Signal detection method, electronic equipment and computer readable storage medium

Abstract

The invention discloses a signal detection method, electronic equipment and a computer readable storage medium, and belongs to the technical field of communication. According to the method, a channel transfer matrix H of the MIMO system is obtained through channel estimation, the dimension of the channel transfer matrix H is N * K, N is the number of receiving antennas, and K is the number of transmitting antennas; constructing a random sampling matrix set based on index elements corresponding to each transmitting antenna in the channel transfer matrix H; and randomly extracting a preset number of random sampling matrixes from the random sampling matrix set, and carrying out L times of random iterative operation on a to-be-detected signal of the MIMO system through the extracted random sampling matrixes, and finally converging to obtain an estimated signal vector for estimating a real sending signal x. According to the invention, while the detection precision of the MIMO system is ensured, the calculation complexity of signal detection is reduced, and the detection convergence efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a signal detection method, an electronic device, and a computer-readable storage medium. Background Art

[0002] Massive Multiple Input Multiple Output (MIMO) system is an emerging technology in 5G. Compared with traditional MIMO system, it has significant improvements in energy efficiency, power consumption and link reliability.

[0003] However, there are two main types of detection techniques for MIMO systems, linear detection and nonlinear detection. In specific applications, nonlinear detection algorithms, such as maximum likelihood estimation, are difficult to apply to actual systems because their computational complexity will increase exponentially with the increase in the number of antennas and baseband signal modulation. Linear detection algorithms, such as zero forcing and minimum mean square error algorithms, can approach optimal performance in MIMO systems. Compared with the maximum likelihood estimation method, the complexity of linear detection is greatly reduced, but it requires complex high-dimensional matrix inversion operations. Especially in large-scale MIMO systems, due to the increase in the number of antennas, the complexity of matrix inversion is extremely high, and such linear detection algorithms become less applicable.

[0004] In order to reduce the complexity of linear detection algorithms caused by the matrix inversion problem, iterative algorithms have been mainly proposed in recent years. Iterative algorithms introduce the iterative method of solving linear equations in numerical analysis into large-scale MIMO systems, replacing the matrix inversion problem to reduce the complexity of the algorithm. They mainly include Richardson iteration, Jacobi iteration, Gauss-Seidel iteration and Successive over Relaxation iteration. However, all of the above iterative detection schemes have relatively strict requirements on the application scenarios (such as the number of antennas, the range of relaxation parameters, etc.) to ensure the convergence of these iterations, which severely limits the above schemes in practical applications.

[0005] Therefore, there is an urgent need for a signal detection solution that can ensure the detection accuracy of the MIMO system while reducing the computational complexity of signal detection and improving the detection convergence efficiency. Summary of the invention

[0006] The main purpose of this application is to provide a signal detection method, an electronic device and a computer-readable storage medium, aiming to solve the technical problem of how to reduce the computational complexity of signal detection and improve the detection convergence efficiency while ensuring the detection accuracy of the MIMO system.

[0007] To achieve the above object, the present application provides a signal detection method, comprising: Obtain a channel transfer matrix H of the MIMO system by using channel estimation, wherein the dimension of the channel transfer matrix H is N*K, N is the number of receiving antennas, and K is the number of transmitting antennas; Constructing a random sampling matrix set based on the index elements corresponding to each transmitting antenna in the channel transfer matrix H, wherein the random sampling matrix set consists of a plurality of random sampling matrices; A preset number of random sampling matrices are randomly extracted from the random sampling matrix set, and L random iteration operations are performed on the signal to be detected of the MIMO system through the extracted random sampling matrices, and finally converge to obtain an estimated signal vector for estimating the real transmitted signal x, where L is a positive integer.

[0008] In addition, to achieve the above-mentioned purpose, the present application also provides an electronic device, which includes: a memory, a processor, and a signal detection program stored in the memory and executable on the processor, and when the signal detection program is executed by the processor, the signal detection method as described above is implemented.

[0009] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, on which a signal detection program is stored, and when the signal detection program is executed by a processor, the signal detection method as described above is implemented.

[0010] The embodiment of the present application obtains the channel transfer matrix H of the MIMO system by using channel estimation, wherein the dimension of the channel transfer matrix H is N*K, N is the number of receiving antennas, and K is the number of transmitting antennas, and then constructs a random sampling matrix set based on the index elements corresponding to each transmitting antenna in the channel transfer matrix H, wherein the random sampling matrix set is composed of multiple random sampling matrices, and a preset number of random sampling matrices are randomly extracted from the random sampling matrix set, and the extracted random sampling matrices are used to perform L random iteration operations (i.e., multi-step conditional sampling) on ​​the signal to be detected of the MIMO system, and finally converge to obtain an estimated signal vector for estimating the real transmitted signal x, wherein L is a positive integer, so that the embodiment of the present application is different from the related art that first needs to convert the detection model into a linear system through a preprocessing transformation. The embodiment of the present application can directly carry out random iteration operations on the detection model, avoiding the complex matrix multiplication operations in the preprocessing transformation, and at the same time does not reduce the detection performance, so that the embodiment of the present application proposes a low-complexity MIMO signal detection method based on random iteration, which effectively reduces the computational complexity while maintaining the performance advantage of the detection method. The present application randomly extracts a preset number of random sampling matrices from a set of random sampling matrices, thereby exploiting the sparsity of random distribution, which can effectively reduce the computational operations in the random iteration process and reduce the computational complexity in the iteration process. By selecting the sampling block size (i.e., the size of the random sampling matrix), a compromise between the computational complexity of the algorithm and the detection performance can be achieved. In addition, the embodiment of the present application further improves the convergence performance on the basis of achieving global exponential convergence through multi-step conditional sampling. This random iterative detection method for uplink large-scale MIMO systems can ensure the detection performance of random iterations at a lower computational complexity.

