Noise covariance matrix updating method, electronic equipment and storage medium

Abstract

The invention provides a noise covariance matrix updating method, electronic equipment and a storage medium, which are applied to the technical field of communication, and the noise covariance matrix updating method comprises the steps that a plurality of noise covariance matrixes corresponding to the current statistical duration are acquired, and each noise covariance matrix corresponds to one statistical granularity; for each statistical granularity, determining a plurality of statistical values according to the noise covariance matrix corresponding to the statistical granularity, and updating the noise covariance matrix according to the magnitude relationship between the plurality of statistical values and each threshold value, each threshold value comprising a first threshold value, and a second threshold value comprising a second threshold value; the first threshold value corresponding to the next statistical duration is updated according to the plurality of statistical values corresponding to the plurality of noise covariance matrixes corresponding to the current statistical duration. According to the updating method of the noise covariance matrix provided by the embodiment of the invention, the noise covariance matrix can be adaptively adjusted, so that the accuracy of the noise covariance matrix can be improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method for updating a noise covariance matrix, an electronic device, and a storage medium. Background Art

[0002] In the 5G New Radio (NR) system, the calculation of the noise covariance matrix has a great impact on the final performance and computational complexity. The noise covariance matrix is ​​a matrix used to describe the noise correlation and variance between variables in multivariate data. The elements on the diagonal of the matrix describe the magnitude of the white noise component and the interference, and the elements on the off-diagonal of the matrix describe the magnitude of the interference. However, the accuracy of the noise covariance matrix obtained by the existing technology is low. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a method for updating a noise covariance matrix, an electronic device, and a storage medium, so as to solve the technical problem of low accuracy of the noise covariance matrix in the prior art.

[0004] In a first aspect, an embodiment of the present application provides a method for updating a noise covariance matrix, comprising: obtaining multiple noise covariance matrices corresponding to a current statistical duration, wherein each noise covariance matrix corresponds to a statistical granularity; for each statistical granularity, determining multiple statistical values ​​according to the noise covariance matrix corresponding to the statistical granularity, and updating the noise covariance matrix according to a size relationship between the multiple statistical values ​​and each threshold value, wherein the each threshold value includes a first threshold value, and the first threshold value corresponding to a next statistical duration is updated according to the multiple statistical values ​​corresponding to the multiple noise covariance matrices corresponding to the current statistical duration.

[0005] In the above scheme, since the overall interference situation of the channel can be determined based on the noise covariance matrix corresponding to the current statistical duration, multiple statistical values ​​corresponding to the current statistical duration can be determined based on the noise covariance matrix corresponding to the current statistical duration, and the noise covariance matrix can be updated by comparing the size relationship between multiple statistical values ​​and each threshold value. In addition, the first threshold value can also be updated based on the multiple statistical values ​​corresponding to the current statistical duration, and then the noise covariance matrix corresponding to the next statistical duration is updated based on the updated first threshold value. By the updating method of the noise covariance matrix provided by the embodiment of the present application, the noise covariance matrix can be adaptively adjusted, so that the accuracy of the noise covariance matrix can be improved.

[0006] In an optional embodiment, the multiple statistical values ​​include the square mean corresponding to the diagonal elements in the noise covariance matrix and the square mean corresponding to the non-diagonal elements in the noise covariance matrix; the updating of the noise covariance matrix according to the size relationship between the multiple statistical values ​​and each threshold value includes: if the ratio of the square mean corresponding to the noise covariance matrix to the square mean is less than the first threshold value, then the non-diagonal elements of the noise covariance matrix are set to 0. In the above scheme, since the diagonal elements of the noise covariance matrix represent noise and possible interference conditions and the non-diagonal elements represent possible interference conditions, if the ratio of the square mean corresponding to the non-diagonal elements to the square mean corresponding to the diagonal elements is small, it can be considered that the interference effect is smaller than the noise effect, so the non-diagonal elements can be set to 0, thereby improving the accuracy of the noise covariance matrix.

[0007] In an optional implementation, the multiple statistical values ​​include the number of matrices used to calculate the noise covariance matrix, and the various threshold values ​​also include a second threshold value; the updating of the noise covariance matrix according to the size relationship between the multiple statistical values ​​and the various threshold values ​​includes: if the number of matrices corresponding to the noise covariance matrix is ​​less than the second threshold value, then the diagonal elements of the noise covariance matrix are replaced with noise power, wherein the noise power is estimated using channel parameters. In the above scheme, when the noise covariance estimate is insufficient, the noise power estimated by the channel parameters can be directly replaced, thereby increasing the noise estimation accuracy in the scenario where the estimate is insufficient.

[0008] In an optional implementation, the multiple statistical values ​​include a first mean corresponding to the diagonal elements in the noise covariance matrix, and the various threshold values ​​also include a third threshold value; the updating of the noise covariance matrix according to the size relationship between the multiple statistical values ​​and the various threshold values ​​includes: if the first mean corresponding to the noise covariance matrix is ​​less than the third threshold value, the diagonal elements of the noise covariance matrix are replaced with noise power, wherein the noise power is obtained by using channel parameter estimation, and / or the non-diagonal elements of the noise covariance matrix are set to 0. In the above scheme, since the diagonal elements of the noise covariance matrix represent the noise and possible interference, they will not be less than the noise power estimated by the channel parameter estimation module in theory. Therefore, if the diagonal elements of the noise covariance matrix are small, it can be considered that their credibility is low, and they can be replaced with the noise power estimated by the channel parameters, thereby improving the accuracy of the noise covariance matrix.

