A signal detection method and device for sparse channel matrix

Abstract

This invention belongs to the field of wireless communication technology, specifically relating to a signal detection method and device for a sparse channel matrix. The method includes: setting elements smaller than a preset threshold in the original equivalent channel matrix corresponding to the currently received signal to 0, resulting in a sparse equivalent channel matrix; wherein the currently received signal is a sparse signal; iteratively optimizing the construction of a lower triangular matrix, aiming to make the matrix obtained by Kolesky decomposition approximate the inverse of the sparse equivalent channel matrix, determining the optimal lower triangular matrix, and using the Kolesky decomposition result corresponding to the optimal lower triangular matrix as the approximate covariance matrix in the approximate posterior probability distribution of the currently received signal; iteratively calculating the variance and mean of the approximate posterior probability distribution of the currently received signal based on the approximate covariance matrix, and selecting the convergent mean as the optimal estimate of the transmitted signal corresponding to the currently received signal. This invention avoids matrix inversion, reducing computational complexity while maintaining accuracy.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and more specifically, relates to a signal detection method and device for a sparse channel matrix. Background Technology

[0002] Sixth-generation wireless communication systems need to handle larger data volumes and meet the higher bandwidth requirements of applications such as telemedicine, ultra-high-definition video, and augmented reality / virtual reality. However, with the increase in data volume, the signal detection problem in wireless communication systems becomes increasingly complex. Traditional signal detection algorithms often employ methods such as minimum mean square error (MMSE) and expectation propagation (EP). However, most of these algorithms require matrix inversion operations, and as the dimension of the signal matrix increases, the complexity of the algorithm rises rapidly by an order of cube. Therefore, there is an urgent need to explore low-complexity, high-precision signal detection schemes. Summary of the Invention

[0003] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a signal detection method and device with sparse channel matrix, the purpose of which is to reduce the signal detection complexity while ensuring the signal detection accuracy.

[0004] To achieve the above objectives, 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 currently received signal that are less than the threshold are set to 0, resulting in a sparse equivalent channel matrix; where the currently received signal is a sparse signal.

[0006] The lower triangular matrix is ​​constructed through iterative optimization. The goal is to make the matrix obtained by the Kolesky decomposition close to the inverse of the sparse equivalent channel matrix. The optimal lower triangular matrix is ​​determined, and the Kolesky 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.

[0007] Based on the approximate covariance matrix, the variance and mean of the approximate posterior probability distribution of the current received signal are calculated iteratively, and the convergent 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 way:

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

[0010] Furthermore, the sparse signal is generated in the following manner:

[0011] Based on the index mapping relationship, the subcarrier corresponding to the bit in the signal to be transmitted is determined and activated. By performing constellation mapping on the bit and using the activated subcarrier to carry the constellation-mapped signal, a sparse signal is obtained and transmitted.

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

[0013] The active subcarrier is determined based on the matrix position of the non-zero elements in the optimal estimate. Based on the active subcarrier and the index mapping relationship, the transmitted bits are recovered, and the original signal detection is completed.

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

[0015] According to another aspect of the present invention, a signal communication system with 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 perform signal detection based on the original signal using the signal detection method described above.

[0016] According to another aspect of the present invention, a signal communication system with a sparse channel matrix is ​​provided, comprising: a signal transmitting device and a signal receiving device; wherein, the signal transmitting device is configured to determine and activate the subcarriers corresponding to bits in the original signal to be transmitted according to an index mapping relationship, and obtain a sparse signal by performing constellation mapping on the bits and using the activated subcarriers to carry the constellation-mapped signal and transmitting it;

[0017] The signal receiving device is configured to, after obtaining the optimal estimate of the sparse signal using the signal detection method described above, determine the active subcarrier based on the matrix position of the non-zero elements in the optimal estimate, and recover the transmitted bits based on the active subcarrier 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, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed by a processor, it controls the device where the storage medium is located to perform method steps performed by a signal receiving device as described above or method steps performed by a transmitting end as described above.

