Signal detection method, device and medium based on multiple-input multiple-output system

Abstract

The present application provides a signal detection method, device and medium based on a multiple-input multiple-output system, which relates to the field of communication technology. The method includes: obtaining a channel coefficient matrix when the multiple-input multiple-output system transmits a target signal; using a first signal detection algorithm to detect a first transmission signal transmitted by at least part of the target column vectors in the channel coefficient matrix and output a first estimation vector, the first signal detection algorithm includes: a zero-forcing detection algorithm; using a second signal detection algorithm to detect a second transmission signal transmitted by other column vectors in the channel coefficient matrix except for part of the target column vectors and output a second estimation vector, the second signal detection algorithm includes: a maximum likelihood detection algorithm; based on the first estimation vector and the second estimation vector, determining the detection signal corresponding to the target signal, so as to fully utilize the advantages of the first signal detection algorithm and the second signal detection algorithm and reduce the search space complexity of the detection method.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a signal detection method, device, and medium based on a multiple-input multiple-output system. Background Art

[0002] Multiple-Input Multiple-Output (MIMO) technology is one of the core technologies of modern wireless communications. By using multiple antennas at the transmitting and receiving ends, it can significantly improve the capacity, reliability, and spectrum efficiency of communication systems.

[0003] In the prior art, when transmitting signals based on a MIMO system, the receiver needs to recover the original signal sent by the transmitter from the mixed received signal through a signal processing algorithm. The prior art generally uses Maximum Likelihood Detection (MLD) for detection.

[0004] However, since the detection principle of the MLD algorithm is to exhaustively enumerate all possible combinations of transmitted signals and select the candidate vector that maximizes the likelihood function of the received signal as the detection result, the existing detection method has the problem of high space complexity. Summary of the Invention

[0005] The purpose of this application is to address the deficiencies in the above-mentioned prior art and provide a signal detection method, device and medium based on a multiple-input multiple-output system, which can reduce the spatial complexity of the detection method.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0007] In a first aspect, the present invention provides a signal detection method based on a multiple-input multiple-output system, the method comprising:

[0008] Obtaining a channel coefficient matrix when a multiple-input multiple-output system transmits a target signal, wherein the channel coefficient matrix is ​​used to represent the complex channel gains between multiple receiving antennas and multiple transmitting antennas in the multiple-input multiple-output system;

[0009] Detecting a first transmission signal transmitted by at least part of the target column vector in the channel coefficient matrix using a first signal detection algorithm, and outputting a first estimation vector corresponding to the first transmission signal, where the first signal detection algorithm includes: a zero-forcing detection algorithm or a minimum mean square error detection algorithm;

[0010] Using a second signal detection algorithm to detect a second transmission signal transmitted by other column vectors in the channel coefficient matrix except the part of the target column vectors, and outputting a second estimation vector corresponding to the second transmission signal, the second signal detection algorithm including: a maximum likelihood detection algorithm;

[0011] A detection signal corresponding to the target signal is determined based on the first estimation vector and the second estimation vector.

[0012] In an optional embodiment, before detecting the first transmitted signal transmitted by at least part of the target column vectors in the channel coefficient matrix using a first signal detection algorithm and outputting a first estimation vector corresponding to the first transmitted signal, the method further includes:

[0013] Obtaining a pseudo-inverse matrix of the channel coefficient matrix;

[0014] Performing singular value decomposition on the pseudo-inverse matrix to determine a diagonal matrix corresponding to the pseudo-inverse matrix;

[0015] Based on the number of target column vectors, the target column vectors in the channel coefficient matrix are determined according to the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix.

[0016] In an optional embodiment, determining the target column vector in the channel coefficient matrix based on the number of target column vectors and according to each singular value in the diagonal matrix corresponding to the pseudo-inverse matrix includes:

[0017] Based on the number of target column vectors, and according to each singular value in the diagonal matrix corresponding to the pseudo-inverse matrix, based on a preset matrix condition number algorithm, a singular value corresponding to a condition number that meets a preset condition number requirement is screened from multiple singular values ​​and determined as a target singular value, wherein the number of the target singular values ​​is the same as the number of the target column vectors;

[0018] A target column vector in the channel coefficient matrix is ​​determined according to a target column index of the target singular value in the pseudo-inverse matrix.

[0019] In an optional embodiment, before detecting the first transmitted signal transmitted by at least part of the target column vectors in the channel coefficient matrix using a first signal detection algorithm and outputting a first estimation vector corresponding to the first transmitted signal, the method further includes:

[0020] Calculating the reciprocal of the modulus of each column vector in the channel coefficient matrix;

[0021] Based on the number of target column vectors, obtaining a target reciprocal whose reciprocal of the modulus of the column vector in the channel coefficient matrix meets a preset requirement, wherein the number of the target reciprocals is the same as the number of the target column vectors;

[0022] A target column vector in the channel coefficient matrix is ​​determined according to the column vector corresponding to the target inverse.

[0023] In an optional embodiment, determining the detection signal corresponding to the target signal based on the first estimation vector and the second estimation vector includes:

[0024] Acquire a target transmit sequence corresponding to the target signal based on the first estimation vector and the second estimation vector;

[0025] A detection signal corresponding to the target signal is determined according to a target transmission sequence corresponding to the target signal.

[0026] In an optional embodiment, acquiring a target transmit sequence corresponding to the target signal based on the first estimation vector and the second estimation vector includes:

[0027] Determining a first target constellation point corresponding to the first estimated vector based on a hard decision algorithm;

[0028] determining a first transmit sequence and a second transmit sequence respectively according to the first target constellation point and the second estimated vector;

[0029] A target transmit sequence corresponding to the target signal is determined according to the first transmit sequence and the second transmit sequence.

[0030] In an optional embodiment, determining the detection signal corresponding to the target signal based on the first estimation vector and the second estimation vector includes:

[0031] Determining, based on a soft decision algorithm and according to the first estimation vector, a confidence level of each bit in the first data stream corresponding to the first transmitted signal;

[0032] determining, according to the second estimation vector, a confidence level of each bit in a second data stream corresponding to the second transmitted signal;

[0033] A detection signal corresponding to the target signal is determined according to the confidence level of each bit in the first data stream corresponding to the first transmit signal and the confidence level of each bit in the second data stream corresponding to the second transmit signal.