[0009] In an optional implementation, the various threshold values ​​also include a fourth threshold value, and the fourth threshold value is greater than the second threshold value; the updating of the noise covariance matrix according to the size relationship between the multiple statistical values ​​and the various threshold values ​​also includes: if the number of matrices corresponding to the noise covariance matrix is ​​less than the fourth threshold value, the non-diagonal elements of the noise covariance matrix are set to 0. In the above scheme, since the diagonal elements of the noise covariance matrix represent noise and possible interference, and the non-diagonal elements of the noise covariance matrix represent possible interference, if the diagonal elements of the noise covariance matrix are small, it can be considered that the current scene is an interference-limited scene, so the non-diagonal elements can be set to 0, thereby improving the accuracy of the noise covariance matrix; in the case of insufficient noise covariance estimation, the non-diagonal elements can be directly set to 0, thereby increasing the noise estimation accuracy of the scene with insufficient estimation.

[0010] In an optional implementation, the first threshold value corresponding to the next statistical duration is updated according to the following steps: calculating the second mean of the square of the mean corresponding to the current statistical duration, and the third mean of the square mean corresponding to the current statistical duration; updating the first threshold value according to the second mean and the third mean corresponding to the current statistical duration. In the above scheme, in addition to the statistical value corresponding to the current statistical duration, multiple statistical values ​​corresponding to the next statistical duration are introduced. By horizontally increasing the statistics of the covariance matrix, the overall interference of the channel can be better determined, thereby adaptively adjusting the noise covariance matrix to improve the accuracy of the noise covariance matrix.

[0011] In an optional implementation, the updating of the first threshold value according to the second mean and the third mean corresponding to the current statistical duration includes: filtering the second mean and the third mean corresponding to the current statistical duration, and the second mean and the third mean corresponding to the previous statistical duration to obtain a filtering result, wherein the filtering result is used to determine the interference situation of the current statistical duration; and updating the first threshold value according to the filtering result. In the above scheme, multiple statistical values ​​of the current statistical duration and multiple statistical values ​​corresponding to the previous statistical duration can be filtered before determining the overall interference situation of the channel, so that the noise covariance matrix can be more flexibly and adaptively adjusted to improve the accuracy of the noise covariance matrix.

[0012] In a second aspect, an embodiment of the present application provides a device for updating a noise covariance matrix, comprising: an acquisition module, used to acquire multiple noise covariance matrices corresponding to a current statistical duration, wherein each noise covariance matrix corresponds to a statistical granularity; a determination module, used to determine multiple statistical values ​​for each statistical granularity according to the noise covariance matrix corresponding to the statistical granularity, and update the noise covariance matrix according to a size relationship between the multiple statistical values ​​and each threshold value, wherein the each threshold value includes a first threshold value, and the first threshold value corresponding to the next statistical duration is updated according to the multiple statistical values ​​corresponding to the multiple noise covariance matrices corresponding to the current statistical duration.

[0013] In the above scheme, since the overall interference situation of the channel can be determined based on the noise covariance matrix corresponding to the current statistical duration, multiple statistical values ​​corresponding to the current statistical duration can be determined based on the noise covariance matrix corresponding to the current statistical duration, and the noise covariance matrix can be updated by comparing the size relationship between multiple statistical values ​​and each threshold value. In addition, the first threshold value can also be updated based on the multiple statistical values ​​corresponding to the current statistical duration, and then the noise covariance matrix corresponding to the next statistical duration is updated based on the updated first threshold value. By the updating method of the noise covariance matrix provided by the embodiment of the present application, the noise covariance matrix can be adaptively adjusted, so that the accuracy of the noise covariance matrix can be improved.

[0014] In an optional embodiment, the multiple statistical values ​​include the square mean corresponding to the diagonal elements in the noise covariance matrix and the square mean corresponding to the non-diagonal elements in the noise covariance matrix; the determination module is specifically used to: if the ratio of the square mean corresponding to the noise covariance matrix to the square mean is less than the first threshold value, then the non-diagonal elements of the noise covariance matrix are set to 0. In the above scheme, since the diagonal elements of the noise covariance matrix represent noise and possible interference conditions and the non-diagonal elements represent possible interference conditions, if the ratio of the square mean corresponding to the non-diagonal elements to the square mean corresponding to the diagonal elements is small, it can be considered that the interference effect is smaller than the noise effect, so the non-diagonal elements can be set to 0, thereby improving the accuracy of the noise covariance matrix.

[0015] In an optional implementation, the multiple statistical values ​​include the number of matrices used to calculate the noise covariance matrix, and the respective threshold values ​​also include a second threshold value; the determination module is specifically used to: if the number of matrices corresponding to the noise covariance matrix is ​​less than the second threshold value, then replace the diagonal elements of the noise covariance matrix with noise power, wherein the noise power is estimated using channel parameters.