[0019] In summary, compared with the prior art, the technical solutions conceived by this invention have the following main advantages:

[0020] 1. This invention proposes a signal detection method using a sparse channel matrix. Considering the presence of noise and interference signals from other signal sources in the original equivalent channel matrix corresponding to the currently received signal, the receiver needs to treat these as valid information for calculation, increasing computational complexity. Furthermore, these noise and interference signals, mixed in with the original transmitted signal, interfere with the receiver's detection of the original transmitted signal, leading to a decrease in detection accuracy. Therefore, this invention's method, based on a preset threshold, sets elements in the original equivalent channel matrix corresponding to the currently received signal that are less than the threshold to 0. Essentially, this sets noise and interference signals to 0, reducing unnecessary calculations by the receiver while minimizing interference to the original transmitted signal, thus reducing complexity and improving detection accuracy. Further, the signal detection step at the receiver involves inverting the equivalent channel matrix, which has a complexity of cubic matrix dimensions. By iteratively optimizing the construction of a lower triangular matrix, aiming to make the matrix obtained from the Kolesky decomposition approximate the inverse of the sparse equivalent channel matrix, the optimal lower triangular matrix is ​​determined. The Kolesky 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 and significantly reducing the complexity of signal detection by the receiver. Furthermore, traditional receivers treat the approximate posterior probability distribution of the received signal as a Gaussian distribution when performing signal detection, 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, iteratively approximating the probability distribution of the actual received signal. 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 signal detection. Therefore, this invention can both reduce the complexity of signal detection and improve signal detection accuracy, and can replace the matrix inversion process involved in traditional signal detection algorithms.

[0021] 2. This invention also proposes to first perform matrix sparsification processing on the signal to be transmitted at the transmitting end before transmission. Specifically, the original dense signal matrix is ​​converted into a sparse signal matrix through indexing and constellation mapping, and information with low correlation in the matrix is ​​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 signal detection at the receiving end, the estimated transmitted signal is restored, achieving high-precision and low-complexity detection regardless of whether the original transmitted signal is non-sparse or sparse, with a wide range of applications. Attached Figure Description

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

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

[0024] Figure 3 The simulation diagram shows the performance comparison of different detection methods provided in the embodiments of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0026] Example 1

[0027] A signal detection method with a sparse channel matrix, such as Figure 1 As shown, it includes:

[0028] Based on a preset threshold, elements in the original equivalent channel matrix corresponding to the currently received signal that are less than the threshold are set to 0, resulting in a sparse equivalent channel matrix; where the currently received signal is a sparse signal.

[0029] The lower triangular matrix is ​​constructed through iterative optimization. The goal is to make the matrix obtained by the Kolesky decomposition close to the inverse of the sparse equivalent channel matrix. The optimal lower triangular matrix is ​​determined, and the Kolesky 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.

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

[0031] This method employs a variable sparsity factor threshold, which can dynamically modify the matrix sparsity based on the actual data volume and processor performance, thereby accelerating the algorithm's convergence speed.

[0032] Alternatively, the lower triangular matrix L can be constructed through iterative optimization by minimizing the Kapolin condition number. The goal is to determine the optimal lower triangular matrix L by making the matrix obtained from the Kolesky decomposition approximate the inverse of the sparse equivalent channel matrix. opt The optimal lower triangular matrix L opt The corresponding Kolesky decomposition result serves as the covariance matrix C in the approximate posterior probability distribution of the received signal corresponding to the sparse signal. This method constructs a lower triangular matrix iteratively based on the Kapolin condition number. The accuracy of the lower triangular matrix increases with the number of iterations, and the number of iterations can be adjusted according to actual conditions 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 sparsified equivalent channel matrix H′ eff Construct the inverse matrix D, using the following method: noise variance σ n -2 Multiply by the sparse equivalent channel matrix H′ eff Hermitian transpose, plus the symbolic 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. Calculate the Kapollin matrix K based on the initial lower triangular matrix L and the inverse matrix D. The calculation method is: multiply the Hermitian transpose of the initial lower triangular matrix L by the inverse matrix D, and then multiply by the initial lower triangular matrix L.

