Signal detection method and device for sparse channel matrix

Abstract

The invention belongs to the related technical field of wireless communication, and particularly relates to a signal detection method and device for a sparse channel matrix, and the method comprises the steps: setting an element smaller than a threshold value in an original equivalent channel matrix corresponding to a current received signal to 0 based on the preset threshold value, and obtaining a sparse equivalent channel matrix; wherein the current received signal is a sparse signal; constructing a lower triangular matrix through iterative optimization, determining an optimal lower triangular matrix by taking the inverse that a matrix obtained through Keraski decomposition is close to a sparse equivalent channel matrix as a target, and taking a Keraski decomposition result corresponding to the optimal lower triangular matrix as an approximate covariance matrix in approximate posterior probability distribution of the current received signal; and iteratively calculating a variance and a mean value of the approximate posterior probability distribution of the current receiving signal based on the approximate covariance matrix, and selecting a convergence mean value as the optimal estimation of a transmitting signal corresponding to the current receiving signal. According to the method, matrix inversion is avoided, and the calculation complexity is reduced while the precision is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field related to wireless communications, and more specifically, relates to a signal detection method and device for a sparse channel matrix. Background Art

[0002] The sixth generation of wireless communication systems needs to carry larger amounts of data to meet application scenarios with higher bandwidth requirements, such as telemedicine, ultra-high-definition video, and augmented reality / virtual reality. However, as the amount of data increases, the signal detection problem in wireless communication systems becomes increasingly complex. In traditional signal detection algorithms, detection algorithms such as minimum mean square error (MMSE) and expected propagation (EP) are often used. However, most detection algorithms require matrix inversion operations, and when the dimension of the signal matrix increases, the complexity of the algorithm increases rapidly at a cubic order of magnitude. Therefore, it is urgent to explore low-complexity, high-precision signal detection solutions. Summary of the invention

[0003] In view of the above defects or improvement needs of the prior art, the present invention provides a signal detection method and device for a sparse channel matrix, which aims to ensure the accuracy of signal detection while reducing the complexity of signal detection.

[0004] To achieve the above object, according to one aspect of the present invention, a signal detection method for a sparse channel matrix is ​​provided, comprising:

[0005] Based on a preset threshold, elements in the original equivalent channel matrix corresponding to the current received signal that are smaller than the threshold are set to 0 to obtain a sparse equivalent channel matrix; wherein the current received signal is a sparse signal;

[0006] Iteratively optimize and construct a lower triangular matrix, with the goal of making the matrix obtained by Cholesky decomposition close to the inverse of the sparse equivalent channel matrix, determine the optimal lower triangular matrix, and use the Cholesky decomposition result corresponding to the optimal lower triangular matrix as the approximate covariance matrix in the approximate posterior probability distribution of the current received signal;

[0007] The variance and mean of the approximate posterior probability distribution of the current received signal are iteratively calculated based on the approximate covariance matrix, and the converged mean is selected as the optimal estimate of the transmitted signal corresponding to the current received signal.

[0008] Furthermore, the sparse equivalent channel matrix is ​​determined in the following specific manner:

[0009] The original equivalent channel matrix corresponding to the current received signal is normalized by using Jacobi scaling. Based on a preset threshold, the elements in the normalized result matrix that are smaller than the threshold and the corresponding elements in the original equivalent channel matrix are set to 0 to obtain a sparse equivalent channel matrix.

[0010] Further, the sparse signal is generated by:

[0011] According to the index mapping relationship, the subcarrier corresponding to the bit in the signal to be transmitted is determined and activated, and a sparse signal is obtained and transmitted by constellation mapping the bit and using the activated subcarrier to carry the constellation mapped signal;

[0012] After obtaining the optimal estimate, the method further includes:

[0013] The activated subcarriers are determined based on the matrix positions of the non-zero elements in the optimal estimation, and the transmitted bits are restored based on the activated subcarriers and the index mapping relationship to complete the original signal detection.

[0014] According to another aspect of the present invention, a signal detection device for a sparse channel matrix is ​​provided, which implements signal detection by using the signal detection method as described above.

[0015] According to another aspect of the present invention, a signal communication system of a sparse channel matrix is ​​provided, comprising: a signal transmitting device and a signal receiving device; wherein the signal transmitting device is used to transmit an original signal, and the signal receiving device is used to implement signal detection based on the original signal using the signal detection method as described above.