[0011] The present application is applicable to large-scale MIMO systems. Compared with the traditional linear detection algorithm, the random iterative signal detection algorithm for large-scale MIMO systems in the embodiments of the present application avoids the matrix inversion operation, greatly reduces the computational complexity, and can achieve performance close to that of the MMSE (maximum likelihood estimation) detection algorithm after several iterative operations. The embodiments of the present application can ensure the accuracy of signal detection while having low computational complexity, improve the error performance and convergence speed of the detection method, and improve the accuracy of MIMO channel estimation. BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0012] Figure 1 This is a flow chart of the first embodiment of the signal detection method of the present application; Figure 2 It is a schematic diagram of an application scenario of a large-scale MIMO system according to an embodiment of the present application; Figure 3 A system structure diagram of the signal detection method according to an embodiment of the present invention; Figure 4 A comparison diagram of bit error rates of different probability distributions of the signal detection method in the embodiment of the present application; Figure 5 A comparison chart of the bit error rates of the signal detection method in the embodiment of the present application and other different detection algorithms; Figure 6 This is a performance comparison diagram of the signal detection method in the embodiment of the present application and other different detection algorithms under relevant channels; Figure 7 This is a diagram comparing the complexity of the signal detection method in the embodiment of the present application with other different detection algorithms under the relevant channel; Figure 8 This is a schematic diagram of the functional modules of the signal detection device according to an embodiment of the present application; Fig. 9 This is a schematic diagram of the hardware structure of the electronic device involved in the embodiment of the present application.

[0013] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0014] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0016] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0017] In this application, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0018] In addition, in this application, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0019] There are two main types of detection technologies for MIMO (Multiple Input Multiple Output) systems, linear detection and nonlinear detection. In specific applications, nonlinear detection algorithms, such as maximum likelihood estimation, are difficult to apply to actual systems because their computational complexity increases exponentially with the increase in the number of antennas and baseband signal modulation. Linear detection algorithms, such as zero forcing and minimum mean square error, can achieve close to optimal performance in MIMO systems. Compared with maximum likelihood estimation, the complexity of linear detection is greatly reduced, but it requires complex high-dimensional matrix inversion operations. Especially in large-scale MIMO systems, due to the increase in the number of antennas, the complexity of matrix inversion is extremely high, and such linear detection algorithms become less applicable.

[0020] In order to reduce the complexity of linear detection algorithms caused by the matrix inversion problem, iterative algorithms have been mainly proposed in recent years. Iterative algorithms introduce the iterative method of solving linear equations in numerical analysis into large-scale MIMO systems, replacing the matrix inversion problem to reduce the complexity of the algorithm. They mainly include Richardson iteration, Jacobi iteration, Gauss-Seidel iteration and Successive over Relaxation iteration. However, all of the above iterative detection schemes have relatively strict requirements on the application scenarios (such as the number of antennas, the range of relaxation parameters, etc.) to ensure the convergence of these iterations, which severely limits the above schemes in practical applications.

[0021] Therefore, there is an urgent need for a signal detection solution that can ensure the detection accuracy of the MIMO system while reducing the computational complexity of signal detection and improving the detection convergence efficiency.

[0022] Based on this, the present application embodiment provides a signal detection method, referring to Figure 1 , Figure 1 The following is a flow chart of an embodiment of a signal detection method of the present application. In this embodiment, the signal detection method includes: Step S10, obtaining a channel transfer matrix H of the MIMO system by using channel estimation, wherein the dimension of the channel transfer matrix H is N*K, N is the number of receiving antennas, and K is the number of transmitting antennas; The signal detection method of the embodiment of the present application is mainly applied to a multi-antenna uplink signal detection scenario, where the transmitting end is composed of one or more terminal users, including a total of K transmitting antennas, and the receiving end is composed of a base station including N receiving antennas, such as Figure 2 shown.

[0023] It should be noted that the embodiments of the present application are applicable to all equations of the form y=Hx+n, and are mainly used in the field of multi-antenna uplink signal detection. If properly adapted, they can also be applied to the calculation of downlink beamforming weights. It can be understood that since the essence of downlink beamforming is a kind of precoding, the distortion caused by the channel is pre-compensated so that the signal received by the receiving end is as equal as possible to the original transmitted signal before precoding. Therefore, the process of solving the beamforming weights can basically be regarded as the process of inverting the channel transfer matrix.

[0024] For example, the system structure diagram of the signal detection method according to the embodiment of the present invention is as follows: Figure 3 As shown. It mainly includes three parts: receiving antenna, RF processing module and baseband processing module. Among them, the baseband processing module includes two sub-modules: channel estimation and signal detection. The uplink signals received by multiple receiving antennas are processed by the RF processing module and sent to the baseband processing module. In the baseband processing module, the channel estimation module estimates the channel transfer matrix H using the pilot signal, and sends the channel transfer matrix H to the signal detection module. Signal detection uses the channel transfer matrix H and the received data signal y, and adopts the signal detection method provided in an embodiment of the present invention (a low-complexity MIMO signal detection method based on random iteration) to perform signal detection.

[0025] After step S10, executing step S20, constructing a random sampling matrix set based on the index elements corresponding to each transmitting antenna in the channel transfer matrix H, wherein the random sampling matrix set consists of a plurality of random sampling matrices; In this embodiment, those skilled in the art can understand that, in a MIMO (Multiple Input Multiple Output) system, multiple receiving antennas at a receiving end receive channel signals transmitted by a transmitting end to obtain at least two receiving signals, and then the receiving end performs channel estimation to obtain a channel transfer matrix H composed of channel coefficients between the transmitting antenna and the receiving antenna. It is easy to understand that each transmitting antenna in a MIMO system corresponds to a unique index element. For example, if there are 8 transmitting antennas in a MIMO system, then the transmitting antenna array of the MIMO system has a total of 8 index elements.