[0034] In an optional embodiment, the method further comprises:

[0035] Obtaining a preset bit error rate requirement and a preset search space complexity requirement corresponding to when the multiple-input multiple-output system transmits a target signal;

[0036] The number of the target column vectors is determined according to the preset bit error rate requirement and the preset search space complexity requirement.

[0037] In a second aspect, the present invention provides a signal detection device based on a multiple-input multiple-output system, the signal detection device comprising:

[0038] an acquisition module, configured to acquire a channel coefficient matrix when a multiple-input multiple-output system transmits a target signal, wherein the channel coefficient matrix is ​​used to represent the complex channel gains between multiple receiving antennas and multiple transmitting antennas in the multiple-input multiple-output system;

[0039] A first detection module is configured to detect a first transmission signal transmitted by at least part of the target column vector in the channel coefficient matrix using a first signal detection algorithm, and output a first estimation vector corresponding to the first transmission signal, wherein the first signal detection algorithm includes a zero-forcing detection algorithm;

[0040] a second detection module, configured to detect a second transmission signal transmitted by other column vectors in the channel coefficient matrix except the part of the target column vectors using a second signal detection algorithm, and output a second estimation vector corresponding to the second transmission signal, wherein the second signal detection algorithm includes a maximum likelihood detection algorithm;

[0041] A determination module is used to determine a detection signal corresponding to the target signal based on the first estimation vector and the second estimation vector.

[0042] In an optional implementation, the first detection module is further configured to obtain a pseudo-inverse matrix of the channel coefficient matrix;

[0043] Performing singular value decomposition on the pseudo-inverse matrix to determine a diagonal matrix corresponding to the pseudo-inverse matrix;

[0044] Based on the number of target column vectors, the target column vectors in the channel coefficient matrix are determined according to the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix.

[0045] In an optional embodiment, the first detection module is further configured to, based on the number of target column vectors and the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix, screen and determine, based on a preset matrix condition number algorithm, from a plurality of singular values, corresponding singular values ​​whose condition numbers meet preset condition number requirements as target singular values, wherein the number of the target singular values ​​is the same as the number of the target column vectors;

[0046] A target column vector in the channel coefficient matrix is ​​determined according to a target column index of the target singular value in the pseudo-inverse matrix.

[0047] In an optional embodiment, the first detection module is further used to calculate the reciprocal of the modulus of each column vector in the channel coefficient matrix;

[0048] Based on the number of target column vectors, obtaining a target reciprocal whose reciprocal of the modulus of the column vector in the channel coefficient matrix meets a preset requirement, wherein the number of the target reciprocals is the same as the number of the target column vectors;

[0049] A target column vector in the channel coefficient matrix is ​​determined according to the column vector corresponding to the target inverse.

[0050] In an optional embodiment, the determining module is specifically configured to obtain a target transmit sequence corresponding to the target signal based on the first estimation vector and the second estimation vector;

[0051] A detection signal corresponding to the target signal is determined according to a target transmission sequence corresponding to the target signal.

[0052] In an optional implementation manner, the determining module is specifically configured to determine a first target constellation point corresponding to the first estimation vector based on a hard decision algorithm;

[0053] determining a first transmit sequence and a second transmit sequence respectively according to the first target constellation point and the second estimated vector;

[0054] A target transmit sequence corresponding to the target signal is determined according to the first transmit sequence and the second transmit sequence.

[0055] In an optional embodiment, the determining module is specifically configured to determine, based on a soft decision algorithm and according to the first estimation vector, the confidence of each bit in the first data stream corresponding to the first transmitted signal;

[0056] determining, according to the second estimation vector, a confidence level of each bit in a second data stream corresponding to the second transmitted signal;

[0057] A detection signal corresponding to the target signal is determined according to the confidence level of each bit in the first data stream corresponding to the first transmit signal and the confidence level of each bit in the second data stream corresponding to the second transmit signal.

[0058] In an optional embodiment, the acquisition module is further used to obtain a preset bit error rate requirement and a preset search space complexity requirement corresponding to the MIMO system transmitting the target signal;

[0059] The number of the target column vectors is determined according to the preset bit error rate requirement and the preset search space complexity requirement.

[0060] In a third aspect, the present invention provides an electronic device comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the signal detection method based on the multiple-input multiple-output system as described in any of the aforementioned embodiments.

[0061] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the signal detection method based on a multiple-input multiple-output system as described in any of the aforementioned embodiments are executed.

[0062] The beneficial effects of this application are:

[0063] In the signal detection method, device, and medium based on a multiple-input multiple-output system provided in the embodiments of the present application, a channel coefficient matrix is ​​obtained when the multiple-input multiple-output system transmits a target signal, wherein the channel coefficient matrix is ​​used to represent the complex channel gain between multiple receiving antennas and multiple transmitting antennas in the multiple-input multiple-output system; a first signal detection algorithm is used to detect a first transmitted signal transmitted by at least part of the target column vectors in the channel coefficient matrix, and a first estimation vector corresponding to the first transmitted signal is output. The first signal detection algorithm includes: a zero-forcing detection algorithm; a second signal detection algorithm is used to detect a second transmitted signal transmitted by other column vectors in the channel coefficient matrix except for some of the target column vectors, and a second estimation vector corresponding to the second transmitted signal is output. The second signal detection algorithm includes: a maximum likelihood detection algorithm; based on the first estimation vector and the second estimation vector, a detection signal corresponding to the target signal is determined. By applying the embodiments of the present application, the advantages of the first signal detection algorithm and the second signal detection algorithm can be fully utilized. Compared with the existing method of completely using the maximum likelihood detection algorithm for detection, the search space complexity of the detection method can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0065] Figure 1 A flowchart of a signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0066] Figure 2A flowchart of another signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0067] Figure 3 A flowchart of another signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0068] Figure 4 A flowchart of another signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0069] Figure 5 A flowchart of another signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0070] Figure 6 A schematic diagram showing the performance comparison between the HD-LC-MLD algorithm and other detection algorithms provided in the embodiments of the present application;

[0071] Figure 7 A flowchart of another signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0072] Figure 8 A flowchart of another signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0073] Figure 9 A schematic diagram showing the performance comparison between the SD-LC-MLD algorithm and other detection algorithms provided in the embodiments of the present application;

[0074] Figure 10 A flowchart of another signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0075] Figure 11 A schematic diagram of the functional modules of a signal detection device based on a multiple-input multiple-output system provided in an embodiment of the present application;

[0076] Figure 12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0077] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of 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. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0078] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0079] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0080] In the prior art, when transmitting signals based on a MIMO system, the receiver needs to recover the original signal sent by the transmitter from the mixed received signals through a signal processing algorithm. The prior art generally uses a Maximum Likelihood Detection (MLD) algorithm for detection.