[0036] Calculate the Kapolin condition number; calculate the initial Kapolin condition number K of the Kapolin matrix K. (0) The estimation performance of the lower triangular matrix L is measured by dividing the rank of the Kapolin matrix K by the total number of subcarriers N, and then dividing by the determinant of the Kapolin matrix K to the power of 1 / N.

[0037] Lower triangular matrix update; iteratively update the column vector l of the lower triangular matrix L, starting from the index set of non-zero elements of l. Select the index vector e k Add the corresponding column vector l to obtain the updated column vector l′, and recalculate the new Kapolin condition number K. (t) If the new Kapolin condition number K (t) Less than the original Kapolin 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 Kolesky decomposition, calculate the approximate covariance matrix of the approximate posterior probability distribution of the received signal. The calculation method is the optimal lower triangular matrix L. opt Multiply by the optimal lower triangular matrix L opt Hermitian transpose.

[0039] Specifically, the steps for determining the optimal estimate of the received signal include:

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

[0041] The estimated signal is output when the absolute value of the change in the mean u between two consecutive iterations is less than a set constant ε, and the iteration terminates, outputting the estimated signal.

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

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

[0044] Using Jacobi scaling, the equivalent channel matrix H is... eff Normalize the diagonal elements; when the elements in the normalized matrix are less than the set sparsity factor threshold, H... eff The elements at the corresponding positions are set to zero. At this point, the original matrix is ​​divided into multiple independent submatrices, and H is increased. eff Sparsity.

[0045] Specifically, take H eff The absolute values ​​of the diagonal elements are placed sequentially into the diagonal matrix J, and the matrix is ​​normalized. The calculation method is as follows: multiply the reciprocal of the square root of the diagonal matrix J by the equivalent channel matrix H. eff Then multiply by the reciprocal of the square root of the diagonal matrix J. If the normalized matrix... elements on If the absolute value is less than the sparsity factor threshold ξ, then the corresponding element H on the equivalent channel matrix will be... eff Set (i,j) to 0 to obtain the sparse equivalent channel matrix H′ eff .

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

[0047] Based on the index mapping relationship, the subcarrier corresponding to the bit in the signal to be transmitted is determined and activated. By performing constellation mapping on the bit and using the activated subcarrier to carry the constellation-mapped signal, a sparse signal is obtained and transmitted.

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

[0049] The active subcarrier is determined based on the matrix position of the non-zero elements in the optimal estimate. Based on the active subcarrier and the index mapping relationship, the transmitted bits are recovered, and the original signal detection is completed.

[0050] The sparse signal has modulation symbols only on the active subcarriers, while the other inactive subcarriers only transmit zero symbols, including:

[0051] A sequence of bits to be transmitted of length m is divided into G groups, each group having p = m / G bits. These p bits are further 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, each group has a symbol length of L.

[0052] Each group activates only K subcarriers, while the other subcarriers only transmit 0 symbols.

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

[0054] Specifically, the transmitted bit sequence of length m is divided into G sub-blocks, each sub-block having a bit length of p = m / G, and each group of bits is mapped and modulated to a length of N. g The number of subcarriers in the system is N, and there are N subcarriers. g = N / G. The p bits are divided into two parts, the first part... 1 bit is 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 mapping the constellation into K constellation symbols of order M, where the constellation symbol is a. g =[a g,1 ,...,a g,k ,…,a g,K ],in It is a constellation symbol set. After obtaining the active subcarrier information and constellation symbols of all sub-blocks, modulation is performed to obtain a modulation symbol [x1,...,x] of length N×1. g ,…,x G ], where the modulation symbol x of the g-th group g for

[0055] At the receiving end, the likelihood probability and log-likelihood ratio of each detected signal vector to the vector in the index mapping set are calculated to obtain the transmitted bit information. This makes the algorithm easier to combine with channel coding, further increasing detection accuracy. In other words, it estimates the signal... Group the vectors and calculate the Gaussian likelihood probability of each sub-vector and the set of indexed modulation vectors. Calculate the log-likelihood ratio L of each bit of the indexed modulation vector in each group. a Then, the inverse mapping symbols of each group of indices and the symbols after constellation inversion are merged to restore the transmitted bits and complete the detection of the original signal.