[0016] According to another aspect of the present invention, a signal communication system for a sparse channel matrix is ​​provided, comprising: a signal transmitting device and a signal receiving device; wherein the signal transmitting device is used to determine and activate the subcarrier corresponding to the bit in the original signal to be transmitted according to the index mapping relationship, and obtain and transmit a sparse signal by constellation mapping the bit and using the activated subcarrier to carry the constellation mapped signal;

[0017] The signal receiving device is used to determine the activated subcarriers based on the matrix positions of non-zero elements in the optimal estimate after obtaining the optimal estimate of the sparse signal using the signal detection method as described above, and to restore the transmitted bits based on the activated subcarriers and the index mapping relationship to complete the original signal detection.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, which includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the method steps executed by the signal receiving device as described above or the method steps executed by the transmitting end as described above.

[0019] In general, compared with the prior art, the technical solution conceived by the present invention has the following beneficial effects:

[0020] 1. The present invention proposes a signal detection method for a sparse channel matrix. Considering that there are noise and interference signals from other signal sources in the original equivalent channel matrix corresponding to the current received signal, on the one hand, the receiving end needs to regard them as valid information for calculation when performing signal detection, which increases the amount of calculation; on the other hand, these noise and interference signals are mixed in the original transmitted signal, which will interfere with the receiver's detection of the original transmitted signal, resulting in a decrease in detection accuracy. Therefore, based on a preset threshold, the method of the present invention sets the elements in the original equivalent channel matrix corresponding to the current received signal that are less than the threshold to 0, which essentially sets the noise and interference signals to 0, while reducing unnecessary calculations of the receiver and reducing interference to the original transmitted signal, thereby reducing complexity and improving detection accuracy. Furthermore, the receiver end involves an equivalent channel matrix inversion step in the signal detection step of the received signal, and the complexity is that the matrix dimension is cubic. The lower triangular matrix is ​​constructed by iterative optimization, and the matrix obtained by Cholesky decomposition is close to the inverse of the sparse equivalent channel matrix as the goal, the optimal lower triangular matrix is ​​determined, and the Cholesky decomposition result corresponding to the optimal lower triangular matrix is ​​used as the approximate covariance matrix in the approximate posterior probability distribution of the current received signal, avoiding the original matrix inversion step, which can significantly reduce the complexity of the receiver to perform signal detection. Furthermore, when performing signal detection, the traditional receiver regards the approximate posterior probability distribution of the received signal as a Gaussian distribution, which deviates from the actual received signal probability distribution, resulting in low detection accuracy. Based on the approximate covariance matrix, the variance and mean of the approximate posterior probability distribution of the current received signal are iteratively calculated, and the probability distribution of the actual received signal is iteratively approximated. When the set probability distribution converges, the converged mean is used as the optimal estimate of the transmitted signal corresponding to the current received signal, thereby improving the accuracy of the detected signal. Therefore, the present invention can not only reduce the complexity of signal detection, but also improve the accuracy of signal detection, and can replace the matrix inversion process involved in the traditional signal detection algorithm.

[0021] 2. The present invention also proposes to first perform matrix sparse processing on the signal to be transmitted at the transmitting end before transmitting. Specifically, the original dense signal matrix is ​​converted into a sparse signal matrix through index and constellation mapping, and the information with low correlation in the matrix can be removed to increase the sparsity of the matrix. Finally, the covariance matrix of the approximate posterior probability of the received signal is solved based on the sparse matrix, replacing the step of inverting the covariance matrix in the traditional algorithm. During the signal detection process, the receiving end restores the estimated transmitted signal after obtaining it, thereby realizing high-precision and low-complexity detection of both non-sparse original transmitted signals and sparse original transmitted signals, and has a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of a signal detection method for a sparse channel matrix provided by an embodiment of the present invention;

[0023] Figure 2 is a model diagram of a signal detection device for a sparse channel matrix provided by an embodiment of the present invention;

[0024] Figure 3 A simulation diagram comparing the performance of different detection methods provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0026] Embodiment 1

[0027] A signal detection method for a sparse channel matrix, such as Figure 1 As shown, including:

[0028] Based on a preset threshold, elements in the original equivalent channel matrix corresponding to the current received signal that are smaller than the threshold are set to 0 to obtain a sparse equivalent channel matrix; wherein the current received signal is a sparse signal;

[0029] Iteratively optimize and construct a lower triangular matrix, with the goal of making the matrix obtained by Cholesky decomposition close to the inverse of the sparse equivalent channel matrix, determine the optimal lower triangular matrix, and use the Cholesky decomposition result corresponding to the optimal lower triangular matrix as the approximate covariance matrix in the approximate posterior probability distribution of the current received signal;

[0030] The variance and mean of the approximate posterior probability distribution of the current received signal are iteratively calculated based on the approximate covariance matrix, and the converged mean is selected as the optimal estimate of the transmitted signal corresponding to the current received signal.

