier transform processing to obtain first transformed data; a first value less than or equal to a threshold value in the first transformed data is set to zero to obtain processed data; the processed data is subjected to fast Fourier transform processing to obtain second transformed data; the preprocessing data is subjected to inverse fast Fourier transform processing according to the second transformed data Processing data for updating; determining the predicted channel coefficient matrix at the current time point according to the preprocessed data and the channel coefficient matrix prediction model; determining and reporting the channel state information CSI in combination with the predicted channel coefficient matrix; wherein, the inverse fast Fourier transform processing, the first value zeroing processing and the fast Fourier transform processing of the preprocessed data can reduce the data volume of the preprocessed data, thereby reducing the processing volume of the channel coefficient matrix prediction model on the preprocessed data, thereby further accelerating the prediction speed of the channel coefficient matrix, thereby further avoiding the situation where the CSI information is outdated, thereby ensuring the channel throughput.

[0124] Figure 4 The present invention is a schematic diagram of the structure of a channel state information reporting device according to an embodiment of the present invention.

[0125] like Figure 4 As shown, the channel state information reporting device may include: an acquisition module 401, a filtering processing module 402, a determination module 403 and a reporting processing module 404.

[0126] Among them, the acquisition module 401 is used to acquire multiple historical channel coefficient matrices of the target channel and the preprocessing matrix corresponding to the current Doppler gear of the target channel; the filtering processing module 402 is used to filter the multiple historical channel coefficient matrices according to the preprocessing matrix to obtain the preprocessing data at the current time point; the determination module 403 is used to determine the predicted channel coefficient matrix at the current time point according to the preprocessing data and the channel coefficient matrix prediction model; the reporting processing module 404 is used to determine and report the channel state information CSI in combination with the predicted channel coefficient matrix.

[0127] In one embodiment of the present disclosure, the acquisition module 401 is specifically used to acquire multiple historical channel coefficient matrices of the target channel; determine the current Doppler gear of the target channel; query the matrix database according to the current Doppler gear, and acquire the preprocessing matrix corresponding to the current Doppler gear in the matrix database.

[0128] In one embodiment of the present disclosure, the matrix database is determined in the following manner: determining the values ​​of Doppler-related parameters adapted to each Doppler gear; for each Doppler gear, determining the distribution function of the channel coefficient matrix correlation value in combination with the values ​​of the Doppler-related parameters adapted to the Doppler gear; generating a plurality of reference channel coefficient matrices according to the distribution function; determining a preprocessing matrix corresponding to the Doppler gear according to the plurality of reference channel coefficient matrices; and determining the matrix database according to the preprocessing matrices corresponding to each of the Doppler gears.

[0129] In one embodiment of the present disclosure, the Doppler-related parameter includes at least one of the following: Doppler frequency, and symbol time difference between channel coefficient matrices.

[0130] In one embodiment of the present disclosure, the distribution function includes at least one of the following: a Bessel function and an approximate sampling function.

[0131] In one embodiment of the present disclosure, the device also includes: a first transformation module, a zeroing processing module, a second transformation module and an update processing module; the first transformation module is used to perform inverse fast Fourier transform processing on the pre-processed data to obtain first transformed data; the zeroing processing module is used to perform zero processing on a first value less than or equal to a threshold value in the first transformed data to obtain processed data; the second transformation module is used to perform fast Fourier transform processing on the processed data to obtain second transformed data; the update processing module is used to update the pre-processed data according to the second transformed data.

[0132] In one embodiment of the present disclosure, the determination module 403 is specifically used to obtain the channel characteristic parameters of the target channel; input the preprocessed data and the channel characteristic parameters into the channel coefficient matrix prediction model, and obtain the predicted channel coefficient matrix output by the channel coefficient matrix prediction model.

[0133] In one embodiment of the present disclosure, the channel characteristic parameter includes at least one of the following: the current Doppler frequency offset, channel delay spread, and channel spatial correlation.