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

[0057] Gaussian likelihood probability calculation; the optimal estimated signal Divided into G groups, each group has a sub-vector A vector s of length L is mapped to an index in a vector set. i Calculate the Gaussian likelihood probability p(x) for each vector in the index-mapped vector set. g =s i The calculation method is as follows: 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 by π and the noise variance. The product of.

[0058] Log-likelihood ratio calculation; dividing the index-mapped vector set into two equal-sized subsets X 1 and X 0 , where X 1 Let X be the set of vectors that are judged to be 1 under the current bit. 0 Let x be the set of vectors whose judgment is 0, and then let p(x) be the Gaussian likelihood probability. g =s i Calculate the m-th bit d in the g-th index mapping vector. g,m Log-likelihood ratio L a (d g,m The calculation method is as follows: the Gaussian likelihood probability set of vectors with a decision result of 1. The set of Gaussian likelihood probabilities of vectors with a decision result of 0 The ratio is taken as the natural logarithm. Finally, based on the sign of the log-likelihood ratio, the transmitted bit sequences of each group are recovered, and the transmitted bit sequences of each group are combined and output to recover the transmitted bits, thus completing the detection of the original signal.

[0059] The signal detection method in this embodiment obtains an approximate solution for the covariance matrix of the approximate posterior probability distribution of the received signal by sparsifying the equivalent channel matrix corresponding to the sparse signal. Specifically, for the transmitted bits, index mapping and constellation mapping are performed after grouping to obtain the sparse signal. For the equivalent channel matrix corresponding to the sparse signal, elements in the equivalent channel matrix less than the preset threshold are set to 0, and noise and interference elements in the original equivalent channel matrix are removed. An initial lower triangular matrix is ​​constructed, and the column vectors of the lower triangular matrix are iteratively updated according to minimizing the Kapolin condition number to obtain the optimal lower triangular matrix. Then, the approximate covariance matrix of the approximate posterior probability distribution of the received signal is solved by Kolesky decomposition. The received signal is detected using the expectation propagation algorithm, and the active subcarrier is determined based on the matrix position of the non-zero elements in the detected received signal. Based on the active subcarrier and the index mapping relationship, the original transmitted bits are obtained. This method increases the sparsity of the equivalent channel matrix corresponding to the received signal by using index mapping and Jacobi scaling, and then finds an approximate solution for the covariance matrix of the approximate posterior probability distribution of the received signal. This avoids the matrix inversion step, reduces the complexity of the signal detection algorithm, and improves the accuracy of signal detection.

[0060] In summary, by sparsifying the equivalent channel matrix corresponding to the received signal during the approximate covariance matrix inversion operation, and then aiming to determine the optimal lower triangular matrix by making the matrix obtained from the Kolesky decomposition approximate the inverse of the sparse equivalent channel matrix, the optimal lower triangular matrix is ​​determined. The Kolesky decomposition result corresponding to the optimal lower triangular matrix is ​​then used as the approximate covariance matrix in the approximate posterior probability distribution of the received signal. This effectively avoids the matrix inversion step in the expectation propagation algorithm, reducing the algorithm's complexity. The specific framework of the entire method can be found here. Figure 2 .

[0061] Example 2

[0062] A signal detection device for a sparse channel matrix, employing the signal detection method described above to achieve signal detection.

[0063] The relevant technical solutions are the same as in Embodiment 1, and will not be repeated here.

[0064] Example 3

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

[0066] The relevant technical solutions are the same as in Embodiment 1, and will not be repeated here.

[0067] Example 4

[0068] A signal communication system with a sparse channel matrix includes: a signal transmitting device and a signal receiving device; wherein, the signal transmitting device is used to determine and activate the subcarriers corresponding to the bits in the original signal to be transmitted according to the index mapping relationship, and obtain a sparse signal by performing constellation mapping on the bits and using the activated subcarriers to carry the constellation-mapped signal;

[0069] The signal receiving device is used to determine the active subcarrier based on the matrix position of the non-zero elements in the optimal estimate after obtaining the optimal estimate of the sparse signal using the signal detection method described above, and to recover the transmitted bits based on the active subcarrier and the index mapping relationship, thereby completing the original signal detection.