[0026] As an example, based on the index elements corresponding to each transmitting antenna in the channel transfer matrix H, the step of constructing a random sampling matrix set may specifically include: Step A10, randomly dividing the index elements corresponding to the K transmitting antennas in the channel transfer matrix H into r groups, and each group contains q index elements, to obtain r random sampling matrices; Step A20: combining the r random sampling matrices to form a random sampling matrix set.

[0027] In this embodiment, that is, the random sampling matrix set consists of r random sampling matrices, each random sampling matrix consists of index elements of q transmitting antennas, wherein q=K / r, and q and r are both positive integers.

[0028] Furthermore, in order to obtain better performance, that is, to improve the signal detection accuracy and convergence efficiency of the MIMO system, when selecting parameters, under the premise of ensuring that q and r are both integers, the value of q should be within As close as possible to .

[0029] In this embodiment, a random sampling matrix set is set to consist of r random sampling matrices, each of which consists of index elements of q transmitting antennas, wherein q=K / r, q and r are both positive integers, so that in this embodiment of the application, K indexes are randomly divided into r groups, each group containing q elements, to obtain r groups of non-repeated and non-missing index sets: .

[0030] In this embodiment, according to r Different sets of indexes The index in , for the identity matrix By dividing the columns, we can get r Random sampling matrix , this rThe random sampling matrices are put into a set to form a random sampling matrix set , so that it is convenient to perform random iterative operations based on the multi-step conditional sampling method (i.e., step S30 described below) according to the random sampling matrix set, so as to restore the transmission signal x of the transmitting antenna to the greatest extent, realize signal detection with low computational complexity, and ensure signal detection accuracy.

[0031] After step S20, execute step S30, randomly extract a preset number of random sampling matrices from the random sampling matrix set, and use the extracted random sampling matrices to perform L random iterative operations on the signal to be detected of the MIMO system, and finally converge to obtain an estimated signal vector for estimating the actual transmitted signal x, where L is a positive integer.

[0032] The embodiment of the present application obtains the channel transfer matrix H of the MIMO system by using channel estimation, wherein the dimension of the channel transfer matrix H is N*K, N is the number of receiving antennas, and K is the number of transmitting antennas, and then constructs a random sampling matrix set based on the index elements corresponding to each transmitting antenna in the channel transfer matrix H, wherein the random sampling matrix set is composed of multiple random sampling matrices, and a preset number of random sampling matrices are randomly extracted from the random sampling matrix set, and the extracted random sampling matrices are used to perform L random iteration operations (i.e., multi-step conditional sampling) on ​​the signal to be detected of the MIMO system, and finally converge to obtain an estimated signal vector for estimating the real transmitted signal x, wherein L is a positive integer, so that the embodiment of the present application is different from the related art that first needs to convert the detection model into a linear system through a preprocessing transformation. The embodiment of the present application can directly carry out random iteration operations on the detection model, avoiding the complex matrix multiplication operations in the preprocessing transformation, and at the same time does not reduce the detection performance, so that the embodiment of the present application proposes a low-complexity MIMO signal detection method based on random iteration, which effectively reduces the computational complexity while maintaining the performance advantage of the detection method. The present application randomly extracts a preset number of random sampling matrices from a set of random sampling matrices, thereby exploiting the sparsity of random distribution, which can effectively reduce the computational operations in the random iteration process and reduce the computational complexity in the iteration process. By selecting the sampling block size (i.e., the size of the random sampling matrix), a compromise between the computational complexity of the algorithm and the detection performance can be achieved. In addition, the embodiment of the present application further improves the convergence performance on the basis of achieving global exponential convergence through multi-step conditional sampling. This random iterative detection method for uplink large-scale MIMO systems can ensure the detection performance of random iterations at a lower computational complexity.

[0033] The present application is applicable to large-scale MIMO systems. Compared with the traditional linear detection algorithm, the random iterative signal detection algorithm for large-scale MIMO systems in the embodiments of the present application avoids the matrix inversion operation, greatly reduces the computational complexity, and can achieve performance close to that of the MMSE (maximum likelihood estimation) detection algorithm after several iterative operations. The embodiments of the present application can ensure the accuracy of signal detection while having low computational complexity, improve the error performance and convergence speed of the detection method, and improve the accuracy of MIMO channel estimation. It is worth mentioning that, compared with the traditional detection method, the method proposed in the embodiment of the present application not only has lower complexity, but also has global convergence characteristics, and can adapt to various practical application scenarios. Specifically, traditional low-complexity iterative detection requires a pre-processing stage, converting the original system model y=Hx+n into a linear model Ax=b and then performing iterative operations on its basis, which requires a matrix multiplication operation with a complexity of O(NK^2), which naturally has a higher complexity. The method proposed in the embodiment of the present application can directly perform low-complexity iterative detection on y=Hx+n, completely avoiding the matrix multiplication operation, and achieving lower detection complexity without reducing the detection performance. In terms of convergence, the random iterative detection method proposed in the embodiment of the present invention is globally convergent, that is, there is no potential condition required for the operation of any algorithm, and it can adapt to various network environments. On the contrary, some low-complexity iterative methods require a certain ratio of transmit and receive antennas. For example, some signal detection algorithms require that the number of receiving antennas N must be greater than the number of transmitting antennas K, while some signal detection algorithms require careful screening of parameters.

[0034] In addition, refer to Figure 4 and Figure 5 The applicant has found through a large amount of experimental data that the signal detection algorithm of the embodiment of the present application can ensure the accuracy of signal detection while having low computational complexity and high bit error performance, thereby improving the accuracy of MIMO channel estimation. Figure 4 It is a comparison chart of bit error rates of different probability distributions of the signal detection method in the embodiment of the present application. Figure 5 It is a comparison chart of the bit error rate of the signal detection method in the embodiment of the present application and other different detection algorithms.