[0134] In one embodiment of the present disclosure, the channel coefficient matrix prediction model is obtained by training in combination with multiple channel coefficient matrix sequences; the number of sample channel coefficient matrices in the channel coefficient matrix sequence is greater than the number of historical channel coefficient matrices used in channel coefficient matrix prediction.

[0135] In the reporting device for channel state information of the embodiment of the present disclosure, multiple historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler position of the target channel are obtained; the multiple historical channel coefficient matrices are filtered according to the preprocessing matrix to obtain preprocessing data at a current time point; the predicted channel coefficient matrix at the current time point is determined according to the preprocessing data and a channel coefficient matrix prediction model; and channel state information CSI is determined and reported in combination with the predicted channel coefficient matrix; wherein, predicting the channel coefficient matrix at the current time point in combination with the multiple historical channel coefficient matrices of the target channel can shorten the reporting period of CSI information, increase the reporting frequency of CSI information, avoid outdated CSI information, and thus ensure channel throughput.

[0136] According to a third aspect of an embodiment of the present disclosure, there is also provided an electronic device, including: a processor; and a memory for storing instructions executable by the processor, wherein the processor is configured to: implement the steps of the channel state information reporting method as described above.

[0137] In order to implement the above embodiments, the present disclosure also proposes a non-transitory computer-readable storage medium.

[0138] When the instructions in the storage medium are executed by the processor, the processor is enabled to execute the channel state information reporting method as described above.

[0139] In order to implement the above embodiments, the present disclosure also provides a computer program product.

[0140] When the computer program product is executed by a processor of an electronic device, the electronic device is enabled to execute the above method.

[0141] Figure 5 The figure is a structural block diagram of an electronic device according to an exemplary embodiment. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0142] like Figure 5As shown, the electronic device 1000 includes a processor 111, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 112 or a program loaded from a memory 116 to a random access memory (RAM) 113. In the RAM 113, various programs and data required for the operation of the electronic device 1000 are also stored. The processor 111, the ROM 112, and the RAM 113 are connected to each other via a bus 114. An input / output (I / O) interface 115 is also connected to the bus 114.

[0143] The following components are connected to the I / O interface 115: a memory 116 including a hard disk, etc.; and a communication part 117 including a network interface card such as a local area network (LAN) card, a modem, etc., and the communication part 117 performs communication processing via a network such as the Internet; a drive 118 is also connected to the I / O interface 115 as needed.

[0144] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 117. When the computer program is executed by the processor 111, the above-mentioned functions defined in the method of the present disclosure are executed.

[0145] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, and the instructions can be executed by the processor 111 of the electronic device 1000 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0146] In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0147] Figure 6 FIG. 1 is a schematic diagram of the structure of a chip according to an embodiment of the present disclosure. Figure 6 As shown, the chip includes a processor 601 and an interface circuit 602. The number of the processor 601 can be one or more, and the number of the interface circuit 602 can be one or more.

[0148] Optionally, the interface circuit 602 is used to receive a signal, where the signal includes a computer instruction. When the processor 601 executes the computer instruction, the chip executes the channel state information reporting method described in the above embodiment of the present disclosure.

[0149] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0150] In addition, the word "exemplary" is used herein to indicate serving as an example, instance, or diagram. Any aspect or design described as "exemplary" in this article is not necessarily understood to be advantageous compared to other aspects or designs. On the contrary, the use of the word exemplary is intended to present concepts in a specific way. As used herein, the term "or" is intended to represent an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X applies A or B" is intended to represent any one of the natural inclusive arrangements. That is, if X applies A; X applies B; or X applies both A and B, "X applies A or B" is satisfied under any of the aforementioned examples. In addition, unless otherwise specified or clearly pointed to a singular form from the context, the articles "one" and "an" as used in this application and the appended claims are generally understood to mean "one or more".

[0151] Likewise, although the present disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art after reading and understanding the specification and drawings. The present disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components (e.g., elements, resources, etc.) described above, unless otherwise noted, the terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific functions of the described components, even if the structure is not equivalent to the disclosed structure. In addition, although the specific features of the present disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations as may be desired and conducive to any given or specific application. In addition, with respect to "including", "having", "having", "having", or variations thereof used in the specific embodiments or claims, such terms are intended to be inclusive in a manner similar to the term "comprising".

