阅读说明：本技术 一种mmse检测方法、装置、设备和存储介质 (MMSE detection method, device, equipment and storage medium ) 是由 张川 王辉征 杨敏华 黄永明 尤肖虎 于 2021-08-13 设计创作，主要内容包括：本发明公开了一种MMSE检测方法、装置、设备和存储介质,根据信道矩阵和噪声方差计算Gram矩阵,根据每个用户端分配的天线数目从Gram矩阵中提取若干个子块矩阵,并得到非块对角矩阵；对所述子块矩阵分别求逆得到子块矩阵的逆矩阵,将所述子块矩阵的逆矩阵沿着对角线组合得到块对角矩阵的逆矩阵；根据所述块对角矩阵的逆矩阵、非块对角矩阵、接收信号和信道矩阵,通过Neumann迭代的方式得到MMSE中的发送向量的估计值；根据发送向量的估计值进行判决,得到发送信号的检测值。本发明提出的MMSE检测方法既能具有较好的检测性能,又能具有较低的复杂度。(The invention discloses a method, a device, equipment and a storage medium for MMSE detection.A Gram matrix is calculated according to a channel matrix and noise variance, a plurality of sub-block matrixes are extracted from the Gram matrix according to the number of antennas distributed by each user side, and a non-block diagonal matrix is obtained; respectively inverting the subblock matrixes to obtain inverse matrixes of the subblock matrixes, and combining the inverse matrixes of the subblock matrixes along a diagonal to obtain inverse matrixes of block diagonal matrixes; obtaining an estimated value of a transmission vector in MMSE (minimum mean square error) in a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix; and judging according to the estimated value of the transmission vector to obtain the detection value of the transmission signal. The MMSE detection method provided by the invention not only has better detection performance, but also has lower complexity.)

1. An MMSE detection method, comprising: the method comprises the following steps:

acquiring a received signal, the number of antennas distributed by each user side, a channel matrix and a noise variance;

calculating a Gram matrix according to the channel matrix and the noise variance, extracting a plurality of sub-block matrixes from the Gram matrix according to the number of antennas distributed by each user side, and obtaining a non-block diagonal matrix; respectively inverting the subblock matrixes to obtain inverse matrixes of the subblock matrixes, and combining the inverse matrixes of the subblock matrixes along a diagonal to obtain inverse matrixes of block diagonal matrixes;

obtaining an estimated value of a transmission vector in MMSE (minimum mean square error) in a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix; and judging according to the estimated value of the transmission vector to obtain the detection value of the transmission signal.

2. An MMSE detection method according to claim 1, characterised in that: the extracting a plurality of sub-block matrixes from the Gram matrix according to the number of antennas distributed by each user side and obtaining a non-block diagonal matrix comprises the following steps:

sequentially extracting m subblock matrixes from a main diagonal direction in the Gram matrix, wherein the subblock matrixes have the sizes as follows: m is_UE×m_UEM is the number of users at the transmitting end, m_UEThe number of antennas for each user terminal;

and removing the sub-block matrix from the Gram matrix to obtain a non-block diagonal matrix.

3. An MMSE detection method according to claim 1, characterised in that: the method for inverting the subblock matrix comprises the following steps: cholesky algorithm.

4. An MMSE detection method according to claim 1, characterised in that: obtaining an estimated value of a transmission vector in MMSE through a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix, wherein the estimated value comprises the following steps:

performing iterative computation on an estimated transmission vector in a Neumann iterative mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix, and taking an estimated value when the maximum iterative times is reached as an estimated value of the transmission vector;

and substituting the inverse matrix of the Gram matrix into a calculation formula for estimating a transmission vector in MMSE to obtain an estimated transmission vector in the Neumann iteration mode.

5. An MMSE detection method according to claim 1, characterised in that: judging according to the estimated value of the transmission vector to obtain a detection value of a transmission signal, wherein the judgment comprises the following steps:

and finding a group of symbols closest to the Euclidean distance of the estimated value of the transmission vector in a symbol set of a constellation diagram, and taking a vector formed by the group of symbols as a detection value of the transmission signal.