[0081] Among them, the MLD algorithm is the optimal algorithm for MIMO detection. Its principle is to select a vector from the transmission space composed of all possible transmission vectors so that the Euclidean distance between the information obtained after passing through the same channel and the received signal is minimized. In other words, when the transmitted information is this vector, the probability of receiving the signal is the highest.

[0082] However, since the MLD algorithm detection principle is to exhaustively enumerate all possible combinations of transmitted signals and select the candidate vector that maximizes the received signal likelihood function as the detection result, as the modulation order increases and the MIMO scale increases, the complexity increases exponentially. Therefore, the existing detection method has the problem of high complexity.

[0083] In view of this, an embodiment of the present application provides a signal detection method based on a multiple-input multiple-output system. By applying this method, a first signal detection algorithm is used to detect the first transmitted signal transmitted by at least part of the target column vectors in the channel coefficient matrix, and a second signal detection algorithm is used to detect the second transmitted signal transmitted by other column vectors. The advantages of the first signal detection algorithm and the second signal detection algorithm can be fully utilized. Compared with the existing method of using the maximum likelihood detection algorithm for detection, the search space complexity of the signal detection can be effectively reduced.

[0084] Before introducing this application, a brief description of the MIMO system involved in this application is given first:

[0085] A Multiple-Input, Multiple-Output (MIMO) system significantly improves system performance by using multiple antennas at both ends of a communication link (the transmitter, Tx, and the receiver, Rx). At the transmitter, the data stream is segmented, encoded, and sent in a specific manner through different antennas. At the receiver, the multiple antennas receive the spatially mixed and superimposed signals from all transmitting antennas (plus noise and interference). The receiver then uses a pre-defined signal processing algorithm (such as maximum likelihood detection) to separate and decode these mixed signal streams.

[0086] The received signal model of the MIMO system can be expressed as:

[0087]

[0088] Where y represents the received signal vector obtained by the receiver, x represents the transmitted signal vector to be estimated (each element is taken from the modulation constellation, such as QPSK and 16-QAM), H represents the channel coefficient matrix, and n represents the additive white Gaussian noise vector.

[0089] Figure 1 A flow chart of a signal detection method based on a MIMO system provided in an embodiment of the present application is provided. The execution subject of the method may be a receiver in the MIMO system, such as Figure 1 As shown, the method includes:

[0090] S101. Obtain a channel coefficient matrix when a MIMO system transmits a target signal, wherein the channel coefficient matrix is ​​used to represent complex channel gains between multiple receiving antennas and multiple transmitting antennas in the MIMO system.

[0091] Different MIMO systems correspond to different channel coefficient matrices. The channel coefficient matrix can represent the complex channel gain between multiple receiving antennas and multiple transmitting antennas in the MIMO system. The complex channel gain can include the amplitude attenuation and phase offset experienced by the signal along the propagation path.

[0092] Among them, amplitude attenuation can be caused by path loss, shadow fading, and small-scale fading, which can reflect the degree of signal power attenuation; phase offset can be caused by propagation delay (causing carrier phase rotation) and multipath effect (phase superposition of signals on different paths).

[0093] For a better understanding of the present application, a multiple-input multiple-output system including Ntx transmitting antennas and Nrx receiving antennas is used as an example for explanation. The channel coefficient matrix H is an Nrx × Ntx complex matrix, where the number of rows Nrx of the channel coefficient matrix H corresponds to the number of receiving antennas in the multiple-input multiple-output system; and the number of columns Ntx of the channel coefficient matrix H corresponds to the number of transmitting antennas in the multiple-input multiple-output system.

[0094] In addition, it should be noted that for the channel coefficient matrix H, each column corresponds to a specific transmitting antenna Ntx, and the column index j (an integer from 1 to Ntx) represents the complex channel gain from the jth transmitting antenna to all receiving antennas.

[0095] Furthermore, it can be understood that since the wireless channel is dynamic, when transmitting a target signal, a channel coefficient matrix of the MIMO system should be obtained accordingly when transmitting the target signal.

[0096] Optionally, when obtaining the channel coefficient matrix when the MIMO system transmits the target signal, it can be obtained through least squares (LS), or it can be obtained through a statistical model (such as a Rayleigh model, a Rice model, etc.), which is not limited here.

[0097] S102: Detect a first transmission signal transmitted by at least part of the target column vectors in the channel coefficient matrix using a first signal detection algorithm, and output a first estimation vector corresponding to the first transmission signal.

[0098] S103 : Detect the second transmission signal transmitted by the other column vectors in the channel coefficient matrix except for some target column vectors using a second signal detection algorithm, and output a second estimation vector corresponding to the second transmission signal.

[0099] In which, the target column vector is at least part of the column vectors in the channel coefficient matrix. In some embodiments, the number of target column vectors can be set according to the preset signal-to-noise ratio and preset search space complexity requirements required when the multiple-input multiple-output system transmits the target signal, and the column index where the target column vector is located can be determined according to the preset channel selection rules.

[0100] Optionally, the search space complexity of the second signal detection algorithm is related to the modulation order and the number of transmit antennas in the multiple-input multiple-output system, and the first signal detection algorithm has no search space. Optionally, the first signal detection algorithm may be a zero-forcing detection algorithm (ZF), and the second signal detection algorithm may be a maximum likelihood detection algorithm (MLD).

[0101] The modulation order C can be determined based on the modulation method. For example, if the modulation method is binary phase shift keying (BPSK), the corresponding modulation order C = 2; if the modulation method is quadrature phase shift keying (QPSK), the corresponding modulation order C = 4; and if the modulation method is 16-order quadrature amplitude modulation (16QAM), the corresponding modulation order C = 16. Of course, it should be noted that this application does not limit the modulation method used in the target signal transmission process, and it can be flexibly set according to the actual application scenario.