[0016] In an optional implementation, the multiple statistical values ​​include a first mean corresponding to the diagonal elements in the noise covariance matrix, and the threshold values ​​also include a third threshold value; the determination module is specifically used to: if the first mean corresponding to the noise covariance matrix is ​​less than the third threshold value, the diagonal elements of the noise covariance matrix are replaced with noise power, wherein the noise power is obtained by using channel parameter estimation, and / or the non-diagonal elements of the noise covariance matrix are set to 0. In the above scheme, since the diagonal elements of the noise covariance matrix represent possible interference conditions, they will not be less than the noise power estimated by the channel parameter estimation module in theory. Therefore, if the diagonal elements of the noise covariance matrix are small, it can be considered that their credibility is low, and they can be replaced with the noise power estimated by the channel parameters, thereby improving the accuracy of the noise covariance matrix.

[0017] In an optional implementation, the various threshold values ​​also include a fourth threshold value, and the fourth threshold value is greater than the second threshold value; the determination module is specifically used to: if the number of matrices corresponding to the noise covariance matrix is ​​less than the fourth threshold value, then the non-diagonal elements of the noise covariance matrix are set to 0. In the above scheme, since the diagonal elements of the noise covariance matrix represent possible interference situations, and the non-diagonal elements of the noise covariance matrix represent possible interference situations, if the diagonal elements of the noise covariance matrix are small, it can be considered that the current scene is an interference-limited scene, so the non-diagonal elements can be set to 0, thereby improving the accuracy of the noise covariance matrix; in the case of insufficient noise covariance estimation, the non-diagonal elements can be directly set to 0, thereby increasing the noise estimation accuracy of the scene with insufficient estimation.

[0018] In an optional implementation, the updating device of the noise covariance matrix also includes: an updating module, which is used to update the first threshold value corresponding to the next statistical duration according to the following steps: calculating the second mean of the square of the mean corresponding to the current statistical duration, and the third mean of the square mean corresponding to the current statistical duration; updating the first threshold value according to the second mean and the third mean corresponding to the current statistical duration. In the above scheme, in addition to the multiple statistical values ​​corresponding to the current statistical duration, multiple statistical values ​​corresponding to the next statistical duration are also introduced. By horizontally increasing the statistics of the covariance matrix, the overall interference of the channel can be better determined, thereby adaptively adjusting the noise covariance matrix to improve the accuracy of the noise covariance matrix.

[0019] In an optional implementation, the update module is specifically used to: filter the second mean and the third mean corresponding to the current statistical duration, and the second mean and the third mean corresponding to the previous statistical duration to obtain a filtering result, and the filtering result is used to determine the interference situation of the current statistical duration; and update the first threshold value according to the filtering result. In the above scheme, the overall interference situation of the channel can be determined after filtering multiple statistical values ​​corresponding to the current statistical duration and multiple statistical values ​​corresponding to the previous statistical duration, so that the noise covariance matrix can be more flexibly and adaptively adjusted to improve the accuracy of the noise covariance matrix.

[0020] In a third aspect, an embodiment of the present application provides a computer program product, comprising computer program instructions, which, when read and executed by a processor, execute the method for updating the noise covariance matrix as described in the first aspect.

[0021] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory and a bus; the processor and the memory communicate with each other through the bus; the memory stores computer program instructions that can be executed by the processor, and the processor calls the computer program instructions to execute the noise covariance matrix updating method as described in the first aspect.

[0022] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer program instructions. When the computer program instructions are executed by a computer, the computer executes the method for updating the noise covariance matrix as described in the first aspect.

[0023] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the following specifically cites the embodiments of the present application and describes them in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A flowchart of a method for updating a noise covariance matrix provided in an embodiment of the present application; Figure 2 A structural block diagram of a noise covariance matrix updating device provided in an embodiment of the present application; Figure 3A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0027] Please refer to Figure 1 , Figure 1 A flowchart of a method for updating a noise covariance matrix provided in an embodiment of the present application, the method for updating the noise covariance matrix may specifically include the following steps: Step S101: obtaining a plurality of noise covariance matrices corresponding to the current statistical duration, wherein each noise covariance matrix corresponds to a statistical granularity.

[0028] Step S102: For each statistical granularity, multiple statistical values ​​are determined according to the noise covariance matrix corresponding to the statistical granularity, and the noise covariance matrix is ​​updated according to the size relationship between the multiple statistical values ​​and each threshold value, wherein each threshold value includes a first threshold value, and the first threshold value corresponding to the next statistical duration is updated according to the multiple statistical values ​​corresponding to the multiple noise covariance matrices corresponding to the current statistical duration.

[0029] Specifically, in the above step S101, the noise covariance matrices corresponding to the multiple statistical granularities corresponding to the current statistical duration can be obtained. The noise covariance matrix corresponding to the above current statistical duration can be the noise covariance matrix corresponding to all statistical granularities within the statistical duration, or can also be the noise covariance matrix corresponding to some statistical granularities within the statistical duration.

[0030] As an implementation manner, the statistical granularity may be a physical resource group (PRG). As another implementation manner, the statistical duration may be a time slot.

[0031] It should be noted that the embodiments of the present application do not specifically limit the specific implementation methods for obtaining the noise covariance matrix, and those skilled in the art can make appropriate adjustments according to actual conditions. For example, the statistical granularity for calculating the noise covariance matrix can be determined first; then the pilot signal data or other signal data that can be used for channel estimation related to the statistical granularity is collected; the channel estimation is then performed using the collected data to estimate the transmission characteristics of the channel and separate the noise component from the received signal; finally, based on the separated noise data, the noise covariance matrix on the statistical granularity is calculated, wherein the noise covariance matrix on the statistical granularity can be obtained by calculating the covariance between each sample in the noise signal and placing these covariance values ​​into a matrix.