6. An MMSE detection apparatus, comprising: the method comprises the following steps:

the acquisition module is used for acquiring the received signals, the number of antennas distributed by each user side, a channel matrix and a noise variance;

the matrix diagonal blocking module is used for calculating a Gram matrix according to the channel matrix and the noise variance, extracting a plurality of sub-block matrixes from the Gram matrix according to the number of antennas distributed by each user side, and obtaining a non-block diagonal matrix; obtaining inverse matrixes of the subblock matrixes by respectively inverting the subblock matrixes, and combining the inverse matrixes of the subblock matrixes along a diagonal line to obtain an inverse matrix of a block diagonal matrix;

a detection value iteration determination module for obtaining an estimation value of a transmission vector in MMSE by a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix; and judging according to the estimated value of the transmission vector to obtain the detection value of the transmission signal.

7. An MMSE detection arrangement according to claim 6, characterised in that: the extracting a plurality of sub-block matrixes from the Gram matrix according to the number of antennas distributed by each user side and obtaining a non-block diagonal matrix comprises the following steps:

sequentially extracting m subblock matrixes from a main diagonal direction in the Gram matrix, wherein the subblock matrixes have the sizes as follows: m is_UE×m_UEM is the number of users at the transmitting end, m_UEThe number of antennas for each user terminal;

and removing the sub-block matrix from the Gram matrix to obtain a non-block diagonal matrix.

8. An MMSE detection arrangement according to claim 6, characterised in that: the method for inverting the subblock matrix comprises the following steps: cholesky algorithm.

9. An MMSE detection arrangement according to claim 6, characterised in that: obtaining an estimated value of a transmission vector in MMSE through a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix, wherein the estimated value comprises the following steps:

performing iterative computation on an estimated transmission vector in a Neumann iterative mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix, and taking an estimated value when the maximum iterative times is reached as an estimated value of the transmission vector;

and substituting the inverse matrix of the Gram matrix into a calculation formula for estimating a transmission vector in MMSE to obtain an estimated transmission vector in the Neumann iteration mode.

10. An MMSE detection arrangement according to claim 6, characterised in that: judging according to the estimated value of the transmission vector to obtain a detection value of a transmission signal, wherein the judgment comprises the following steps:

and finding a group of symbols closest to the Euclidean distance of the estimated value of the transmission vector in a symbol set of a constellation diagram, and taking a vector formed by the group of symbols as a detection value of the transmission signal.

11. An MMSE detection apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the MMSE detection method of any one of claims 1-5 when executing the program.

12. A computer-readable storage medium having stored thereon computer-executable instructions for performing the MMSE detection method of any one of claims 1-5.

Technical Field

The present invention belongs to the technical field of channel estimation, and in particular, to a MMSE detection method, apparatus, device and storage medium.

Background

As a key technology for mobile communication development, a multiple-input multiple-output (MIMO) technology can improve network capacity, enhance network robustness, reduce network communication delay, and the like. However, as the number of antennas increases, the baseband processing complexity also increases dramatically. The computation complexity of a conventional optimal detection algorithm, such as a maximum-likelihood (ML) detector, increases exponentially with the number of antennas, which hinders practical applications thereof. However, a suboptimal linear detector, such as a minimum mean square error detector (MMSE), can better balance between computational complexity and detection performance, and thus has been widely used in practice. However, the computational complexity is still in the order of the third power of the number of antennas at the transmitting end, and in the context of the fifth generation mobile communication key technology, especially-scale mimo (massive mimo), the computational cost is still large.