[0035] Further, see Figure 6 and Figure 7 The applicant has proved through a large amount of experimental data that the signal detection algorithm of the embodiment of the present application can ensure the accuracy of signal detection while having low computational complexity and high convergence performance. Figure 6 It is a performance comparison chart of the signal detection method in the embodiment of the present application and other different detection algorithms under relevant channels. Figure 7This is a complexity comparison chart of the signal detection method in the embodiment of the present application and other different detection algorithms under the relevant channel.

[0036] In a possible implementation, the step of randomly extracting a preset number of random sampling matrices from the random sampling matrix set based on the multi-step conditional sampling method, and performing L random iteration operations on the signal to be detected of the MIMO system through the extracted random sampling matrices, and finally converging to obtain an estimated signal vector for estimating the real transmitted signal x includes: Step B10, setting the initial value of the number of iterations b to zero; Step B20, set b=b+1; Step B30, randomly extracting a preset number of random sampling matrices from the random sampling matrix set as the target matrix used in the b-th random iteration; In one embodiment, after randomly extracting a preset number of random sampling matrices from the random sampling matrix set as the target matrix used in the b-th random iteration, the method further includes: G10, remove the target matrix from the set of random sampling matrices.

[0037] This embodiment randomly extracts a preset number of random sampling matrices from the random sampling matrix set as the target matrix used for the b-th random iteration, and then removes the target matrix from the random sampling matrix set, thereby ensuring that a new random sampling matrix is ​​selected from the random sampling matrix set for each iteration, effectively avoiding the problem of the same random sampling matrix being repeatedly selected, thereby effectively improving the sampling diversity and accelerating the iterative convergence speed of signal detection.

[0038] In this embodiment, the preset number can be set by those skilled in the art according to actual conditions, and this embodiment does not make any specific limitation. For example, the preset number can be one or two.

[0039] Step B40: superimpose the transmitter residual obtained based on the least squares estimation to x b-1 , get the signal vector x to be detected after the bth random iteration b , where x b-1 is the signal vector to be detected after the b-1th random iteration of the MIMO system; In this embodiment, x 0 is equal to zero.

[0040] Exemplarily, in step B50, in the step of superimposing the transmitting end residual obtained based on the least squares estimation to x b-1 Before the steps, also include: Step B50, determining the receiving end residual corresponding to the received signal of the receiving antenna, and performing least squares estimation on the transmitting end residual corresponding to the transmitting signal of the transmitting antenna based on the target matrix and the receiving end residual, to obtain the transmitting end residual obtained based on the least squares estimation.

[0041] As an example, in step B50, the step of determining a receiving end residual corresponding to a received signal of the receiving antenna includes: Step C10, calculating a receiving-end residual corresponding to a received signal of the receiving antenna by a first preset calculation formula; Wherein, the first preset calculation formula is: C=y-Hx b-1 ; Where y is the corresponding received signal when the transmitted signal is x, H is the channel transfer matrix, Hx b-1 To send a signal x b-1 The corresponding received signal when C is the receiving end residual corresponding to the received signal, x b-1 is the signal vector to be detected after b-1 random iterations.

[0042] This embodiment uses the above-mentioned first preset calculation formula to accurately calculate the receiving-end residual corresponding to the received signal with low complexity.

[0043] Step B61, when b is less than L, return to step B20: set b=b+1; Step B62: when b is greater than or equal to L, based on the signal vector x to be detected after b random iterations b , determine the estimated signal vector obtained by the final convergence for estimating the real transmitted signal x.

[0044] Exemplarily, in the step of obtaining the signal vector x to be detected after the b-th random iteration of the MIMO system b After the step of, the method further comprises: Furthermore, based on the signal vector x to be detected after b random iterations b The steps before include: Step D10: Calculate the signal vector x to be detected after b random iterations by using the second preset calculation formula b ; Wherein, the second preset calculation formula is: x b =x b-1 + ;

[0045] in, , H is the channel transfer matrix, is the target matrix, x b-1 is the signal vector to be detected after b-1 random iterations, It is a channel based on extraction The transmitting end residual D is estimated by least squares using the receiving end residual C, where D= , x is the real sending signal, yes The conjugate transposed matrix of .

[0046] This embodiment uses the above-mentioned second preset calculation formula to accurately calculate the signal vector x to be detected after b random iterations with low complexity. b , which is convenient for the subsequent accurate calculation of the signal vector x to be detected b , accurately restore the real sending signal of the transmitting antenna in the MIMO system, and then further improve the convergence performance on the basis of achieving global exponential convergence, ensure the detection performance of random iteration, improve the error performance and convergence speed of the detection method, and improve the accuracy of MIMO channel estimation. Exemplarily, the signal vector x to be detected based on the b-th random iteration b The step of determining the estimated signal vector obtained by final convergence for estimating the real transmitted signal x comprises: Step E10: the signal vector x to be detected after b random iterations b Perform constellation point quantization output and get x b The quantitative detection results corresponding to each element in; Step E20: determining, based on the quantization detection result, an estimated signal vector obtained by final convergence and used to estimate the actual transmitted signal x.

[0047] Furthermore, the signal vector x to be detected that undergoes b random iterations b Perform constellation point quantization output and get x b The steps of quantifying the detection results corresponding to each element in include: Step F10, compare x b Each element in The Euclidean distance between each constellation point in the decoding candidate point set;

[0048] Step F20, each element The corresponding constellation point with the smallest Euclidean distance is used as each element The corresponding quantitative test results.

[0049] In this embodiment, the signal vector x to be detected is randomly iterated b times. b Perform constellation point quantization output and get x bThe quantized detection result corresponding to each element in is then determined based on the quantized detection result, and the estimated signal vector for estimating the real transmitted signal x is finally converged, so as to accurately restore the real transmitted signal of the transmitting antenna in the MIMO system to the greatest extent possible, thereby achieving the goal of reducing the computational complexity of signal detection and improving the detection convergence efficiency while ensuring the detection accuracy of the MIMO system.