[0032] Taking the statistical granularity of PRG as an example, for the The noise covariance matrix corresponding to a PRG can be expressed using the following formula: ; in, Indicates The noise covariance matrix corresponding to the PRG is is the first OK The element value of the column, Indicates the number of receiving antennas.

[0033] In the above step S102, for each statistical granularity, multiple statistical values ​​can be determined according to the noise covariance matrix corresponding to the statistical granularity. It should be noted that the embodiment of the present application does not specifically limit the specific implementation method for determining multiple statistical values, and those skilled in the art can make appropriate adjustments according to actual conditions. For example, multiple statistical values ​​can be obtained by calculating the mean of the diagonal elements in the noise covariance matrix; or, multiple statistical values ​​can be obtained by calculating the mean of the squares of the non-diagonal elements in the noise covariance matrix, etc.

[0034] In addition, in the above step S102, the noise covariance matrix may be updated by comparing the magnitude relationship between the above multiple statistical values ​​and each threshold value.

[0035] It should be noted that the embodiments of the present application do not specifically limit the specific implementation method of updating the noise covariance matrix, and those skilled in the art can make appropriate adjustments according to actual conditions. For example, the diagonal elements of the noise covariance matrix can be replaced with noise power; or, the non-diagonal elements of the noise covariance matrix can be set to 0, etc.

[0036] In addition, the embodiments of the present application do not specifically limit the specific implementation of the above-mentioned threshold values, and those skilled in the art can make appropriate adjustments according to actual conditions. For example, the threshold values ​​may include a first threshold value for evaluating the degree of antenna interference relative to noise interference, a second threshold value and a fourth threshold value for evaluating the number of matrices used when calculating each noise covariance matrix, and a third threshold value for evaluating the size of the diagonal elements in the noise covariance matrix.

[0037] As an implementation mode, the above-mentioned threshold values ​​may include a first threshold value, wherein the first threshold value is used to determine whether to update the noise covariance matrix corresponding to the current statistical duration. The first threshold value corresponding to the next statistical duration may be updated according to multiple statistical values ​​corresponding to multiple noise covariance matrices corresponding to the current statistical duration, thereby obtaining a new first threshold value.

[0038] It should be noted that the specific implementation method of updating the first threshold value is not specifically limited in the embodiment of the present application, and those skilled in the art can make appropriate adjustments according to actual conditions. For example, when a statistical value among multiple statistical values ​​is large, the first threshold value can be increased; or when a statistical value among multiple statistical values ​​is small, the first threshold value can be reduced, etc.

[0039] In the above scheme, since the overall interference situation of the channel can be determined based on the noise covariance matrix corresponding to the current statistical duration, multiple statistical values ​​corresponding to the current statistical duration can be determined based on the noise covariance matrix corresponding to the current statistical duration, and the noise covariance matrix can be updated by comparing the size relationship between multiple statistical values ​​and each threshold value. In addition, the first threshold value can also be updated based on the multiple statistical values ​​corresponding to the current statistical duration, and then the noise covariance matrix corresponding to the next statistical duration is updated based on the updated first threshold value. By the updating method of the noise covariance matrix provided by the embodiment of the present application, the noise covariance matrix can be adaptively adjusted, so that the accuracy of the noise covariance matrix can be improved.

[0040] Further, based on the above embodiment, as an implementation mode, the multiple statistical values ​​may include the mean square corresponding to the diagonal elements in each noise covariance matrix and the square mean corresponding to the non-diagonal elements in each noise covariance matrix, and each threshold value includes a first threshold value; at this time, the above step S102 may specifically include the following steps: If the ratio of the square mean corresponding to the noise covariance matrix to the square of the mean is less than a first threshold value, the non-diagonal elements of the noise covariance matrix are set to 0.

[0041] Specifically, the above mean square refers to calculating the mean of the diagonal elements in the noise covariance matrix and then squaring them, which can be expressed by the following formula: ; ; in, Indicates The first mean corresponding to the diagonal elements in the noise covariance matrix, Indicates The noise covariance matrix OK The element value of the column, represents the number of receiving antennas, Indicates The square of the mean corresponding to the diagonal elements in the noise covariance matrix.

[0042] It can be understood that, assuming that the interference power is the same for each receiving antenna, the mean square corresponding to the diagonal elements in the noise covariance matrix is ​​equal to the square mean.

[0043] The above square mean refers to the mean calculated after squaring the off-diagonal elements in the noise covariance matrix, which can be expressed by the following formula: ; in, represents the square mean corresponding to the off-diagonal elements in the noise covariance matrix, represents the noise covariance matrix OK The element value of the column.

[0044] It should be noted that the diagonal elements in the noise covariance matrix are all real numbers, so you can calculate the mean first and then square it, or you can square it first and then calculate the mean; other calculation methods are also possible, the purpose is to determine whether the diagonal elements in the noise covariance matrix are too small. The off-diagonal elements in the noise covariance matrix are non-real numbers, so you need to square them first and then calculate the mean; other calculation methods are also possible, for example: calculate the modulus and then square it, the purpose is to determine whether it is the same as Is the ratio between them too small?