For this reason, Michael Wu et al propose a method of utilizing a Neumann-series approximation (NSA) iteration to reduce the computation cost of large-scale matrix inversion in MMSE detection (referred to as NSA algorithm for short) based on the condition of channel hardening (channel hardening), but it can only achieve better detection performance under the condition of a large antenna load ρ (ρ ═ number of receiving antennas/number of transmitting antennas). To solve this problem, Prabhu et al propose a Neumann series iteration algorithm based on a tri-diagonal matrix, which essentially improves detection performance by including more channel matrix elements in the diagonal matrix compared to the original Neumann iteration. However, the disadvantage of this algorithm is that after the number of iterations exceeds 3, the computational complexity rises to the order of 3 times the number of antennas. In order to reduce the complexity of detection under the condition of ensuring the detection performance, Haijian Wu et al further proposes a matrix-blocking-based Neumann series iterative algorithm, which divides the Neumann iteration into two layers by means of matrix blocking, the second layer of iteration adopts the traditional diagonal Neumann iteration to calculate the inverse matrix of the sub-matrix required by the first layer of iteration, and further, the first layer of iteration carries out Neumann iteration in which one sub-matrix is a block matrix. The method can enable the diagonal matrix to contain more channel matrix elements, improve the iterative convergence rate, reduce the multiplication times required by multiplication among the matrixes during each iteration, effectively reduce the calculation complexity and better balance the calculation complexity and the detection performance.

However, the common disadvantages of the above existing algorithms are that the assumed channel conditions are too ideal (both are ideal rayleigh fading channels), and in some more practical system models, for example, under the conditions of a multi-user multi-antenna correlation system, the detection algorithm is almost ineffective, the error rate is high, and the performance of the detection method is poor, that is, the existing algorithms are not robust. An MMSE detection method needs to be researched, which not only has higher detection performance under a correlation channel, but also can reduce the complexity of the MMSE method.

Disclosure of Invention

The purpose of the invention is as follows: aiming at the problems in the prior art, the invention discloses an MMSE detection method, an MMSE detection device, MMSE detection equipment and a storage medium, which solve the problem that the existing MMSE detection method based on Neumann iteration has poor detection performance under a correlation channel.

The technical scheme is as follows: in order to achieve the purpose, the invention adopts the following technical scheme: an MMSE detection method, comprising:

acquiring a received signal, the number of antennas distributed by each user side, a channel matrix and a noise variance;

calculating a Gram matrix according to the channel matrix and the noise variance, extracting a plurality of sub-block matrixes from the Gram matrix according to the number of antennas distributed by each user side, and obtaining a non-block diagonal matrix; respectively inverting the subblock matrixes to obtain inverse matrixes of the subblock matrixes, and combining the inverse matrixes of the subblock matrixes along a diagonal to obtain inverse matrixes of block diagonal matrixes;

obtaining an estimated value of a transmission vector in MMSE (minimum mean square error) in a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix;

and judging according to the estimated value of the transmission vector to obtain the detection value of the transmission signal.

Further, the extracting a plurality of sub-block matrices from the Gram matrix according to the number of antennas allocated to each user side and obtaining a non-block diagonal matrix includes:

sequentially extracting m subblock matrixes from a main diagonal direction in the Gram matrix, wherein the subblock matrixes have the sizes as follows: m is_UE×m_UEM is the number of users at the transmitting end, m_UEThe number of antennas for each user terminal;

and removing the sub-block matrix from the Gram matrix to obtain a non-block diagonal matrix.

Further, the method for inverting the subblock matrix comprises: cholesky algorithm.

Further, obtaining an estimated value of a transmission vector in MMSE by a Neumann iteration method according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal, and the channel matrix, includes:

performing iterative computation on an estimated transmission vector in a Neumann iterative mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix, and taking an estimated value when the maximum iterative times is reached as an estimated value of the transmission vector;

and substituting the inverse matrix of the Gram matrix into a calculation formula for estimating a transmission vector in MMSE to obtain an estimated transmission vector in the Neumann iteration mode.

Further, the determining according to the estimated value of the transmission vector to obtain a detection value of the transmission signal includes:

and finding a group of symbols closest to the Euclidean distance of the estimated value of the transmission vector in a symbol set of a constellation diagram, and taking a vector formed by the group of symbols as a detection value of the transmission signal.