[0050] In the embodiment of the present application, the initial value of the number of iterations b is set to zero, and then b=b+1 is set, and then a preset number of random sampling matrices are randomly selected from the random sampling matrix set as the target matrix used for the b-th random iteration, and the target matrix is ​​removed from the random sampling matrix set; the receiving end residual corresponding to the received signal of the receiving antenna is determined, and based on the target matrix and the receiving end residual, the transmitting end residual corresponding to the transmitting signal of the transmitting antenna is estimated by least squares, and then the estimated transmitting end residual is superimposed on x b-1 , get the signal vector x to be detected after the bth random iteration b , where x 0 =0, and when b is less than L, return to execute: set b=b+1; when b is greater than or equal to L, based on the signal vector x to be detected after b random iterations, b , determine the estimated signal vector obtained by the final convergence for estimating the real transmitted signal x, so that the embodiment of the present application provides a multi-step conditional sampling method to utilize the sparse structure of the sampling distribution, realize multi-step conditional probability optimization to accelerate iterative convergence, and then achieve lower detection complexity without reducing the detection performance. In addition, many traditional low-complexity iterative detection schemes have certain restrictions on application scenarios when applied in order to ensure convergence. For example, some signal detection algorithms require that the number of transceiver antennas satisfy the condition that N (the number of receiving antennas) is greater than K (the number of transmitting antennas), and some signal detection algorithms require careful screening of parameters, etc. The signal detection method based on random iteration proposed in the embodiment of the present application is globally convergent, that is, there are no potential conditions required for the operation of any algorithm, and it can be applied in various network environments.

[0051] In order to help understand the technical principles or technical concepts of the embodiments of the present application, a specific embodiment 1 is listed, in which: This specific embodiment provides a low-complexity MIMO signal detection method based on random iteration, which directly uses the detection model to optimize the random sampling iteration method to obtain the final decoding decision; the method has better detection performance under the condition of lower computational complexity. Specifically, a low-complexity MIMO signal detection method based on random iteration includes: Step 1: For a transmit antennas and For a MIMO system with multiple receiving antennas, the channel is estimated using pilot signals to obtain the channel transfer matrix , the dimension is .

[0052] Step 2: Construction r Random sampling matrix ( ), the random sampling matrix set composed of these random sampling matrices is recorded as ,in represents the number of columns of each random sampling matrix, r and K / r All must be integers. To achieve better performance, when selecting parameters, q and r Under the premise that are all integers, q The value of As close as possible to .

[0053] The specific random sampling matrix construction method is: randomly divide K indexes into r groups, each containing q elements, and get r The index set with no duplicate or missing groups: .

[0054] This embodiment follows r Different sets of indexes The index in , for the identity matrix By dividing the columns, we can get r Random sampling matrix . r The random sampling matrices are put into a set to form a random sampling matrix set .

[0055] Step 3: Based on the multi-step conditional sampling method, each time the r One is extracted from the random sampling matrix. The extracted random sampling matrix is ​​used to perform random iteration on the signal to be detected. The specific operation steps are as follows: Step 3.1 Set the initial value of the signal to be detected to , the initial number of iterations is set to , the maximum number of iterations is set to L.

[0056] Step 3.2 Based on the multi-step conditional sampling method, randomly select a matrix from the random sampling matrix set R , as the random sampling matrix used for the k+1th random iteration, and remove the matrix from the set R .

[0057] Step 3.3 Use the following formula to iterate and get the signal to be detected after the k+1th iteration:

[0058] ;

[0059] In this embodiment, it should be noted that the mechanism of the formula can be understood as follows: The signal is sent The actual received signal And send signal The corresponding received signal The difference between them can be regarded as the residual of the received signal. It is a channel based on extraction and the receiving end residual The residual error of the signal sent by the transmitting end Perform least squares estimation. Add the estimated transmitting residual to the signal Get the updated signal Through such iteration, this embodiment can make and Closer to the real signal than As the number of iterations increases, the iteration results will eventually converge to the real transmitted signal .

[0060] in , Represents the signal received by the receiving antenna, with a dimension of , number of iterations .

[0061] Step 3.4 compares the size of k and L. If , jump to step 3.2 to continue iteration. , proceed to step 4.

[0062] Step 4: Output results after L iterations The constellation points (constellation points refer to the set of vector points in the constellation diagram after modulation) are quantized and output to obtain the final quantization detection result. The specific quantization operation can be expressed as: .

[0063] It should be noted that the above-mentioned specific embodiment 1 is only used to help understand the technical concept of the embodiment of the present application, and does not constitute a limitation on the signal detection method of the present application. More simple transformations based on the technical concept should all be within the scope of protection of the present application.

[0064] In order to further help understand the technical principles of the embodiments of the present application, the signal detection method of the specific embodiment 2 is listed, wherein: like Figure 2 As shown, the MIMO system includes transmit antennas and The receiving antenna receives the signal and the signal sent by the transmitting antenna The relationship between them is shown as follows:

[0065]

[0066] In the formula, represents the channel transfer matrix, with dimension ; Represents Gaussian noise interference, with dimension , with a mean of 0 and a variance of Gaussian distribution of Represents sending a signal, with a dimension of , where each element belongs to the constellation point set modulated by M-QAM , and its decoding candidate point set is ; Represents the output signal, with dimension .

[0067] The traditional iterative detection algorithm iteratively solves the equivalent linear system of the detection model, that is, ;

[0068] In the formula , (ZF filter matrix) or (MMSE filter matrix).

[0069] However, this method requires pre-calculation of the symmetric positive definite matrix in order to achieve matrix partitioning in subsequent iterations or column partitioning in random iterations or and vector .because , matrix multiplication or The computational complexity of ), and matrix inversion It also requires a high level of complexity.

[0070] This embodiment directly targets Design the detection system instead of Linear detection system, thus achieving the same performance with lower computational complexity.

[0071] The following describes a specific implementation of the present invention by taking a MIMO system with K=6 transmitting antennas and N=8 receiving antennas as an example.