[0045] Since the diagonal elements of the noise covariance matrix represent the noise and possible interference and the off-diagonal elements represent possible interference, if , it can be considered that the interference effect is smaller than the noise effect, so the non-diagonal elements of the noise covariance matrix can be set to 0; where, Indicates the first threshold.

[0046] In the above scheme, since the diagonal elements of the noise covariance matrix represent noise and possible interference and the non-diagonal elements represent possible interference, if the ratio of the square means corresponding to the non-diagonal elements is smaller than the square means corresponding to the diagonal elements, it can be considered that the interference effect is smaller than the noise effect. Therefore, the non-diagonal elements can be set to 0, thereby improving the accuracy of the noise covariance matrix.

[0047] Further, based on the above embodiment, as another implementation mode, the multiple statistical values ​​may include the number of matrices used when calculating each noise covariance matrix, and each threshold value may also include a second threshold value; in this case, the above step S102 may also include the following steps: If the number of matrices corresponding to the noise covariance matrix is ​​less than a second threshold value, or the first mean value corresponding to the noise covariance matrix is ​​less than a third threshold value, the diagonal elements of the noise covariance matrix are replaced with noise power, where the noise power is estimated using channel parameters.

[0048] Specifically, if the noise covariance estimator is insufficient, that is, , the diagonal elements can be directly replaced by the noise power estimated by the channel parameters; where represents the number of matrices used to calculate each noise covariance matrix, Indicates the second threshold value.

[0049] In the above scheme, when the noise covariance estimate is insufficient, the noise power estimated by the channel parameters can be directly used for replacement, thereby increasing the noise estimation accuracy in the scenario where the estimate is insufficient.

[0050] Further, based on the above embodiment, as another implementation mode, the multiple statistical values ​​may include a first mean value corresponding to the diagonal elements in each noise covariance matrix, and each threshold value may further include a third threshold value; in this case, the above step S102 may further include the following steps: If the first mean corresponding to the noise covariance matrix is ​​less than the third threshold value, the diagonal elements of the noise covariance matrix are replaced with noise power, where the noise power is estimated using channel parameters, and / or the non-diagonal elements of the noise covariance matrix are set to 0.

[0051] Specifically, since the diagonal elements of the noise covariance matrix represent the noise situation, if the diagonal elements of the noise covariance matrix are small, that is, , it can be considered that its credibility is low, and the diagonal elements can be replaced by the noise power estimated by the channel parameters, or the non-diagonal elements of the noise covariance matrix can be set to 0; where, Indicates the third threshold.

[0052] As an implementation manner, the noise power may be a noise power value of each antenna directly calculated using a packet data protocol (PDP), or may be a reference additive white noise energy obtained based on other reference signals or other methods.

[0053] In the above scheme, since the diagonal elements of the noise covariance matrix represent the noise and possible interference, they will not be less than the noise power estimated by the channel parameter estimation module in theory. Therefore, if the diagonal elements of the noise covariance matrix are small, it can be considered that their credibility is low and can be replaced by the noise power estimated by the channel parameters, thereby improving the accuracy of the noise covariance matrix.

[0054] Further, based on the above embodiment, as another implementation mode, each threshold value may further include a fourth threshold value, and the fourth threshold value is greater than the second threshold value; in this case, the above step S102 may further include the following steps: If the number of matrices corresponding to the noise covariance matrix is ​​less than the fourth threshold value, the non-diagonal elements of the noise covariance matrix are set to 0.

[0055] Specifically, if the noise covariance estimator is insufficient, that is, , we can directly set the off-diagonal elements of the noise covariance matrix to 0; where, represents the fourth threshold value, and .

[0056] Alternatively, since the diagonal elements of the noise covariance matrix represent the noise and possible interference, if the diagonal elements of the noise covariance matrix are small, that is, , it can be considered that the off-diagonal elements should be smaller, thus representing an interference-limited scenario, so the off-diagonal elements of the noise covariance matrix can be set to 0; where, Indicates the third threshold.

[0057] It can be understood that among the following three conditions: the number of matrices corresponding to the noise covariance matrix is ​​less than the fourth threshold value, the mean corresponding to the noise covariance matrix is ​​less than the third threshold value, and the ratio of the square mean corresponding to the noise covariance matrix to the square of the mean is less than the first threshold value, as long as one of the following three conditions is met, the non-diagonal elements of the noise covariance matrix can be set to 0.

[0058] In the above scheme, since the diagonal elements of the noise covariance matrix represent the noise and possible interference, and the non-diagonal elements of the noise covariance matrix represent the possible interference, if the diagonal elements of the noise covariance matrix are small, it can be considered that the current scene is an interference-limited scene, so the non-diagonal elements can be set to 0, thereby improving the accuracy of the noise covariance matrix; when the noise covariance estimate is insufficient, the non-diagonal elements can be directly set to 0, thereby increasing the noise estimation accuracy in the scene with insufficient estimate.

[0059] Further, based on the above embodiment, the first threshold value corresponding to the next statistical duration may be updated according to the following steps: Step 1) calculates a second mean of the square of the mean corresponding to the current statistical duration, and a third mean of the square mean corresponding to the current statistical duration.