An MMSE detection apparatus, comprising:

the acquisition module is used for acquiring the received signals, the number of antennas distributed by each user side, a channel matrix and a noise variance;

the matrix diagonal blocking module is used for calculating a Gram matrix according to the channel matrix and the noise variance, extracting a plurality of sub-block matrixes from the Gram matrix according to the number of antennas distributed by each user side, and obtaining a non-block diagonal matrix; obtaining inverse matrixes of the subblock matrixes by respectively inverting the subblock matrixes, and combining the inverse matrixes of the subblock matrixes along a diagonal line to obtain an inverse matrix of a block diagonal matrix;

a detection value iteration determination module for obtaining an estimation value of a transmission vector in MMSE by a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix; and judging according to the estimated value of the transmission vector to obtain the detection value of the transmission signal.

Further, the extracting a plurality of sub-block matrices from the Gram matrix according to the number of antennas allocated to each user side and obtaining a non-block diagonal matrix includes:

sequentially extracting m subblock matrixes from a main diagonal direction in the Gram matrix, wherein the subblock matrixes have the sizes as follows: m is_UE×m_UEM is the number of users at the transmitting end, m_UEThe number of antennas for each user terminal;

and removing the sub-block matrix from the Gram matrix to obtain a non-block diagonal matrix.

Further, the method for inverting the subblock matrix comprises: cholesky algorithm.

Further, obtaining an estimated value of a transmission vector in MMSE by a Neumann iteration method according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal, and the channel matrix, includes:

performing iterative computation on an estimated transmission vector in a Neumann iterative mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix, and taking an estimated value when the maximum iterative times is reached as an estimated value of the transmission vector;

and substituting the inverse matrix of the Gram matrix into a calculation formula for estimating a transmission vector in MMSE to obtain an estimated transmission vector in the Neumann iteration mode.

Further, the determining according to the estimated value of the transmission vector to obtain a detection value of the transmission signal includes:

and finding a group of symbols closest to the Euclidean distance of the estimated value of the transmission vector in a symbol set of a constellation diagram, and taking a vector formed by the group of symbols as a detection value of the transmission signal.

An MMSE detection apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements any one of the MMSE detection methods described above when executing the program.

A computer-readable storage medium storing computer-executable instructions for performing any of the MMSE detection methods described above.

Compared with the prior art, the invention has the following advantages and beneficial effects:

the invention carries out diagonal blocking on the Gram matrix, extracts a subblock matrix containing the main characteristics of the channel, inverts the subblock matrix, and combines to obtain the inverse matrix of the block diagonal matrix. Because the size of the sub-block matrix is usually smaller than that of the Gram matrix, the invention can greatly reserve and utilize channel information and simultaneously reduce the matrix inversion complexity to obtain better detection performance;

according to the inverse matrix of the block diagonal matrix, the inverse of the Gram matrix is approximately obtained in a Neumann iteration mode, and therefore the calculation complexity of the estimated value of the transmission vector in the MMSE is reduced;

the MMSE detection method provided by the invention not only has better detection performance, but also has lower complexity, and particularly has better detection performance under a correlation channel.

Drawings

FIG. 1 is a diagram of a correlation channel model of a multi-user multi-antenna system according to the present invention;

fig. 2 is a schematic diagram of amplitude characteristics of a Gram matrix in massive MIMO under different correlation conditions in the embodiment of the present invention;

FIG. 3 is a flow chart of an MMSE detection method in an embodiment of the invention;

FIG. 4 is a schematic diagram of sub-block matrix extraction according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of the inverse combination and extraction of the subblock matrix in the embodiment of the invention;

fig. 6 is a performance graph of the BD-NSA detection method and other methods according to the embodiment of the present invention, where L represents the number of iterations, when the antenna is configured to be 128 × 16, and the BER varies with the SNR;

FIG. 7 shows a BD-NSA detection method and other detection methods in an antenna configuration of 128 × 16, m according to an embodiment of the present invention_UEReceiving end antenna correlation coefficient ζ of 4_r0.2, antenna correlation coefficient ζ at transmitting end_tPerformance plot of BER as a function of SNR, where L denotes the number of iterations, for the case of 0.4;