[0031] This method uses a variable sparse factor threshold, which can dynamically modify the matrix sparsity according to the actual data volume and processor computing performance, thereby accelerating the convergence of the algorithm.

[0032] In addition, the lower triangular matrix L can be constructed by iterative optimization by minimizing the Karpolin condition number, with the goal of making the matrix obtained by Cholesky decomposition close to the inverse of the sparse equivalent channel matrix, and determining the optimal lower triangular matrix L opt , the optimal lower triangular matrix L opt The corresponding Cholesky decomposition result is used as the covariance matrix C in the approximate posterior probability distribution of the received signal corresponding to the sparse signal. This method constructs the lower triangular matrix based on the Karpolin condition number iteration. The accuracy of the lower triangular matrix increases with the number of iterations. The number of iterations can be adjusted according to the actual situation to achieve a balance between detection accuracy and system complexity.

[0033] As a specific implementation, for example, the processing steps include:

[0034] Construct the inverse matrix; for the equivalent channel matrix H′ after sparseness eff , construct the inverse matrix D, the construction method is: noise variance σ n -2 Multiply by the sparse equivalent channel matrix H′ eff After the Hermitian transpose, add the symbol energy E s The reciprocal of .

[0035] Initialization: Set the identity matrix I as the initial lower triangular matrix L of the optimal lower triangular matrix, and calculate the Karpolin matrix K based on the initial lower triangular matrix L and the inverse matrix D. The calculation method is: the Hermitian transpose of the initial lower triangular matrix L multiplied by the inverse matrix D, and then multiplied by the initial lower triangular matrix L.

[0036] Calculate the Karboline condition number; Calculate the initial Karboline condition number K of the Karboline matrix K (0) It is used to measure the estimation performance of the lower triangular matrix L, and is calculated as follows: the rank of the Kalpolin matrix K divided by the total number of subcarriers N, and then divided by the 1 / Nth power of the determinant of the Kalpolin matrix K;

[0037] Lower triangular matrix update; iteratively update the column vector l of the lower triangular matrix L, from the non-zero element index set of l Select the index vector e from k Add to the corresponding column vector l to obtain the updated column vector l′, and recalculate the new Capolin condition number K (t) , if the new Karboline condition number K (t) Less than the original Kapoor condition number K (t-1) , replace the original column vector l of the lower triangular matrix L with the new column vector l′, and continue iterating until the upper limit of the number of iterations T is reached L , output the optimal lower triangular matrix L opt .

[0038] Approximate covariance matrix; Based on the Cholesky decomposition, the approximate covariance matrix of the approximate posterior probability distribution of the received signal is calculated The calculation method is the optimal lower triangular matrix L opt Multiply by the optimal lower triangular matrix L opt The Hermitian transpose of .

[0039] Regarding the determination of the optimal estimate of the received signal, in particular, the processing steps include:

[0040] Approximate distribution update; set the initial value C of the variance of the prior distribution q(x) of the transmitted signal (0) =I / E S , the mean initial value u(0) =I, in the tth iteration, calculate the covariance C of the approximate distribution (t) and mean u (t) , where the covariance C of the approximate distribution is (t) By approximate covariance Approximately, the mean u of the approximate distribution (t) By the sparse equivalent channel matrix H′ eff The Hermitian transpose of is multiplied by the received signal y and then multiplied by the inverse of the noise variance Add the covariance C (t-1) The diagonal matrix of (t) get.

[0041] Estimated signal output; when the absolute value of the mean u change between the previous and next two iterations is less than a set constant ε, the iteration is terminated and the estimated signal is output

[0042] As a preferred implementation, the sparse equivalent channel matrix is ​​determined by the following specific method:

[0043] The original equivalent channel matrix corresponding to the current received signal is normalized by using Jacobi scaling. Based on a preset threshold, the elements in the normalized result matrix that are smaller than the threshold and the corresponding elements in the original equivalent channel matrix are set to 0 to obtain a sparse equivalent channel matrix.