[0072] Step 1: For a transmit antennas and For a MIMO system with multiple receiving antennas, the channel is estimated using pilot signals to obtain the channel transfer matrix , the dimension is For a MIMO system with K=6 transmitting antennas and N=8 receiving antennas, the channel transfer matrix can be expressed as

[0073]

[0074] in represents the channel response between the jth transmitting antenna and the ith receiving antenna.

[0075] Step 2: Construction r Random sampling matrix ( ), the random sampling matrix set composed of these random sampling matrices is recorded as ,in represents the number of columns of each random sampling matrix, r and K / r To achieve better performance, when selecting parameters, q and r should be integers. As close as possible to .

[0076] In this embodiment, the specific random sampling matrix construction method is: K indexes are randomly divided into r groups, each group contains q elements, and get r The index set with no duplicate or missing groups: .

[0077] This embodiment can be r Different sets of indexes The index in , for the identity matrix By dividing the columns, we can get r Random sampling matrix . r The random sampling matrices are put into a set to form a random sampling matrix set .

[0078] Taking K=6 as an example, within the range of q The closest you can get The integer is 2, and the corresponding r=K / q=3 is also an integer, which meets the parameter selection conditions. Therefore, q=2 and r=3 are selected to construct the random sampling matrix.

[0079] First, K=6 indexes are randomly divided into 3 groups, 2 in each group. The grouping results are as follows: ;

[0080] Among them, the identity matrix It can be expressed as:

[0081]

[0082] This embodiment follows r Different sets of indexes The index in , for the identity matrix Column division, that is, from Extract from The columns corresponding to the index in are:

[0083] , , ;

[0084] In this embodiment, these three random sampling matrices are put into a set to form a random sampling matrix set: .

[0085] Step 3: Based on the multi-step conditional sampling method, one random sampling matrix is ​​extracted each time from the r random sampling matrices obtained in step 2. Then, the extracted random sampling matrix is ​​used to perform random iteration on the signal to be detected. The specific operation steps are as follows: Step 3.1: Set the initial value of the signal to be detected to , the initial number of iterations is set to , the maximum number of iterations is set to L.

[0086] Step 3.2: Based on the multi-step conditional sampling method, randomly select a matrix from the random sampling matrix set R , as the random sampling matrix used for the k+1th random iteration, and remove the matrix from the set R .

[0087] Step 3.3 Use the following formula to iterate and get the signal to be detected after the k+1th iteration:

[0088] ;

[0089] in , Represents the signal received by the receiving antenna, with a dimension of . Iterations .

[0090] Step 3.4 compares the size of k and L. If , jump to step 3.2 to continue iteration. , proceed to step 4.

[0091] This embodiment still takes the MIMO system with 6 transmitting antennas and 8 receiving antennas as an example to illustrate the specific operation process: Step 1, according to step 3.1, the initial value is set to , the initial number of iterations is set to , the maximum number of iterations is set to L=2.

[0092] Step 2: According to step 3.2, Randomly extract a random sampling matrix from , assuming that the extracted matrix is , then the matrix is ​​the random sampling matrix of the first iteration, and the matrix is ​​removed from the set R , the R matrix after removal is updated to .

[0093] Step 3: Perform an iterative operation according to step 3.3 to obtain the signal to be detected after the first iteration. ,Right now

[0094]

[0095] in , Represents the signal received by the receiving antenna, with a dimension of . Iterations .

[0096] Step 4: According to step 3.4, compare the size of k and L and find , jump to step 3.2 to continue iteration.

[0097] Step 5: According to step 3.2, Randomly extract a random sampling matrix from , assuming that the extracted matrix is , then the matrix is ​​the random sampling matrix of the second iteration, and the matrix is ​​removed from the set R , the R matrix after removal is updated to .

[0098] Step 6: Continue to iterate according to step 3.3 to obtain the signal to be detected after the second iteration. ,Right now

[0099] ;

[0100] in , Represents the signal received by the receiving antenna, with a dimension of . Iterations .

[0101] Step 7: According to step 3.4, compare the size of k and L and find , proceed to step 4.

[0102] Step 4: Output results after L iterations The constellation points are quantized and output to obtain the final quantization detection result. The specific quantization operation can be expressed as: . That is, comparison Each element in and the set of candidate points for decoding The Euclidean distance between the constellation points in The constellation point with the smallest Euclidean distance between Quantitative test results.

[0103] In addition, the following is an explanation of the computational complexity of the embodiments of the present invention: Since the signal detection algorithm of the embodiment of the present invention is an iterative algorithm, its complexity mainly depends on the complexity of the iterative formula. In the iterative formula, since the random sampling matrix Each row or column of has only one non-zero element 1, and all other elements are 0, so In actual calculation, the equivalent result can be obtained by simply extracting the matrix H by column. and When multiplying , it can be quickly implemented by simple element transposition and 0-element augmentation matrix, and does not involve multiplication operations. Therefore, the computational complexity of these two steps can be ignored.

[0104] Therefore, the complexity of a single iteration mainly depends on the complexity of the following four calculations: (1) Calculation .

[0105] (2) Calculation and The product of .

[0106] (3) Calculation and The product of .

[0107] (4) Calculation and The product of .

[0108] In this embodiment, according to the statistics of the number of multiplications, it can be obtained that: The complexity is ,in for The complexity of is the complexity of finding the matrix inversion. Calculate and The complexity of the product is .calculate and The complexity of the product is .calculate and The complexity of multiplication is Therefore, the single iteration complexity of the solution proposed in this embodiment is ,Right now However, since only q columns of channel information are extracted for calculation in each iteration, it takes r=K / q iterations to utilize all the channel information. Therefore, when compared with other traditional iterative detection algorithms, r iterations in this embodiment are equivalent to one iteration of the traditional solution. When q selects the optimal parameter hour, , at this time, the complexity of this scheme for r iterations is , which is superior to other iterative detection schemes in both theoretical and simulation results.