FIG. 8 shows a BD-NSA detection method and other detection methods in an antenna configuration of 128 × 16, m according to an embodiment of the present invention_UEReceiving end antenna correlation coefficient ζ of 4_r0.2, antenna correlation coefficient ζ at transmitting end_tPerformance plot of BER as a function of SNR, where L denotes the number of iterations, for the case of 0.5;

FIG. 9 shows a BD-NSA detection algorithm and other detection algorithms in an antenna configuration of 128 × 16, m according to an embodiment of the present invention_UEReceiving end antenna correlation coefficient ζ of 4_r0.2, antenna correlation coefficient ζ at transmitting end_tA comparison graph of performance and complexity for the case of 0.4;

FIG. 10 shows a BD-NSA detection algorithm and other detection algorithms in an antenna configuration of 128 × 16, m according to an embodiment of the present invention_UEReceiving end antenna correlation coefficient ζ of 4_r0.2, transmitting endAntenna correlation coefficient ζ_tA comparison graph of performance and complexity for the case of 0.5;

fig. 11 is a block diagram of an MMSE detection apparatus according to an embodiment of the present invention.

Detailed Description

To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

In describing positional relationships, unless otherwise specified, when an element such as a layer, film or substrate is referred to as being "on" another layer, it can be directly on the other layer or intervening layers may also be present. Further, when a layer is referred to as being "under" another layer, it can be directly under, or one or more intervening layers may also be present. It will also be understood that when a layer is referred to as being "between" two layers, it can be the only layer between the two layers, or one or more intervening layers may also be present.

Where the terms "comprising," "having," and "including" are used herein, another element may be added unless an explicit limitation is used, such as "only," "consisting of … …," etc. Unless mentioned to the contrary, terms in the singular may include the plural and are not to be construed as being one in number.

Application scenario description: the application scenario of the present invention is shown in fig. 1, which considers the related channel model of a multi-user multi-antenna system, the number of antennas at the transmitting endThe number of antennas at the receiving end is N, wherein the number of antennas at the same user end is M_UE. The amplitude characteristics of the Gram matrix are shown in fig. 2, where the brighter parts indicate the larger absolute values of the corresponding elements of the matrix. Specifically, fig. 2(a) shows that the number of receiving antennas N is 128, the number of transmitting antennas M is 32, and the number of antennas M per user terminal is M_UEAntenna correlation coefficient ζ at transmitting end under antenna configuration of 4_t0.2, receiving end antenna correlation coefficient ζ_rIn the case of 0, the channel amplitude characteristic diagram of the Gram matrix; fig. 2(b) shows the number of receiving antennas N-128, the number of transmitting antennas M-32, and the number of antennas M per subscriber end_UEAntenna correlation coefficient ζ at transmitting end under antenna configuration of 4_tAntenna correlation coefficient ζ at receiving end of 0.4_rIn case of 0.2, the channel amplitude characteristic of the Gram matrix is shown; fig. 2(a) shows the number of receiving antennas N-128, the number of transmitting antennas M-32, and the number of antennas M per subscriber end_UEAntenna correlation coefficient ζ at transmitting end under antenna configuration of 4_t0.6, receiving end antenna correlation coefficient ζ_rIn case of 0.5, the channel amplitude characteristic of the Gram matrix is shown; it can be seen that the Gram matrix exhibits a significant block diagonal dominance characteristic with increasing correlation, with the antenna configuration remaining unchanged, where the size of the sub-block matrix on the diagonal is m_UE×m_UE. The Gram matrix in the present invention isH is the MIM0 channel matrix. The MMSE detection method based on the diagonal matrix block is abbreviated as a BD-NSA method.