[0152] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0153] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for reporting channel state information, characterized in that: The method comprises: Acquire multiple historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler position of the target channel; Performing filtering processing on the multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at a current time point; Determining a predicted channel coefficient matrix at the current time point according to the preprocessed data and a channel coefficient matrix prediction model; In combination with the predicted channel coefficient matrix, channel state information CSI is determined and reported.

2. The method according to claim 1, characterized in that: The acquiring of multiple historical channel coefficient matrices of the target channel and a preprocessing matrix corresponding to the current Doppler gear position of the target channel includes: Acquire multiple historical channel coefficient matrices of the target channel; Determining a current Doppler level of the target channel; A matrix database is queried according to the current Doppler gear position to obtain a preprocessing matrix corresponding to the current Doppler gear position in the matrix database.

3. The method according to claim 2, characterized in that The matrix database is determined in the following manner: Determine the values ​​of Doppler related parameters adapted to each Doppler gear position; For each Doppler gear, combining the values ​​of Doppler related parameters adapted to the Doppler gear, determining a distribution function of the channel coefficient matrix correlation value; generating a plurality of reference channel coefficient matrices according to the distribution function; Determining a preprocessing matrix corresponding to the Doppler gear position according to the multiple reference channel coefficient matrices; The matrix database is determined according to the preprocessing matrix corresponding to each of the Doppler gears.

4. The method according to claim 3, characterized in that The Doppler related parameters include at least one of the following: Doppler frequency, and symbol time difference between channel coefficient matrices.

5. The method according to claim 3, characterized in that: The distribution function includes at least one of the following: a Bessel function and an approximate sampling function.

6. The method according to claim 1, characterized in that After filtering the multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at the current time point, the method further includes: Performing inverse fast Fourier transform processing on the pre-processed data to obtain first transformed data; Performing zero processing on a first value in the first transformed data that is less than or equal to a threshold value to obtain processed data; Performing fast Fourier transform processing on the processed data to obtain second transformed data; The pre-processed data is updated according to the second transformed data.

7. The method according to claim 1, characterized in that The step of determining the predicted channel coefficient matrix at the current time point according to the preprocessed data and the channel coefficient matrix prediction model includes: Acquiring channel characteristic parameters of the target channel; The preprocessed data and the channel characteristic parameters are input into the channel coefficient matrix prediction model to obtain the predicted channel coefficient matrix output by the channel coefficient matrix prediction model.

8. The method according to claim 7, characterized in that The channel characteristic parameters include at least one of the following: the current Doppler frequency offset, channel delay spread, and channel spatial correlation.

9. The method according to claim 1 or 7, characterized in that: The channel coefficient matrix prediction model is obtained by training in combination with a plurality of channel coefficient matrix sequences; the number of sample channel coefficient matrices in the channel coefficient matrix sequence is greater than the number of historical channel coefficient matrices used in channel coefficient matrix prediction.

10. A channel state information reporting device, characterized in that: The device comprises: An acquisition module, used to acquire multiple historical channel coefficient matrices of a target channel and a preprocessing matrix corresponding to a current Doppler gear position of the target channel; A filtering processing module, used for filtering the multiple historical channel coefficient matrices according to the preprocessing matrix to obtain preprocessing data at a current time point; A determination module, used to determine the predicted channel coefficient matrix at the current time point according to the preprocessed data and the channel coefficient matrix prediction model; The reporting processing module is used to determine and report the channel state information CSI in combination with the predicted channel coefficient matrix.

11. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; Wherein, the processor is configured to: Implement the steps of the method for reporting channel state information as described in any one of claims 1 to 9. 12 . A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor, enables the processor to execute the channel state information reporting method according to any one of claims 1 to 9.

13. A chip, characterized in that: The chip comprises one or more interface circuits and one or more processors; the interface circuit is used to receive a signal, the signal comprises a computer instruction, and when the processor executes the computer instruction, the chip executes the channel state information reporting method according to any one of claims 1 to 9.

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