[0109] Description of iterative convergence of the embodiment of the present invention: In this embodiment, the multi-step conditional sampling method given in step 3.2 takes the previous sampling selection into consideration during the current sampling, thereby effectively avoiding the problem of the same random sampling matrix being repeatedly selected, effectively improving the sampling diversity, and accelerating the iterative convergence speed.

[0110] Theoretical derivation shows that the convergence rate of the random iterative scheme provided in the embodiment of the present invention is satisfy:

[0111] ;

[0112] in, represents the minimum eigenvalue of the matrix. is less than 1, so the iterative process provided by this embodiment has global convergence.

[0113] Compared with other low-complexity detection algorithms such as Jacobi iteration, Richardson iteration, Gauss-Seidel iteration and continuous over-relaxation (SOR) iteration, considering the comparison of computational complexity and global convergence characteristics, the low-complexity detection algorithm based on random iteration provided in this embodiment has the best overall performance.

[0114] For ease of understanding, please refer to the following Table (I). Table (I) is a comparison table of the detection performance of the signal detection method in the embodiment of the present application under the relevant channel and other different detection algorithms, that is, a comparison chart of the complexity and global convergence between different schemes is provided.

[0115] (one) Among them, NRIDA is a low-complexity MIMO detection method based on random iteration proposed in the present invention, wherein N is the number of antennas on the large-scale MIMO base station side, K is the total number of antennas on the user side, and k is the number of iterations of each iterative algorithm.

[0117] It should be noted that what is disclosed above is only a preferred embodiment of the present application, and it certainly cannot be used to limit the protection scope of the present application. Ordinary technicians in this field can understand that all or part of the processes of implementing the above embodiment and the equivalent changes made according to the claims of the present invention are still within the scope covered by the present application.

[0118] That is to say, the above-mentioned specific embodiment 2 is only used to help understand the technical concept of the embodiment of the present application, and does not constitute a limitation of the present application. More simple transformations based on the technical concept should all be within the scope of protection of the present application.

[0119] In addition, the present application also provides a signal detection device, referring to Figure 8 , Figure 8 This is a functional module diagram of an embodiment of a signal detection device of the present application.

[0120] In this embodiment, the signal detection device includes: A channel transfer matrix determination module 10 is configured to obtain a channel transfer matrix H of the MIMO system by using channel estimation, wherein the dimension of the channel transfer matrix H is N*K, N is the number of receiving antennas, and K is the number of transmitting antennas; A sampling matrix determination module 20 is configured to construct a random sampling matrix set based on the index elements corresponding to each transmitting antenna in the channel transfer matrix H, wherein the random sampling matrix set is composed of a plurality of random sampling matrices; The transmitted signal restoration module 30 is configured to randomly extract a preset number of random sampling matrices from the random sampling matrix set based on a multi-step conditional sampling method, and perform L random iterative operations on the signal to be detected of the MIMO system through the extracted random sampling matrices, and finally converge to obtain an estimated signal vector for estimating the actual transmitted signal x, where L is a positive integer.

[0121] In some embodiments, the random sampling matrix set consists of r random sampling matrices, each random sampling matrix consists of index elements of q transmitting antennas, where q=K / r, and q and r are both positive integers.

[0122] In some embodiments, the sending signal restoration module 30 is further configured to: Set the initial value of the number of iterations b to zero; Set b = b + 1; Randomly extract a preset number of random sampling matrices from the random sampling matrix set as the target matrix used in the b-th random iteration; The transmitter residual obtained based on the least squares estimation is superimposed on x b-1 , we get the signal vector x to be detected after the bth random iteration of the MIMO system b , where x b-1 is the signal vector to be detected after the b-1th random iteration of the MIMO system; When b is less than L, return to execute: the step of setting b=b+1; When b is greater than or equal to L, based on the signal vector x to be detected after b random iterations, b , determine the estimated signal vector obtained by the final convergence for estimating the real transmitted signal x.

[0123] In some embodiments, the sending signal restoration module 30 is further configured to: After the step of randomly extracting a preset number of random sampling matrices from the random sampling matrix set as the target matrix used in the b-th random iteration, the target matrix is ​​removed from the random sampling matrix set.

[0124] In some embodiments, the sending signal restoration module 30 is further configured to: Determine a receiving-end residual corresponding to a received signal of the receiving antenna, and perform a least squares estimation on a transmitting-end residual corresponding to a transmitted signal of the transmitting antenna based on the target matrix and the receiving-end residual to obtain a transmitting-end residual obtained based on the least squares estimation; In some embodiments, the sending signal restoration module 30 is further configured to: The signal vector x to be detected after b random iterations b Perform constellation point quantization output and get x b The quantitative detection results corresponding to each element in; Based on the quantization detection result, a finally converged estimated signal vector for estimating the real transmitted signal x is determined.

[0125] In some embodiments, the sending signal restoration module 30 is further configured to: Compare x b Each element in The Euclidean distance between each constellation point in the decoding candidate point set;

[0126] Each element The corresponding constellation point with the smallest Euclidean distance is used as each element The corresponding quantitative test results.

[0127] In some embodiments, the sending signal restoration module 30 is further configured to: Calculate the receiving end residual corresponding to the received signal by using a first preset calculation formula; Wherein, the first preset calculation formula is: C=y-Hx b-1 ; Where y is the corresponding received signal when the transmitted signal is x, H is the channel transfer matrix, Hx b-1 To send a signal x b-1 The corresponding received signal when C is the receiving end residual corresponding to the received signal, x b-1 is the signal vector to be detected after b-1 random iterations.