As shown in fig. 3, in an embodiment of the present application, an MMSE detection method is provided, which includes the steps of:

step S01, obtaining received signal y, number of antennas distributed by each user terminal, channel matrix H and noise variance

Step S02, calculating a Gram matrix according to a channel matrix and noise variance, extracting a plurality of sub-block matrixes from the Gram matrix according to the number of antennas distributed by each user side, obtaining non-block diagonal matrixes, respectively inverting the sub-block matrixes to obtain inverse matrixes of the sub-block matrixes, and combining the inverse matrixes of the sub-block matrixes along the diagonal to obtain an inverse matrix of the block diagonal;

step S03, obtaining an estimated value of a transmission vector in MMSE through a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix; and judging according to the estimated value of the transmission vector to obtain the detection value of the transmission signal.

In one embodiment, the formula for Gram matrix a is:

wherein H is a channel matrix;is the variance of the noise; i is_MRepresenting an M × M dimensional identity matrix;

in one embodiment, extracting a plurality of sub-block matrices from the Gram matrix according to the number of antennas allocated to each ue, and obtaining a non-block diagonal matrix includes:

sequentially extracting a plurality of subblock matrixes from the main diagonal direction in the Gram matrix A, wherein the subblock matrixes have the sizes as follows: m is_UE×m_UEThe total number of M subblock matrixes is M, M is the number of users at a sending end, and M is M/M_UE，m_UEThe number of antennas allocated to each user terminal; m is the number of transmitting terminal antennas;

removing the subblock matrixes from the Gram matrix A to obtain a non-block diagonal matrix E;

as shown in fig. 4, an example is illustrated where M is 8, M_UEA sub-block matrix (also called sub-matrix) extraction scheme of the Gram matrix of 2. The number of the extracted subblock matrixes is 4, and the subblock matrixes are respectively D_(1，1)，D_(1，2)，D_(1，3)，D_(1，4)Removing the subblock matrixes by the Gram matrix A to obtain a non-block diagonal matrix E required by subsequent calculation;

the subblock matrixes are combined along the diagonal to obtain a block diagonal matrix D, and the inverse matrix D of the block diagonal matrix is obtained subsequently^-1；

The method for respectively inverting the subblock matrixes comprises the following steps: cholesky algorithm, but is not limited to such inversion method; the inverse matrix of the subblock matrix is obtained by respectively carrying out accurate inverse calculation on the subblock matrix through a Cholesky algorithm, so that the detection performance of the method is further improved, and the error rate is reduced. Taking the sub-block matrix in FIG. 4 as an example, the sub-block matrices are calculated separately

Combining the inverse matrixes of the sub-block matrixes along the diagonal to obtain an inverse matrix D of the block diagonal matrix^-1(ii) a As shown in FIG. 5, the inverse matrix obtained in the example of FIG. 3Combined along a diagonal to obtain D^-1；

In one embodiment, obtaining an estimated value of a transmission vector in MMSE by a Neumann iteration method according to an inverse matrix of the block-diagonal matrix, a non-block-diagonal matrix, a received signal and a channel matrix includes:

performing iterative computation on an estimated transmission vector in a Neumann iterative mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix, and taking an estimated value when the maximum iterative times is reached as an estimated value of the transmission vector;

and substituting the inverse matrix of the Gram matrix into a calculation formula for estimating a transmission vector in MMSE to obtain an estimated transmission vector in the Neumann iteration mode.

In particular, the amount of the solvent to be used,

calculating an inverse matrix A of the Gram matrix A in a Neumann iteration mode^-1As shown in the following formula;

wherein k is the number of Neumann iterations, n is a serial number, the range is 0-k-1,is the inverse A of Gram matrix at the kth Neumann iteration^-1An approximation of (d);

the rewritings are in the recursive form:

wherein, L represents the maximum iteration number of Neumann iteration;

estimating a transmission vector according to a calculation formula for estimating a transmission vector in minimum mean-square error (MMSE) detectionComprises the following steps:

will be provided withBy replacing A in the above formula^-1A transmission vector at the kth Neumann iteration can be obtained:

will D^-1H^HY is recorded asThe above equation can be rewritten as:

judging whether the maximum iteration frequency L is reached or not by iterating the formula, and if so, obtaining a sending vector by the last iterationAs an estimate of the transmit vector. The value of L is set manually.