[0102] The MLD algorithm detection principle is to exhaustively enumerate all possible combinations of transmitted signals and select the candidate vector that maximizes the likelihood function of the received signal as the detection result. Maximizing the likelihood function is equivalent to minimizing the Euclidean distance. Therefore, in the specific calculation, the detection formula corresponding to the MLD algorithm is:

[0103]

[0104] Where C represents the constellation point set of the transmitted signal, Ntx represents the number of transmitting antennas, y represents the received signal, x represents the transmitted signal to be estimated, and H represents the channel coefficient matrix. Represents the estimated vector of the transmitted signal determined using the MLD algorithm.

[0105] The detection formula corresponding to the ZF algorithm is:

[0106]

[0107] After ZF detection, the estimated vector corresponding to the transmitted signal can be restored using the following formula:

[0108]

[0109] Where x represents the transmitted signal to be estimated, H represents the channel coefficient matrix, and n represents the additive white Gaussian noise vector. represents the pseudo-inverse matrix of the channel coefficient matrix H.

[0110] Among them, comparing the zero-forcing detection algorithm and the maximum likelihood detection algorithm, it should be noted that, through experiments, it is found that if the number of transmitting antennas in the MIMO system is Ntx and the modulation order is C, then when the above-mentioned MLD algorithm is used for detection search of the MIMO system, the MLD algorithm needs to traverse all possible transmission vectors (the search space grows exponentially with Ntx), and the search space complexity corresponding to the MLD algorithm is ; The ZF algorithm directly decouples the signal through mathematical transformation (pseudo-inverse) rather than comparing candidate solutions. Therefore, there is no search space when using the zero-forcing detection algorithm to search for the MIMO system. Therefore, the search space complexity of the ZF algorithm is lower than that of the MLD algorithm. However, when the channel coefficient matrix of the ZF algorithm is ill-conditioned (large condition number), the pseudo-inverse will amplify the noise. Therefore, the bit error rate (BER) of the ZF algorithm is higher than that of the MLD algorithm, that is, the detection performance of the ZF algorithm is worse than that of the MLD algorithm.

[0111] Therefore, when detecting the target signal transmitted by the multiple-input multiple-output system, the respective advantages of the first signal detection algorithm and the second signal detection algorithm can be utilized, wherein the first transmission signal transmitted by at least part of the target column vectors in the channel coefficient matrix is ​​detected using the first signal detection algorithm, and a first estimation vector corresponding to the first transmission signal is output; the second transmission signal transmitted by other column vectors in the channel coefficient matrix except for part of the target column vectors is detected using the second signal detection algorithm, and a second estimation vector corresponding to the second transmission signal is output. Compared with the existing method of using MLD for detection, the overall detection search space complexity can be reduced and the detection performance can be guaranteed.

[0112] It should be noted that, if the number of columns of the target column vector is d and the modulation order is C, then in the embodiment of the present application, if d columns in the channel coefficient matrix are selected for ZF detection and the remaining nd columns are selected for MLD detection, compared with the existing method of using all MLD for detection, the complexity of MLD can be reduced from Reduced to , which can reduce Dimensional complexity.

[0113] S104: Determine a detection signal corresponding to the target signal based on the first estimation vector and the second estimation vector.

[0114] Among them, after obtaining the above-mentioned first estimation vector and second estimation vector, the detection signal corresponding to the target signal can be further determined based on this. Optionally, when making the specific determination, the hard decision algorithm or the soft decision algorithm can be combined to further determine the detection signal corresponding to the target signal.

[0115] In summary, an embodiment of the present application provides a signal detection method based on a multiple-input multiple-output (MIMO) system, the method including: obtaining a channel coefficient matrix when the MIMO system transmits a target signal, wherein the channel coefficient matrix is ​​used to characterize the complex channel gain between multiple receiving antennas and multiple transmitting antennas in the MIMO system; using a first signal detection algorithm to detect a first transmitted signal transmitted by at least part of the target column vectors in the channel coefficient matrix, and outputting a first estimation vector corresponding to the first transmitted signal, the first signal detection algorithm including: a zero-forcing detection algorithm; using a second signal detection algorithm to detect a second transmitted signal transmitted by other column vectors in the channel coefficient matrix except for part of the target column vectors, and outputting a second estimation vector corresponding to the second transmitted signal, the second signal detection algorithm including: a maximum likelihood detection algorithm; based on the first estimation vector and the second estimation vector, determining the detection signal corresponding to the target signal, by applying the embodiment of the present application, the advantages of the first signal detection algorithm and the second signal detection algorithm can be fully utilized, and compared with the existing method of completely using the maximum likelihood detection algorithm for detection, the search space complexity of the detection method can be reduced.

[0116] Figure 2 A flow chart of a signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application. In an optional embodiment, as Figure 2 As shown, the method further includes: detecting the first transmitted signal transmitted by at least part of the target column vector in the channel coefficient matrix using a first signal detection algorithm, and outputting a first estimation vector corresponding to the first transmitted signal before outputting the first estimation vector.

[0117] S201. Obtain a pseudo-inverse matrix of a channel coefficient matrix.

[0118] S202. Perform singular value decomposition on the pseudo-inverse matrix to determine a diagonal matrix corresponding to the pseudo-inverse matrix.

[0119] S203 : Based on the number of target column vectors and according to the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix, determine the target column vectors in the channel coefficient matrix.

[0120] Optionally, the target column vector may be determined as follows, wherein applying the maximum likelihood criterion (ML) to the result after the ZF test yields:

[0121]

[0122] Assuming that the receiver can obtain perfect channel estimation, the result of ZF detection can be further expressed as:

[0123]

[0124] Where C represents the constellation point set of the transmitted signal, Ntx represents the number of transmitting antennas, y represents the received signal, and x represents the transmitted signal to be estimated. represents the pseudo-inverse matrix of the channel coefficient matrix H, n represents the additive white Gaussian noise vector, represents the estimated vector of the transmitted signal determined by the ZF algorithm, and n represents the additive white Gaussian noise vector.