[0128] In some embodiments, the sending signal restoration module 30 is further configured to: The signal vector x to be detected after b random iterations is calculated by the second preset calculation formula. b ; Wherein, the second preset calculation formula is: x b =x b-1 + ;

[0129] in, , H is the channel transfer matrix, is the target matrix, x b-1 is the signal vector to be detected after b-1 random iterations, is based on the decimated channel response matrix The transmitting end residual D is estimated by least squares using the receiving end residual C, where D= , x is the real sending signal, yes The conjugate transposed matrix of .

[0130] The signal detection device provided in this embodiment and the signal detection method provided in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the embodiments of the above signal detection method. This embodiment has the same beneficial effects as the embodiments of the signal detection method, which will not be repeated here.

[0131] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] In addition, an embodiment of the present application further provides an electronic device, Reference Fig. 9 , Fig. 9 The hardware structure diagram of an electronic device provided in an embodiment of the present application is shown in FIG. Fig. 9 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0133] Those skilled in the art will understand that Fig. 9 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Fig. 9 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module and a signal detection program.

[0134] exist Fig. 9In the electronic device shown, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in this embodiment can be set in the communication device, and the communication device calls the signal detection program stored in the memory 1005 through the processor 1001, and executes the signal detection method provided in any of the above embodiments.

[0135] The terminal proposed in this embodiment and the signal detection method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to any of the above embodiments, and this embodiment has the same beneficial effects as executing the signal detection method.

[0136] In addition, an embodiment of the present application also proposes a computer storage medium, which may be a non-volatile computer-readable storage medium. A signal detection program is stored on the computer-readable storage medium. When the signal detection program is executed by a processor, the signal detection method of the present application as described above is implemented.

[0137] The various embodiments of the electronic device and the computer-readable storage medium of the present application may refer to the various embodiments of the signal detection method of the present application, which will not be described in detail here.

[0138] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0139] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0140] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling an electronic device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0141] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A signal detection method, characterized in that: include: Obtain a channel transfer matrix H of the MIMO system by using channel estimation, wherein the dimension of the channel transfer matrix H is N*K, N is the number of receiving antennas, and K is the number of transmitting antennas; Constructing a random sampling matrix set based on the index elements corresponding to each transmitting antenna in the channel transfer matrix H, wherein the random sampling matrix set consists of a plurality of random sampling matrices; A preset number of random sampling matrices are randomly extracted from the random sampling matrix set, and L random iteration operations are performed on the signal to be detected of the MIMO system through the extracted random sampling matrices, and finally converge to obtain an estimated signal vector for estimating the real transmitted signal x, where L is a positive integer.

2. The signal detection method according to claim 1, characterized in that: The random sampling matrix set consists of r random sampling matrices, each of which consists of index elements of q transmitting antennas, wherein q=K / r, and q and r are both positive integers.

3. The signal detection method according to claim 1, characterized in that: The step of randomly extracting a preset number of random sampling matrices from the random sampling matrix set, and performing L random iteration operations on the signal to be detected of the MIMO system through the extracted random sampling matrices, and finally converging to obtain an estimated signal vector for estimating the real transmitted signal x comprises: Set the initial value of the number of iterations b to zero; Set b = b + 1; Randomly extract a preset number of random sampling matrices from the random sampling matrix set as the target matrix used in the b-th random iteration; The transmitter residual obtained based on the least squares estimation is superimposed on x b-1 , we get the signal vector x to be detected after the bth random iteration of the MIMO system b , where x b-1 is the signal vector to be detected after the b-1th random iteration of the MIMO system; When b is less than L, return to execute: the step of setting b=b+1; When b is greater than or equal to L, based on the signal vector x to be detected after b random iterations, b , determine the estimated signal vector obtained by the final convergence for estimating the real transmitted signal x.

4. The signal detection method according to claim 3, characterized in that: After the step of randomly extracting a preset number of random sampling matrices from the random sampling matrix set as the target matrix used in the b-th random iteration, the method further includes: The target matrix is ​​removed from the set of randomly sampled matrices.

5. The signal detection method according to claim 3, characterized in that: In the above method, the transmitting end residual obtained based on the least squares estimation is superimposed on x b-1 Before the steps, also include: Determine the receiving end residual corresponding to the received signal of the receiving antenna, and perform least squares estimation on the transmitting end residual corresponding to the transmitting signal of the transmitting antenna based on the target matrix and the receiving end residual to obtain the transmitting end residual obtained based on the least squares estimation.

6. The signal detection method according to claim 3, characterized in that: The signal vector x to be detected based on the b-th random iteration b The step of determining the estimated signal vector obtained by final convergence for estimating the real transmitted signal x comprises: The signal vector x to be detected after b random iterations b Perform constellation point quantization output and get x b The quantitative detection results corresponding to each element in; Based on the quantization detection result, a finally converged estimated signal vector for estimating the real transmitted signal x is determined.

7. The signal detection method according to claim 6, characterized in that: The signal vector x to be detected will undergo b random iterations b Perform constellation point quantization output and get x b The steps of quantifying the detection results corresponding to each element in include: Compare x b Each element in The Euclidean distance between each constellation point in the decoding candidate point set; Each element The corresponding constellation point with the smallest Euclidean distance is used as each element The corresponding quantitative test results.

8. The signal detection method according to claim 5, characterized in that: The step of determining the receiving end residual corresponding to the received signal of the receiving antenna comprises: Calculating a receiving-end residual corresponding to a received signal of the receiving antenna by a first preset calculation formula; Wherein, the first preset calculation formula is: C=y-Hx b-1 ; Where y is the corresponding received signal when the transmitted signal is x, H is the channel transfer matrix, Hx b-1 To send a signal x b-1 The corresponding received signal when C is the receiving end residual corresponding to the received signal, x b-1 is the signal vector to be detected after b-1 random iterations.

9. An electronic device, characterized in that: include: A memory, a processor, and a signal detection program stored in the memory and executable on the processor, wherein the signal detection program implements the signal detection method according to any one of claims 1 to 8 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a signal detection program, and when the signal detection program is executed by a processor, the signal detection method according to any one of claims 1 to 8 is implemented.

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