[0070] The relevant technical solutions are the same as in Embodiment 1, and will not be repeated here.

[0071] To illustrate the effectiveness of this invention, the following examples are provided for verification.

[0072] Figure 3 The left and right figures in the diagram are simulation diagrams of the bit error rate performance and complexity performance when the method in the embodiment of the present invention processes the received signal. Figure 3 The left figure shows the signal-to-noise ratio (SNR) on the horizontal axis (dB) and the bit error rate (BER) on the vertical axis. Figure 3 The right figure shows that 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 shows the bit error rate (BER) versus signal-to-noise ratio (SNR) of the proposed detection method and the traditional expectation propagation detection method under different sparsity factor thresholds and the number of iterations for constructing the lower triangular matrix. The parameters in the proposed detection method are set as (ξ, T...). L )=(0.01,5),(ξ,T L ) = (0.05, 10), where ξ represents the sparsity factor threshold, T L This represents the number of iterations required to construct the lower triangular matrix.

[0073] Figure 3 The right figure in the diagram illustrates the complexity of the detection method proposed in this 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 packets, K represents the number of active indexes, and E represents the number of active indexes. sThis represents the energy of each modulation symbol. Experiments show that the complexity of this invention can be reduced by 60% compared to the traditional expectation propagation algorithm, while the detection accuracy of the two is similar.

[0074] Example 5

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

[0076] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

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

[0078] Those skilled in the art will readily understand that the above description is merely 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 within the scope of protection 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 currently received signal that are less than the threshold are set to 0, resulting in a sparse equivalent channel matrix; where the currently received signal is a sparse signal. The lower triangular matrix is ​​constructed through iterative optimization. The goal is to make the matrix obtained by the Kolesky decomposition close to the inverse of the sparse equivalent channel matrix. The optimal lower triangular matrix is ​​determined, and the Kolesky 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. Based on the approximate covariance matrix, the variance and mean of the approximate posterior probability distribution of the current received signal are calculated iteratively, and the convergent mean is selected as the optimal estimate of the transmitted signal corresponding to the current received signal. The sparse equivalent channel matrix is ​​determined in the following specific way: For the original equivalent channel matrix corresponding to the current received signal, Jacobi scaling is used for normalization. Based on a preset threshold, the elements in the normalized result matrix that are less than the threshold are set to 0 in the original equivalent channel matrix, resulting in a sparse equivalent channel matrix. The sparse signal is generated in the following manner: Based on the index mapping relationship, the subcarrier corresponding to the bit in the signal to be transmitted is determined and activated. By performing constellation mapping on the bit and using the activated subcarrier to carry the constellation-mapped signal, a sparse signal is obtained and transmitted. After obtaining the optimal estimate, the method further includes: The active subcarrier is determined based on the matrix position of the non-zero elements in the optimal estimate. Based on the active subcarrier and the index mapping relationship, the transmitted bits are recovered, and the original signal detection is completed.

2. A signal detection device for a sparsed channel matrix, characterized in that, Signal detection is achieved by using the signal detection method as described in claim 1.

3. A signal communication system with a sparsed channel matrix, characterized in that, include: A signal transmitting device and a signal receiving device; wherein the signal transmitting device is used to transmit a raw signal, and the signal receiving device is used to perform signal detection based on the raw signal using the signal detection method as described in claim 1.

4. A signal communication system with a sparsed 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 a sparse signal by performing constellation mapping on the bit and using the activated subcarrier to carry the constellation-mapped signal and transmitting it; The signal receiving device is configured to, after obtaining the optimal estimate of the sparse signal using the signal detection method as described in claim 1, determine the active subcarrier based on the matrix position of the non-zero elements in the optimal estimate, and recover the transmitted bits based on the active subcarrier and the index mapping relationship to complete the original signal detection.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein the computer program, when executed by a processor, controls the device containing the storage medium to perform the steps of the signal detection method as described in claim 1.

Images