[0125] Assuming that the receiver can obtain perfect channel estimation, the result of MLD detection can be expressed as:

[0126]

[0127] Where C represents the constellation point set of the transmitted signal, Ntx represents the number of transmitting antennas, y represents the received signal, x represents the transmitted signal to be estimated, and n represents the additive white Gaussian noise vector. It represents the estimated vector of the transmitted signal determined by the MLD algorithm, and H represents the channel coefficient matrix.

[0128] It can be seen that whether the ZF test and the MLD test are equal depends on , where the channel coefficient matrix is ​​H, then we can refer to the formula Calculate the pseudo-inverse matrix of the channel coefficient matrix , represents the conjugate transpose.

[0129] The pseudo-inverse matrix of the channel coefficient matrix Performing singular value decomposition yields: ;

[0130]

[0131] in, represents the pseudo-inverse matrix The corresponding left singular matrix, represents the pseudo-inverse matrix The corresponding right singular matrix, represents the pseudo-inverse matrix The corresponding diagonal matrix.

[0132] From the expression of the diagonal matrix S, it can be seen that the diagonal matrix S can include 、 、…、 There are Ntx singular values ​​in total, and since the U and V matrices are unitary matrices, they actually rotate the n vector without affecting the value of its two norm. Therefore, the experiment also found that when When , the detection performance of ZF is the same as that of MLD. When the singular values ​​are not equal, then when The larger the condition number, the worse the performance of ZF detection.

[0133] Therefore, in some embodiments, based on the number d of target column vectors, according to the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix, d columns of target column vectors can be determined from the channel coefficient matrix through a preset matrix condition number algorithm, so that when detection is performed based on this method as described above, the detection performance of ZF can be guaranteed, and thus the detection performance of the overall detection method can be guaranteed.

[0134] Of course, it should be noted that the method for determining the target column vector is not limited to this.

[0135] Figure 3 A flow chart of a signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application. In an optional embodiment, as Figure 3 As shown, the above method, based on the number of target column vectors and according to the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix, determines the target column vector in the channel coefficient matrix, including:

[0136] S301. Based on the number of target column vectors and the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix, a preset matrix condition number algorithm is used to screen out the corresponding singular values ​​from multiple singular values ​​when the condition number meets the preset condition number requirement, and the number of target singular values ​​is the same as the number of target column vectors.

[0137] The preset condition number requirement may be a minimum condition number, or the condition number may be less than a preset condition number threshold, etc., which is not limited here.

[0138] Optionally, the minimum condition number can be calculated based on a preset matrix condition number algorithm by referring to the following calculation formula: ,in, represents the pseudo-inverse matrix The corresponding diagonal matrix, represents the largest singular value in the diagonal matrix S, represents the smallest singular value in the diagonal matrix S.

[0139] Based on the above description, it can be understood that if the number of target column vectors is d, the preset matrix condition number algorithm can be used to screen out multiple singular values ​​and determine that the condition numbers meet the preset condition number requirements. D singular values ​​can be regarded as target singular values.

[0140] S302 : Determine a target column vector in the channel coefficient matrix according to a target column index of the target singular value in the pseudo-inverse matrix.

[0141] Optionally, after determining the target singular value, the target column index of the target singular value in the pseudo-inverse matrix can be further obtained. Based on the target column index, the column vector corresponding to the target column index can be determined in the channel coefficient matrix as the target column vector.

[0142] By applying the embodiments of the present application, it is possible to determine the target column vector in the channel coefficient matrix according to a preset matrix condition number algorithm, which can effectively ensure the detection performance of ZF and thus ensure the detection performance of the overall detection method.

[0143] Of course, in some embodiments, the target column vector in the channel coefficient matrix may also be determined by referring to the following method.

[0144] Figure 4 A flow chart of a signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application. In an optional embodiment, as Figure 4 As shown, the method further includes: detecting the first transmitted signal transmitted by at least part of the target column vector in the channel coefficient matrix using a first signal detection algorithm, and outputting a first estimation vector corresponding to the first transmitted signal before outputting the first estimation vector.

[0145] S401. Calculate the reciprocal of the modulus of each column vector in the channel coefficient matrix.

[0146] S402. Based on the number of target column vectors, obtain a target inverse of the modulus of the column vector in the channel coefficient matrix that meets preset requirements, wherein the number of target inverses is the same as the number of target column vectors.

[0147] S403 : Determine a target column vector in the channel coefficient matrix according to the column vector corresponding to the target inverse.

[0148] Among them, when making specific determinations, the modulus of each column vector in the channel coefficient matrix can be calculated; based on the modulus of each column vector, the inverse of the modulus of each column vector can be determined; the inverses of the modulus of each column vector can be sorted, and based on the number d of target column vectors, d smaller inverses can be selected as target inverses; then at this time, d target column vectors can be determined in the channel coefficient matrix based on the column vectors corresponding to the target inverses.

[0149] It should be noted that the method for determining the target column vector is not limited to the above description. In some embodiments, the target column vector may also be determined by referring to the following method.

[0150] By applying the embodiments of the present application, it is possible to determine the target column vector in the channel coefficient matrix based on the inverse of the modulus of each column vector in the channel coefficient matrix. While ensuring the detection performance of ZF, since multiple methods for determining the target column vector are provided, the flexibility and applicability of the method of the present application can be improved.

[0151] Figure 5 A flow chart of a signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application. In an optional embodiment, as Figure 5 As shown, the above-mentioned determination of the detection signal corresponding to the target signal based on the first estimation vector and the second estimation vector includes:

[0152] S501: Acquire a target transmit sequence corresponding to a target signal based on a first estimation vector and a second estimation vector.

[0153] S502: Determine a detection signal corresponding to the target signal according to a target transmission sequence corresponding to the target signal.

[0154] Optionally, based on the first estimation vector, a first transmission sequence corresponding to the first estimation vector can be obtained based on a hard decision algorithm; for the second estimation vector, since the second estimation vector is obtained based on MLD, the corresponding second transmission sequence can be directly obtained according to the second estimation vector.

[0155] After obtaining the first transmission sequence and the second transmission sequence, a target transmission sequence corresponding to the target signal can be further obtained according to the first transmission sequence and the second transmission sequence, and a detection signal corresponding to the target transmission signal can be obtained accordingly.