[0044] Use Jacobian scaling to convert the equivalent channel matrix H eff The diagonal elements of H are normalized. When the element in the normalized matrix is ​​less than the set sparse factor threshold, H eff The elements at the corresponding positions in are set to zero. At this time, the original matrix is ​​divided into multiple independent sub-matrices, increasing H eff The sparsity of .

[0045] Specifically, take H eff The absolute values ​​of the diagonal elements of are placed in the diagonal matrix J in turn, and the normalized matrix The calculation method is: the inverse of the square root of the diagonal matrix J multiplied by the equivalent channel matrix H eff Then multiply it by the inverse of the square root of the diagonal matrix J. If the normalized matrix Elements on If the absolute value is less than the sparse factor threshold ξ, the corresponding element H on the equivalent channel matrix eff (i,j) is set to 0, and the sparse equivalent channel matrix H′ is obtained eff .

[0046] As a preferred implementation, the sparse signal is generated in the following manner:

[0047] According to the index mapping relationship, the subcarrier corresponding to the bit in the signal to be transmitted is determined and activated, and a sparse signal is obtained and transmitted by constellation mapping the bit and using the activated subcarrier to carry the constellation mapped signal;

[0048] After obtaining the above optimal estimate, the method further includes:

[0049] The activated subcarriers are determined based on the matrix positions of the non-zero elements in the optimal estimation, and the transmitted bits are restored based on the activated subcarriers and the index mapping relationship to complete the original signal detection.

[0050] The sparse signal has modulation symbols only on the activated subcarriers, and other inactive subcarriers only send zero symbols, including:

[0051] The bit sequence to be transmitted with a length of m is divided into G groups, each group has p = m / G bits, and the p bits are divided into two groups p1 and p2, where p1 is used to activate subcarrier selection and p2 is used for constellation mapping. After activation and constellation mapping, the length of each group of symbols is L.

[0052] Each group only activates K subcarriers, and the other subcarriers only send 0 symbols.

[0053] In a specific implementation example, at the transmitting end, a transmission bit sequence of length m is divided into G sub-blocks, each sub-block is index mapped and constellation mapped, and then modulated to obtain a modulation symbol of length N×1.

[0054] Specifically, the transmission bit sequence of length m is divided into G sub-blocks, the bit length of each sub-block is p = m / G, and each group of bits is mapped and modulated to a length of N. g The total number of subcarriers in the system is N, and there are N g =N / G. p bits are divided into two parts. The first part bits are used to activate K (K<N) subcarriers from N available subcarriers, and the selected index is Γ g ={i g,1 ,...,i g,k ,…,i g,K}, where i g,k ∈[1,...,N], g={1,...,G}, k={1,...,K}. The second part contains p2=Klog2(M) bits for constellation mapping into K M-order constellation symbols, where the constellation symbol is a g =[a g,1 ,...,a g,k ,…,a g,K ],in is a constellation symbol set. After obtaining the activated subcarrier information and constellation symbols of all subblocks, modulation is performed to obtain a modulation symbol [x1,...,x g ,…,x G ], where the modulation symbol x of the gth group g for

[0055] At the receiving end, the likelihood probability and log-likelihood ratio of each detected signal vector and the vector in the index mapping relationship are calculated to obtain the transmitted bit information, making it easy to combine the algorithm with channel coding to further increase the detection accuracy. Group, calculate the Gaussian likelihood probability of each group of sub-vectors and the index modulation vector set, and calculate the logarithmic likelihood ratio L of each bit of each group of index modulation vectors a , and merge the inverse-mapped symbols of each group of indexes with the inverse-mapped symbols of the constellation to restore the transmitted bits and complete the original signal detection.

[0056] Specifically, as an example, the processing steps include:

[0057] Gaussian likelihood probability calculation; the optimal estimate signal Divided into G groups, each group's sub-vector The length is L, according to the index mapping vector s in the vector set i Calculate the Gaussian likelihood probability p(x) corresponding to each vector in the index mapping vector set g =s i ), the calculation method is: each group of sub-vectors With vector s i The negative of the second moment of the difference, divided by the noise variance Then take the exponent and divide it by pi and the noise variance The product of .