[0060] Step 2) updating the first threshold value according to the second mean value and the third mean value corresponding to the current statistical duration.

[0061] Specifically, the second mean value can be expressed by the following formula: ; in, Represents the second mean corresponding to the mean square of multiple noise covariance matrices, Indicates the number of statistical granularities.

[0062] The third mean can be expressed by the following formula: ; in, Represents the third mean corresponding to the square means corresponding to multiple noise covariance matrices.

[0063] In addition, the next statistical duration may indicate a statistical duration after the current statistical duration, or may indicate a statistical duration for calculating the next second statistic.

[0064] In the above scheme, in addition to multiple statistical values ​​of the current statistical duration, multiple statistical values ​​corresponding to the next statistical duration are also introduced. By horizontally increasing the statistics of the covariance matrix, the overall interference situation of the channel can be better determined, thereby adaptively adjusting the noise covariance matrix to improve the accuracy of the noise covariance matrix.

[0065] Further, based on the above embodiment, the step of updating the first threshold value according to the second mean and the third mean corresponding to the current statistical duration may specifically include the following steps: Step 1) Filter the second mean and the third mean corresponding to the current statistical duration, and the second mean and the third mean corresponding to the previous statistical duration to obtain a filtering result, which is used to determine the interference situation of the current statistical duration.

[0066] Step 2) Update the first threshold value according to the filtering result.

[0067] Specifically, in the above step 1), assuming that the filter coefficient is 0.8, the filtering results include: as well as ;in, 0.8× the current statistical duration +0.2×the corresponding time of the previous statistics , 0.8× the current statistical duration +0.2×the corresponding time of the previous statistics It is understandable that the size of the above filter coefficients can be adjusted according to actual conditions.

[0068] In step 2) above, we can calculate and When the ratio is large, it can be considered that the interference of the current statistical duration is large, and when the ratio is small, it can be considered that the interference of the current statistical duration is small; if the interference of the current statistical duration is large, the first threshold value can be increased, otherwise the first threshold value can be reduced.

[0069] In the above scheme, multiple statistical values ​​of the current statistical duration and multiple statistical values ​​corresponding to the previous statistical duration can be filtered to determine the overall interference situation of the channel, so that the noise covariance matrix can be more flexibly and adaptively adjusted to improve the accuracy of the noise covariance matrix.

[0070] Please refer to Figure 2 , Figure 2 A structural block diagram of a noise covariance matrix updating device provided in an embodiment of the present application, the noise covariance matrix updating device 200 includes: an acquisition module 201, used to acquire multiple noise covariance matrices corresponding to a current statistical duration, wherein each noise covariance matrix corresponds to a statistical granularity; a determination module 202, used to determine multiple statistical values ​​for each statistical granularity according to the noise covariance matrix corresponding to the statistical granularity, and update the noise covariance matrix according to the size relationship between the multiple statistical values ​​and each threshold value, wherein the each threshold value includes a first threshold value, and the first threshold value corresponding to the next statistical duration is updated according to the multiple statistical values ​​corresponding to the multiple noise covariance matrices corresponding to the current statistical duration.

[0071] In the above scheme, since the overall interference situation of the channel can be determined based on the noise covariance matrix corresponding to the current statistical duration, multiple statistical values ​​corresponding to the current statistical duration can be determined based on the noise covariance matrix corresponding to the current statistical duration, and the noise covariance matrix can be updated by comparing the size relationship between multiple statistical values ​​and each threshold value. In addition, the first threshold value can also be updated based on the multiple statistical values ​​corresponding to the current statistical duration, and then the noise covariance matrix corresponding to the next statistical duration is updated based on the updated first threshold value. By the updating method of the noise covariance matrix provided by the embodiment of the present application, the noise covariance matrix can be adaptively adjusted, so that the accuracy of the noise covariance matrix can be improved.

[0072] Further, based on the above embodiment, the multiple statistical values ​​include the square mean corresponding to the diagonal elements in the noise covariance matrix and the square mean corresponding to the non-diagonal elements in the noise covariance matrix; the determination module 202 is specifically used to: if the ratio of the square mean corresponding to the noise covariance matrix to the square mean is less than the first threshold value, then the non-diagonal elements of the noise covariance matrix are set to 0.

[0073] In the above scheme, since the diagonal elements of the noise covariance matrix represent noise and possible interference and the non-diagonal elements represent possible interference, if the ratio of the square mean corresponding to the non-diagonal elements to the square of the first mean corresponding to the diagonal elements is small, it can be considered that the interference effect is smaller than the noise effect. Therefore, the non-diagonal elements can be set to 0, thereby improving the accuracy of the noise covariance matrix.

[0074] Further, based on the above embodiment, the multiple statistical values ​​include the number of matrices used to calculate the noise covariance matrix, and the various threshold values ​​also include a second threshold value; the determination module 202 is specifically used for: if the number of matrices corresponding to the noise covariance matrix is ​​less than the second threshold value, then the diagonal elements of the noise covariance matrix are replaced with noise power, wherein the noise power is obtained by using channel parameter estimation.

[0075] In the above scheme, when the noise covariance estimate is insufficient, the noise power estimated by the channel parameters can be directly used for replacement, thereby increasing the noise estimation accuracy in the scenario where the estimate is insufficient.