[0156] It should be noted that, for the convenience of the following description, Figure 5 The algorithm in the embodiment is called a hard decision low complexity maximum likelihood detection (HD-LC-MLD) algorithm.

[0157] Figure 6 The performance comparison diagram of the HD-LC-MLD algorithm and other detection algorithms provided in the embodiment of the present application is shown in FIG. 4. The multiple-input multiple-output system is a 4×4 system (i.e., the transmitting antenna Ntx=4, Nrx=4), and the condition number of the channel coefficient matrix is ​​30dB. The bit error rate (BER) can be used to evaluate the performance of various detection algorithms. It can be seen that when the HD-LC-MLD algorithm detects target signals transmitted in a MIMO system, when the number of target column vectors d = 1, HD-LC-MLD (d = 1) and the method using only MLD detection (corresponding to the FULLML curve in the figure) have exactly the same bit error rate. When d = 2, if the condition number of the two selected target column vectors is large, the detection performance degrades significantly (i.e., the BER is large), as in the case of HD-LC-MLD (d = 2) being bad. If the condition number of the two selected target column vectors is small, the bit error rate approaches that of MLD, as in the case of HD-LC-MLD (d = 2). When d = 3, the conclusion is consistent with that for d = 2, but as d increases, the bit error rate also increases, gradually approaching the bit error rate of ZF detection (corresponding to the ZF curve in the figure). Combined with Table 1, it can be seen that as d increases, the search space complexity of the detection algorithm also decreases.

[0158] Figure 7 A flow chart of a signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application. In an optional embodiment, as Figure 7 As shown, the above-mentioned obtaining the target transmission sequence corresponding to the target signal based on the first estimation vector and the second estimation vector includes:

[0159] S701: Determine a first target constellation point corresponding to a first estimation vector based on a hard decision algorithm.

[0160] S702: Determine a first transmit sequence and a second transmit sequence according to the first target constellation point and the second estimated vector, respectively.

[0161] S703: Determine a target transmission sequence corresponding to the target signal according to the first transmission sequence and the second transmission sequence.

[0162] Among them, based on the first estimation vector, the first estimation vector output can be mapped to the target constellation diagram corresponding to the target modulation order based on a hard decision algorithm, and the constellation point with the closest Euclidean distance in the target constellation diagram is found as the first target constellation point corresponding to the first estimation vector; based on the first target constellation point, the corresponding first transmission sequence can be determined.

[0163] For the second estimated vector, since the second estimated vector is obtained based on MLD, the corresponding second target constellation point can be directly obtained according to the second estimated value. Optionally, the specific calculation can refer to the aforementioned formula. The principle is to traverse all possible constellation point sets in the Ntx-dimensional space and find the constellation point closest to the received signal y after channel mapping as the second target constellation point.

[0164] After obtaining the second target constellation point, the corresponding second transmit sequence can be determined according to the second target constellation point. Further, in combination with the first transmit sequence, the target transmit sequence corresponding to the target signal can be determined.

[0165] Figure 8 A flow chart of a signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application. In an optional embodiment, as Figure 8 As shown, in the above optional embodiment, the above determining the detection signal corresponding to the target signal based on the first estimation vector and the second estimation vector includes:

[0166] S801 : Determine, based on a soft decision algorithm and according to a first estimation vector, the confidence level of each bit in a first data stream corresponding to a first transmitted signal.

[0167] S802: Determine, according to the second estimation vector, the confidence level of each bit in the second data stream corresponding to the second transmitted signal.

[0168] S803 : Determine a detection signal corresponding to the target signal according to the confidence level of each bit in the first data stream corresponding to the first transmitted signal and the confidence level of each bit in the second data stream corresponding to the second transmitted signal.

[0169] Among them, after obtaining the first estimation vector, the confidence of each bit in the first data stream corresponding to the first transmitted signal can be obtained through a preset soft decision algorithm. The confidence can characterize the reliability of each bit being "0" or "1". The confidence can be expressed in the form of a log-likelihood ratio (LLR). Its physical meaning is the logarithmic ratio of the probability that the bit is 1 to the probability that the bit is 0. For specific calculations, please refer to the relevant algorithm description of the preset soft decision algorithm, which will not be repeated here.

[0170] For the second estimated vector, it can be understood that since it is output by the MLD algorithm, optionally, during the specific calculation, the Euclidean distance determined during the calculation process of the MLD algorithm can be used. According to the conversion rules between the Euclidean distance and the confidence of the bit, the confidence of each bit in the second data stream corresponding to the second estimated vector can be calculated.

[0171] Based on the above description, it can be understood that after respectively obtaining the confidence of each bit in the first data stream corresponding to the first transmission signal and the confidence of each bit in the second data stream corresponding to the second transmission signal, the detection signal corresponding to the target signal can be determined accordingly.

[0172] It should be noted that, for the convenience of the following description, Figure 8 The algorithm in the embodiment is called a soft decision low complexity maximum likelihood detection (SD-LC-MLD) algorithm.

[0173] Figure 9 This is a performance comparison diagram of the SD-LC-MLD algorithm and other detection algorithms provided in the embodiment of this application. Figure 9 As shown in the figure, taking a 4×4 MIMO system (i.e., 4 transmitting antennas Ntx=4, Nrx=4) and a channel coefficient matrix condition number of 30dB as an example, it can be seen that the bit error rate of ZF detection is higher. When the number of target column vectors d=1, SD-LC-MLD (d=1) and traditional soft-decision-based MLD have exactly the same bit error rate. When d=2, if the condition numbers of the two selected columns are small, the bit error rate approaches that of traditional soft-decision-based MLD.

[0174] In addition, it should be noted that although the above embodiment is aimed at a 4×4 multiple-input multiple-output system, it should be noted that experiments have also found that regardless of the MIMO scale, HD-LC-MLD and SD-LC-MLD can achieve performance that is completely consistent with traditional MLD when ZF detection is used when d=1. Therefore, when d=1, the application of the embodiment of the present application can obtain better detection performance while reducing the complexity of the search space.

[0175] Figure 10 A flow chart of a signal detection method based on a multiple-input multiple-output system provided in an embodiment of the present application. In an optional embodiment, as Figure 10 As shown, the above method also includes:

[0176] S1001: Obtain a preset bit error rate requirement and a preset search space complexity requirement corresponding to a target signal transmitted by a multiple-input multiple-output system.