[0058] Log-likelihood ratio calculation; divide the index mapping vector set into two equal-sized subsets X 1 and X 0 , where X 1 is the vector set whose judgment is 1 under the current bit, X 0 is a set of vectors judged to be 0, and then according to the Gaussian likelihood probability p(x g =s i ) Calculate the mth bit d in the gth group index mapping vector g,m The log-likelihood ratio L a (d g,m ), the calculation method is: the Gaussian likelihood probability set of the vector whose judgment result is 1 The Gaussian likelihood probability set of the vector with a decision result of 0 The natural logarithm of the ratio is taken. Finally, the transmission bit sequences of each group are restored according to the positive and negative signs of the log-likelihood ratio, and the transmission bit sequences of each group are combined and output to restore the transmission bits and complete the original signal detection.

[0059] The signal detection method of this embodiment performs sparse processing on the equivalent channel matrix corresponding to the sparse signal, and obtains an approximate solution for the covariance matrix of the approximate posterior probability distribution of the received signal. Specifically, for the transmitted bits, index mapping and constellation mapping are performed after grouping to obtain a sparse signal. For the equivalent channel matrix corresponding to the sparse signal, according to a preset threshold, the elements in the equivalent channel matrix that are less than the threshold are set to 0, and the noise and interference elements in the original equivalent channel matrix are removed. Construct an initial lower triangular matrix, and iteratively update the column vector of the lower triangular matrix according to the minimized Capolin condition number to obtain the optimal lower triangular matrix, and then use Cholesky decomposition to solve the approximate covariance matrix of the approximate posterior probability distribution of the received signal, use the expected propagation algorithm to detect the received signal, and determine the activated subcarrier based on the matrix position of the non-zero element in the detected received signal, and obtain the original transmitted bit based on the activated subcarrier and the index mapping relationship. This method uses index mapping and Jacobi scaling methods to increase the sparsity of the equivalent channel matrix corresponding to the received signal, and then obtains an approximate solution to the covariance matrix of the approximate posterior probability distribution of the received signal, avoiding the matrix inversion step, reducing the complexity of the signal detection algorithm, and improving the accuracy of signal detection.

[0060] In general, when the approximate covariance matrix is ​​inverted, the equivalent channel matrix corresponding to the received signal is sparsely processed, and then the matrix obtained by Cholesky decomposition is close to the inverse of the sparse equivalent channel matrix, and the optimal lower triangular matrix is ​​determined. The Cholesky decomposition result corresponding to the optimal lower triangular matrix is ​​used as the approximate covariance matrix in the approximate posterior probability distribution of the received signal, which can effectively avoid the matrix inversion step in the expected propagation algorithm and reduce the complexity of the algorithm. The specific framework of the whole method can be seen Figure 2 .

[0061] Embodiment 2

[0062] A signal detection device for a sparse channel matrix adopts the signal detection method as described above to realize signal detection.

[0063] The relevant technical solution is the same as that in Embodiment 1 and will not be described in detail here.

[0064] Embodiment 3

[0065] A signal communication system of a sparse channel matrix includes: a signal transmitting device and a signal receiving device; wherein the signal transmitting device is used to transmit an original signal, and the signal receiving device is used to implement signal detection based on the original signal using the signal detection method as described above.

[0066] The relevant technical solution is the same as that in Embodiment 1 and will not be described in detail here.

[0067] Embodiment 4

[0068] A signal communication system for a sparse channel matrix, comprising: a signal transmitting device and a signal receiving device; wherein the signal transmitting device is used to determine and activate the subcarrier corresponding to the bit in the original signal to be transmitted according to the index mapping relationship, and obtain and transmit a sparse signal by constellation mapping the bit and using the activated subcarrier to carry the constellation mapped signal;

[0069] The signal receiving device is used to determine the activated subcarriers based on the matrix positions of non-zero elements in the optimal estimate after obtaining the optimal estimate of the sparse signal using the signal detection method as described above, and to restore the transmitted bits based on the activated subcarriers and the index mapping relationship to complete the original signal detection.

[0070] The relevant technical solution is the same as that in Embodiment 1 and will not be described in detail here.

[0071] In order to illustrate the effectiveness of the present invention, the following example verification is given.