[0076] In an optional implementation, the multiple statistical values ​​include a first mean corresponding to the diagonal elements in the noise covariance matrix, and the respective threshold values ​​also include a third threshold value; the determination module 202 is specifically used to: if the first mean corresponding to the noise covariance matrix is ​​less than the third threshold value, then replace the diagonal elements of the noise covariance matrix with noise power, wherein the noise power is estimated using channel parameters, and / or, set the non-diagonal elements of the noise covariance matrix to 0.

[0077] In the above scheme, since the diagonal elements of the noise covariance matrix represent the noise and possible interference, they will not be less than the noise power estimated by the channel parameter estimation module in theory. Therefore, if the diagonal elements of the noise covariance matrix are small, it can be considered that their credibility is low and can be replaced by the noise power estimated by the channel parameters, thereby improving the accuracy of the noise covariance matrix.

[0078] Further, based on the above embodiment, the various threshold values ​​also include a fourth threshold value, and the fourth threshold value is greater than the second threshold value; the determination module 202 is specifically used to: if the number of matrices corresponding to the noise covariance matrix is ​​less than the fourth threshold value, then the non-diagonal elements of the noise covariance matrix are set to 0.

[0079] In the above scheme, since the diagonal elements of the noise covariance matrix represent the noise and possible interference, and the non-diagonal elements of the noise covariance matrix represent the possible interference, if the diagonal elements of the noise covariance matrix are small, it can be considered that the current scene is an interference-limited scene, so the non-diagonal elements can be set to 0, thereby improving the accuracy of the noise covariance matrix; when the noise covariance estimate is insufficient, the non-diagonal elements can be directly set to 0, thereby increasing the noise estimation accuracy in the scene with insufficient estimate.

[0080] Further, based on the above embodiment, the noise covariance matrix updating device 200 also includes: an updating module, which is used to update the first threshold value corresponding to the next statistical duration according to the following steps: calculating the second mean of the square of the mean corresponding to the current statistical duration, and the third mean of the square mean corresponding to the current statistical duration; updating the first threshold value according to the second mean and the third mean corresponding to the current statistical duration.

[0081] In the above scheme, in addition to the multiple statistical values ​​corresponding to the current statistical duration, multiple statistical values ​​corresponding to the next statistical duration are also introduced. By horizontally increasing the statistics of the covariance matrix, the overall interference situation of the channel can be better determined, thereby adaptively adjusting the noise covariance matrix to improve the accuracy of the noise covariance matrix.

[0082] Further, based on the above embodiments, the update module is specifically used to: filter the second mean and the third mean corresponding to the current statistical duration, and the second mean and the third mean corresponding to the previous statistical duration to obtain a filtering result, and the filtering result is used to judge the interference situation of the current statistical duration; update the first threshold value according to the filtering result.

[0083] In the above scheme, the multiple statistical values ​​corresponding to the current statistical duration and the multiple statistical values ​​corresponding to the previous statistical duration can be filtered to determine the overall interference situation of the channel, so that the noise covariance matrix can be more flexibly and adaptively adjusted to improve the accuracy of the noise covariance matrix.

[0084] Please refer to Figure 3 , Figure 3A structural block diagram of an electronic device provided in an embodiment of the present application, the electronic device 300 includes: at least one processor 301, at least one communication interface 302, at least one memory 303 and at least one communication bus 304. Among them, the communication bus 304 is used to realize the direct connection and communication of these components, the communication interface 302 is used to communicate signaling or data with other node devices, and the memory 303 stores machine-readable instructions executable by the processor 301. When the electronic device 300 is running, the processor 301 communicates with the memory 303 through the communication bus 304, and the machine-readable instructions are called by the processor 301 to execute the above-mentioned noise covariance matrix update method.

[0085] For example, the processor 301 of the embodiment of the present application reads a computer program from the memory 303 through the communication bus 304 and executes the computer program to implement the following method: obtaining multiple noise covariance matrices corresponding to the current statistical duration, wherein each noise covariance matrix corresponds to a statistical granularity; for each statistical granularity, determining multiple statistical values ​​according to the noise covariance matrix corresponding to the statistical granularity, and updating the noise covariance matrix according to the size relationship between the multiple statistical values ​​and each threshold value, wherein the each threshold value includes a first threshold value, and the first threshold value corresponding to the next statistical duration is updated according to the multiple statistical values ​​corresponding to the multiple noise covariance matrices corresponding to the current statistical duration.

[0086] Among them, the processor 301 includes one or more, which can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 301 can be a general-purpose processor, including a central processing unit (CPU), a micro control unit (MCU), a network processor (NP) or other conventional processors; it can also be a dedicated processor, including a neural network processor (NPU), a graphics processor (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. In addition, when there are multiple processors 301, some of them can be general-purpose processors, and the other part can be dedicated processors.

[0087] The memory 303 includes one or more, which may be, but not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0088] Understandably, Figure 3 The structure shown is for illustration only. The electronic device 300 may also include Figure 3 More or fewer components as shown, or with Figure 3 Different configurations are shown. Figure 3Each component shown in can be implemented by hardware, software or a combination thereof. In the embodiment of the present application, the electronic device 300 can be, but is not limited to, a physical device such as a desktop computer, a laptop computer, a smart phone, a smart wearable device, a vehicle-mounted device, etc., and can also be a virtual device such as a virtual machine. In addition, the electronic device 300 is not necessarily a single device, but can also be a combination of multiple devices, such as a server cluster, etc.