[0177] S1002: Determine the number of target column vectors according to a preset bit error rate requirement and a preset search space complexity requirement.

[0178] Among them, the signal-to-noise ratio (SNR) of the MIMO system can represent the ratio of the total transmission power to the total noise power in the MIMO system. The bit error rate (BER) of the MIMO system can represent the probability of bit errors during the transmission process of the MIMO system. The lower the bit error rate, the better the MIMO system's anti-interference and anti-fading capabilities, and the better its performance.

[0179] Table 1

[0180]

[0181] Table 1 is a comparison table of the complexity dimensions corresponding to the various detection algorithms provided in the embodiments of the present application. Wherein, C represents the modulation order. It can be seen from Table 1 that for a 2x2 MIMO system, when the number of target column vectors d=1, the search space complexity corresponding to detection using SD-LC-MLD and HD-LC-MLD is less than that of the existing MLD algorithm. When the number of target column vectors d=1 and d=2, the search space complexity corresponding to detection using SD-LC-MLD and HD-LC-MLD is also less than that of the existing MLD algorithm. Moreover, when the number of target column vectors d=2, the search space complexity corresponding to detection using SD-LC-MLD and HD-LC-MLD is less than that when the number of target column vectors d=1.

[0182] Based on the above description, it can be understood that in some embodiments, when setting the number of target column vectors, it can be set according to the preset bit error rate requirements and preset search space complexity requirements corresponding to the target signal transmitted by the multiple-input multiple-output system, so that while meeting the preset bit error rate requirements and the preset search space complexity requirements, the search space complexity of signal detection can also be reduced.

[0183] Optionally, the first signal detection algorithm may also be a minimum mean square error detection algorithm. The minimum mean square error detection algorithm (MMSE) can minimize the mean square error between the estimated signal and the actual transmitted signal by simultaneously considering the statistical characteristics of channel interference and noise. The calculation formula for MMSE detection is:

[0184]

[0185] in, represents the noise power spectral density of the noise vector n corresponding to the MIMO system, H represents the channel coefficient matrix, represents the conjugate transpose of the channel coefficient matrix, represents the estimated vector of the transmitted signal determined by the MMSE algorithm, Represents the identity matrix, which is used to prevent noise amplification when the matrix is ​​inverted.

[0186] It should be noted that for the MMSE detection formula and the ZF detection formula, it can be seen that MMSE introduces a regularization term based on ZF. By balancing channel inversion and noise suppression, performance degradation in high-noise scenarios can be avoided. Therefore, the first signal detection algorithm can be flexibly determined based on the actual application scenario. Furthermore, if MMSE detection is required, please refer to the relevant section on the ZF detection algorithm above and will not be further elaborated here.

[0187] Figure 11 This is a functional module diagram of a signal detection device based on a multiple-input multiple-output system provided in an embodiment of the present application. The basic principle and technical effects of the device are the same as those of the corresponding method embodiment described above. For the sake of brief description, the parts not mentioned in this embodiment can be referred to the corresponding contents in the method embodiment. Figure 11 As shown, the signal detection device 100 includes:

[0188] An acquisition module 110 is configured to acquire a channel coefficient matrix when a multiple-input multiple-output system transmits a target signal, wherein the channel coefficient matrix is ​​used to represent the complex channel gains between multiple receiving antennas and multiple transmitting antennas in the multiple-input multiple-output system;

[0189] A first detection module 120 is configured to detect a first transmission signal transmitted by at least part of the target column vector in the channel coefficient matrix using a first signal detection algorithm, and output a first estimation vector corresponding to the first transmission signal, wherein the first signal detection algorithm includes a zero-forcing detection algorithm;

[0190] A second detection module 130 is configured to detect a second transmission signal transmitted by other column vectors in the channel coefficient matrix except the part of the target column vectors using a second signal detection algorithm, and output a second estimation vector corresponding to the second transmission signal, wherein the second signal detection algorithm includes a maximum likelihood detection algorithm;

[0191] The determination module 140 is configured to determine a detection signal corresponding to the target signal based on the first estimation vector and the second estimation vector.

[0192] In an optional embodiment, the first detection module 120 is further configured to obtain a pseudo-inverse matrix of the channel coefficient matrix;

[0193] Performing singular value decomposition on the pseudo-inverse matrix to determine a diagonal matrix corresponding to the pseudo-inverse matrix;

[0194] Based on the number of target column vectors, the target column vectors in the channel coefficient matrix are determined according to the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix.

[0195] In an optional embodiment, the first detection module 120 is further configured to, based on the number of target column vectors and the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix, screen and determine, based on a preset matrix condition number algorithm, from a plurality of singular values ​​a corresponding singular value whose condition number meets a preset condition number requirement, as a target singular value, wherein the number of the target singular values ​​is the same as the number of the target column vectors;

[0196] A target column vector in the channel coefficient matrix is ​​determined according to a target column index of the target singular value in the pseudo-inverse matrix.

[0197] In an optional embodiment, the first detection module 120 is further configured to calculate the reciprocal of the modulus of each column vector in the channel coefficient matrix;

[0198] Based on the number of target column vectors, obtaining a target reciprocal whose reciprocal of the modulus of the column vector in the channel coefficient matrix meets a preset requirement, wherein the number of the target reciprocals is the same as the number of the target column vectors;

[0199] A target column vector in the channel coefficient matrix is ​​determined according to the column vector corresponding to the target inverse.

[0200] In an optional embodiment, the determining module 140 is specifically configured to obtain a target transmit sequence corresponding to the target signal based on the first estimation vector and the second estimation vector;

[0201] A detection signal corresponding to the target signal is determined according to a target transmission sequence corresponding to the target signal.

[0202] In an optional implementation manner, the determining module 140 is specifically configured to determine a first target constellation point corresponding to the first estimation vector based on a hard decision algorithm;

[0203] determining a first transmit sequence and a second transmit sequence respectively according to the first target constellation point and the second estimated vector;

[0204] A target transmit sequence corresponding to the target signal is determined according to the first transmit sequence and the second transmit sequence.