[0072] Figure 3 The left and right figures in the figure are respectively the bit error rate performance and complexity performance simulation diagrams when the method in the embodiment of the present invention processes the received signal. Figure 3 In the left figure, the horizontal axis represents the signal-to-noise ratio in dB, and the vertical axis represents the bit error rate. Figure 3 In the right figure, the horizontal axis represents different detection methods, and the vertical axis represents the number of runs required for each method. Figure 3 The left figure in the figure shows the relationship between the bit error rate and signal-to-noise ratio of the detection method proposed by the present invention and the traditional expected propagation detection method under different sparse factor thresholds and the number of iterations of the constructed lower triangular matrix. The parameters in the detection method proposed by the present invention are set to (ξ, T L )=(0.01,5),(ξ,T L )=(0.05,10), where ξ represents the sparse factor threshold, T L Represents the number of iterations to construct the lower triangular matrix.

[0073] Figure 3 The right figure in shows the complexity of the detection method proposed by the present invention and the traditional expectation propagation algorithm. Figure 3 In the left and right figures, the parameters are: (N, G, K, E s )=(64,16,2,1), where N represents the number of subcarriers, G represents the number of index modulation groups, K represents the number of activation indexes, and E sRepresents the energy of each modulation symbol. Experiments show that the complexity of the present invention can be reduced by 60% compared with the traditional expectation propagation algorithm, while the detection accuracy of the two is close.

[0074] Embodiment 5

[0075] A computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the method steps executed by the signal receiving device as described above or the method steps executed by the transmitting end as described above.

[0076] Specifically, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0077] The relevant technical solutions are the same as above and will not be described again here.

[0078] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A signal detection method for a sparse channel matrix, characterized in that: include: Based on a preset threshold, elements in the original equivalent channel matrix corresponding to the current received signal that are smaller than the threshold are set to 0 to obtain a sparse equivalent channel matrix; wherein the current received signal is a sparse signal; Iteratively optimize and construct a lower triangular matrix, with the goal of making the matrix obtained by Cholesky decomposition close to the inverse of the sparse equivalent channel matrix, determine the optimal lower triangular matrix, and use the Cholesky decomposition result corresponding to the optimal lower triangular matrix as the approximate covariance matrix in the approximate posterior probability distribution of the current received signal; The variance and mean of the approximate posterior probability distribution of the current received signal are iteratively calculated based on the approximate covariance matrix, and the converged mean is selected as the optimal estimate of the transmitted signal corresponding to the current received signal.

2. The signal detection method according to claim 1, characterized in that: The sparse equivalent channel matrix is ​​determined in the following specific way: The original equivalent channel matrix corresponding to the current received signal is normalized by using Jacobi scaling; Based on a preset threshold, the elements in the normalized result matrix that are smaller than the threshold and the corresponding elements in the original equivalent channel matrix are set to 0, so as to obtain a sparse equivalent channel matrix.

3. The signal detection method according to claim 1 or 2, characterized in that: The sparse signal is generated by: According to the index mapping relationship, the subcarrier corresponding to the bit in the signal to be transmitted is determined and activated, and a sparse signal is obtained and transmitted by constellation mapping the bit and using the activated subcarrier to carry the constellation mapped signal; After obtaining the optimal estimate, the method further includes: The activated subcarriers are determined based on the matrix positions of the non-zero elements in the optimal estimation, and the transmitted bits are restored based on the activated subcarriers and the index mapping relationship to complete the original signal detection.

4. A signal detection device for a sparse channel matrix, characterized in that: Signal detection is achieved by adopting the signal detection method as described in any one of claims 1 to 3.

5. A signal communication system with a sparse channel matrix, characterized in that: include: A signal transmitting device and a signal receiving device; wherein the signal transmitting device is used to transmit an original signal, and the signal receiving device is used to implement signal detection based on the original signal using the signal detection method as described in claim 1 or 2.

6. A signal communication system with a sparse channel matrix, characterized in that: include: A signal transmitting device and a signal receiving device; wherein the signal transmitting device is used to determine and activate the subcarrier corresponding to the bit in the original signal to be transmitted according to the index mapping relationship, and obtain and transmit a sparse signal by constellation mapping the bit and using the activated subcarrier to carry the constellation mapped signal; The signal receiving device is used to determine the activated subcarriers based on the matrix positions of non-zero elements in the optimal estimate after obtaining the optimal estimate of the sparse signal using the signal detection method as described in claim 1 or 2, and to restore the transmitted bits based on the activated subcarriers and index mapping relationship to complete the original signal detection.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed by the processor, the device where the storage medium is located is controlled to execute the method steps executed by the signal receiving device as described in any one of claims 1 to 3 or the method steps executed by the transmitting end as described in claim 3.

Images