[0089] The embodiment of the present application also provides a computer program product, including a computer program stored on a computer-readable storage medium, the computer program including computer program instructions, when the computer program instructions are executed by the computer, the computer can execute the steps of the method for updating the noise covariance matrix in the above embodiment, for example including: Step S101: obtaining multiple noise covariance matrices corresponding to the current statistical duration, wherein each noise covariance matrix corresponds to a statistical granularity. Step S102: for each statistical granularity, determining multiple statistical values ​​according to the noise covariance matrix corresponding to the statistical granularity, and updating the noise covariance matrix according to the size relationship between the multiple statistical values ​​and each threshold value, wherein each threshold value includes a first threshold value, and the first threshold value corresponding to the next statistical duration is updated according to the multiple statistical values ​​corresponding to the multiple noise covariance matrices corresponding to the current statistical duration.

[0090] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer program instructions. When the computer program instructions are executed by a computer, the computer executes the method for updating the noise covariance matrix described in the aforementioned method embodiment.

[0091] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0092] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0094] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, 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, including a number of instructions to enable a computer device (which can be a personal computer, server, or 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: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0095] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0096] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for updating a noise covariance matrix, characterized in that: include: Obtain multiple noise covariance matrices corresponding to the current statistical duration, where each noise covariance matrix corresponds to a statistical granularity; For each statistical granularity, multiple statistical values ​​are determined according to the noise covariance matrix corresponding to the statistical granularity, and the noise covariance matrix is ​​updated according to the size relationship between the multiple statistical values ​​and the various threshold values, wherein the various threshold values ​​include a first threshold value, and the first threshold value corresponding to the next statistical duration is updated according to the multiple statistical values ​​corresponding to the multiple noise covariance matrices corresponding to the current statistical duration.

2. The updating method of the noise covariance matrix according to claim 1, characterized in that: The multiple statistical values ​​include the mean squares corresponding to the diagonal elements in the noise covariance matrix and the square means corresponding to the off-diagonal elements in the noise covariance matrix; The updating of the noise covariance matrix according to the magnitude relationship between the multiple statistical values ​​and the respective threshold values ​​comprises: If the ratio of the square mean corresponding to the noise covariance matrix to the square of the mean is less than the first threshold value, the non-diagonal elements of the noise covariance matrix are set to 0.

3. The updating method of the noise covariance matrix according to claim 1, characterized in that, The multiple statistical values ​​include the number of matrices used when calculating the noise covariance matrix, and the respective threshold values ​​also include a second threshold value; The updating of the noise covariance matrix according to the magnitude relationship between the multiple statistical values ​​and the respective threshold values ​​comprises: If the number of matrices corresponding to the noise covariance matrix is ​​less than the second threshold value, the diagonal elements of the noise covariance matrix are replaced with noise power, wherein the noise power is estimated using channel parameters.

4. The updating method of the noise covariance matrix according to claim 1, characterized in that: The multiple statistical values ​​include a first mean value corresponding to the diagonal elements in the noise covariance matrix, and the respective threshold values ​​also include a third threshold value; The updating of the noise covariance matrix according to the magnitude relationship between the multiple statistical values ​​and the respective threshold values ​​comprises: If the first mean corresponding to the noise covariance matrix is ​​less than the third threshold value, the diagonal elements of the noise covariance matrix are replaced with noise power, wherein the noise power is estimated using channel parameters, and / or the non-diagonal elements of the noise covariance matrix are set to 0.

5. The updating method of the noise covariance matrix according to claim 3, characterized in that: The threshold values ​​further include a fourth threshold value, and the fourth threshold value is greater than the second threshold value; The updating of the noise covariance matrix according to the magnitude relationship between the multiple statistical values ​​and the respective threshold values ​​also includes: If the number of matrices corresponding to the noise covariance matrix is ​​less than the fourth threshold value, the non-diagonal elements of the noise covariance matrix are set to 0.

6. The updating method of the noise covariance matrix according to claim 2, characterized in that: The first threshold value corresponding to the next statistical duration is updated according to the following steps: Calculate a second mean of the squares of the means corresponding to the current statistical duration, and a third mean of the square means corresponding to the current statistical duration; The first threshold value is updated according to the second mean value and the third mean value corresponding to the current statistical duration.

7. The method for updating the noise covariance matrix according to claim 6, wherein: The updating of the first threshold value according to the second mean and the third mean corresponding to the current statistical duration includes: Filtering the second mean and the third mean corresponding to the current statistical duration, and the second mean and the third mean corresponding to the previous statistical duration, to obtain a filtering result, wherein the filtering result is used to determine the interference situation of the current statistical duration; The first threshold value is updated according to the filtering result.

8. A computer program product, characterized in that The method comprises computer program instructions, which, when read and executed by a processor, executes the method for updating the noise covariance matrix according to any one of claims 1 to 7.

9. An electronic device, characterized in that: include: processor, memory, and bus; The processor and the memory communicate with each other via the bus; The memory stores computer program instructions that can be executed by the processor, and the processor calls the computer program instructions to execute the method for updating the noise covariance matrix as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a computer, the computer is caused to execute the method for updating the noise covariance matrix according to any one of claims 1 to 7.

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