[0205] In an optional embodiment, the determining module 140 is specifically configured to determine, based on a soft decision algorithm and according to the first estimation vector, the confidence of each bit in the first data stream corresponding to the first transmitted signal;

[0206] determining, according to the second estimation vector, a confidence level of each bit in a second data stream corresponding to the second transmitted signal;

[0207] A detection signal corresponding to the target signal is determined according to the confidence level of each bit in the first data stream corresponding to the first transmit signal and the confidence level of each bit in the second data stream corresponding to the second transmit signal.

[0208] In an optional embodiment, the acquisition module 110 is further configured to acquire a preset bit error rate requirement and a preset search space complexity requirement corresponding to the MIMO system transmitting the target signal;

[0209] The number of the target column vectors is determined according to the preset bit error rate requirement and the preset search space complexity requirement.

[0210] The above-mentioned device is used to execute the method provided in the above-mentioned embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0211] The above modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more microprocessors, or one or more field programmable gate arrays (FPGAs). For example, when a module is implemented by scheduling program code through a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0212] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device can be integrated into a receiver of a multiple-input multiple-output system. Figure 12 As shown, the electronic device may include: a processor 210, a storage medium 220, and a bus 230. The storage medium 220 stores machine-readable instructions executable by the processor 210. When the electronic device is running, the processor 210 and the storage medium 220 communicate via the bus 230, and the processor 210 executes the machine-readable instructions to perform the steps of the above-mentioned method embodiment. The specific implementation methods and technical effects are similar and will not be repeated here.

[0213] Optionally, the present application further provides a storage medium storing a computer program, which, when executed by a processor, executes the steps of the above method embodiment. The specific implementation and technical effects are similar and will not be described in detail here.

[0214] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0215] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0216] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0217] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor (English: processor) to perform some steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard drives, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), magnetic disks or optical disks, and other media that can store program code.

[0218] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0219] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application. It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A signal detection method based on a multiple-input multiple-output system, characterized in that: The method comprises: Obtaining a channel coefficient matrix when a multiple-input multiple-output system transmits a target signal, wherein the channel coefficient matrix is ​​used to represent the complex channel gains between multiple receiving antennas and multiple transmitting antennas in the multiple-input multiple-output system; Detecting a first transmission signal transmitted by at least part of the target column vector in the channel coefficient matrix using a first signal detection algorithm, and outputting a first estimation vector corresponding to the first transmission signal, where the first signal detection algorithm includes: a zero-forcing detection algorithm or a minimum mean square error detection algorithm; Using a second signal detection algorithm to detect a second transmission signal transmitted by other column vectors in the channel coefficient matrix except the part of the target column vectors, and outputting a second estimation vector corresponding to the second transmission signal, the second signal detection algorithm including: a maximum likelihood detection algorithm; determining a detection signal corresponding to the target signal based on the first estimation vector and the second estimation vector; Before detecting the first transmitted signal transmitted by at least part of the target column vectors in the channel coefficient matrix using a first signal detection algorithm and outputting a first estimation vector corresponding to the first transmitted signal, the method further includes: Obtaining a pseudo-inverse matrix of the channel coefficient matrix; performing singular value decomposition on the pseudo-inverse matrix to determine a diagonal matrix corresponding to the pseudo-inverse matrix; and determining a target column vector in the channel coefficient matrix based on the number of target column vectors and the singular values ​​in the diagonal matrix corresponding to the pseudo-inverse matrix; or, Calculate the inverse of the modulus of each column vector in the channel coefficient matrix; based on the number of target column vectors, obtain a target inverse whose modulus of the column vector in the channel coefficient matrix meets preset requirements, wherein the number of the target inverses is the same as the value of the number of the target column vectors; determine the target column vector in the channel coefficient matrix according to the column vector corresponding to the target inverse.

2. The method according to claim 1, characterized in that The determining, based on the number of target column vectors and according to each singular value in a diagonal matrix corresponding to the pseudo-inverse matrix, a target column vector in the channel coefficient matrix includes: Based on the number of target column vectors, and according to each singular value in the diagonal matrix corresponding to the pseudo-inverse matrix, based on a preset matrix condition number algorithm, a singular value corresponding to a condition number that meets a preset condition number requirement is screened from multiple singular values ​​and determined as a target singular value, wherein the number of the target singular values ​​is the same as the number of the target column vectors; A target column vector in the channel coefficient matrix is ​​determined according to a target column index of the target singular value in the pseudo-inverse matrix.

3. The method according to claim 1, characterized in that The determining, based on the first estimation vector and the second estimation vector, a detection signal corresponding to the target signal includes: Acquire a target transmit sequence corresponding to the target signal based on the first estimation vector and the second estimation vector; A detection signal corresponding to the target signal is determined according to a target transmission sequence corresponding to the target signal.

4. The method according to claim 3, characterized in that The acquiring, based on the first estimation vector and the second estimation vector, a target transmit sequence corresponding to the target signal includes: Determining a first target constellation point corresponding to the first estimated vector based on a hard decision algorithm; determining a first transmit sequence and a second transmit sequence respectively according to the first target constellation point and the second estimated vector; A target transmit sequence corresponding to the target signal is determined according to the first transmit sequence and the second transmit sequence.

5. The method according to claim 1, wherein The determining, based on the first estimation vector and the second estimation vector, a detection signal corresponding to the target signal includes: Determining, based on a soft decision algorithm and according to the first estimation vector, a confidence level of each bit in the first data stream corresponding to the first transmitted signal; determining, according to the second estimation vector, a confidence level of each bit in a second data stream corresponding to the second transmitted signal; A detection signal corresponding to the target signal is determined according to the confidence level of each bit in the first data stream corresponding to the first transmit signal and the confidence level of each bit in the second data stream corresponding to the second transmit signal.

6. The method according to claim 1, characterized in that The method further comprises: Obtaining a preset bit error rate requirement and a preset search space complexity requirement corresponding to when the multiple-input multiple-output system transmits a target signal; The number of the target column vectors is determined according to the preset bit error rate requirement and the preset search space complexity requirement.

7. An electronic device, characterized in that: include: A processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the signal detection method based on a multiple-input multiple-output system as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the signal detection method based on a MIMO system according to any one of claims 1 to 6 are executed.

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