In one embodiment, the determining according to the estimated value of the transmission vector to obtain a detection value of the transmission signal includes:

and finding a group of symbols closest to the Euclidean distance of the estimated value of the transmission vector in a symbol set of a constellation diagram, and taking a vector formed by the group of symbols as a detection value of the transmission signal.

In particular, the amount of the solvent to be used,

finding out the symbol closest to the Euclidean distance from each element in the estimated value of the transmission vector in the symbol set of the constellation diagram, and taking the symbol as the detection value of the ith transmission signalThen the signal is sent asThe decision method adopted in the present embodiment is referred to as a hard decision method.

Table 1 shows the mathematical expressions of the computational complexity of the BD-NSA-EPA algorithm and other detection algorithms in the embodiment of the present invention:

table 1 shows the comparison table of multiplication, addition and division times of the method of the present invention and other methods

From table 1, it can be seen that the conventional MMSE algorithm requires multiplication orders of magnitude ofThe BD-NSA proposed by the present invention is about the order of magnitudeAnd due to m under normal conditions_UE< M, so the proposed algorithm can significantly reduce the complexity of the conventional MMSE algorithm. Although the number of multiplications of the NSA algorithm is onlyBut it is more visible in the later simulation diagrams, where the performance loss under the relevant channel is quite significant.

As shown in fig. 6, under a conventional ideal rayleigh channel (m)_UE1 and the correlation between antennas is not considered), the BD-NSA method proposed by the present invention degenerates into the traditional NSA algorithm, and the performance of the two methods is consistent;

if the multi-user correlation channel is considered, m is shown in FIG. 7_UE4, antenna correlation coefficient ζ at transmitting end_r0.2, receiving end antenna correlation coefficient ζ_tIn the case of 0.4, the performance of the conventional NSA algorithm is drastically reduced, and the performance of the conventional NSA algorithm cannot be converged, and the method of the present invention can still achieve the performance close to the MMSE detection. Specifically, as shown in fig. 9, when the iteration number L is 5, the proposed algorithm BD-NSA can achieve performance almost in accordance with MMSE, and the computational complexity is only about 20% of MMSE. When the antenna correlation at the transmitting end is further increased to ζ_tAs can be seen from fig. 8 and 10, the proposed algorithm can still achieve almost the same detection performance as MMSE while maintaining the computation complexity of 20% of MMSE detection.

As shown in fig. 11, in an embodiment of the present application, an MMSE detection apparatus is provided, including:

the acquisition module is used for acquiring the received signals, the number of antennas distributed by each user side, a channel matrix and a noise variance;

the matrix diagonal blocking module is used for calculating a Gram matrix according to the channel matrix and the noise variance, extracting a plurality of sub-block matrixes from the Gram matrix according to the number of antennas distributed by each user side, and obtaining a non-block diagonal matrix; obtaining inverse matrixes of the subblock matrixes by respectively inverting the subblock matrixes, and combining the inverse matrixes of the subblock matrixes along a diagonal line to obtain an inverse matrix of a block diagonal matrix;

a detection value iteration determination module for obtaining an estimation value of a transmission vector in MMSE by a Neumann iteration mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix; and judging according to the estimated value of the transmission vector to obtain the detection value of the transmission signal.

Further, the extracting a plurality of sub-block matrices from the Gram matrix according to the number of antennas allocated to each user side and obtaining a non-block diagonal matrix includes:

sequentially extracting m subblock matrixes from a main diagonal direction in the Gram matrix, wherein the subblock matrixes have the sizes as follows: m is_UE×m_UEM is the number of users at the transmitting end, m_UEThe number of antennas for each user terminal.

Further, the method for inverting the subblock matrix comprises: cholesky algorithm.

Further, obtaining an estimated value of a transmission vector in MMSE by a Neumann iteration method according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal, and the channel matrix, includes:

performing iterative computation on an estimated transmission vector in a Neumann iterative mode according to the inverse matrix of the block diagonal matrix, the non-block diagonal matrix, the received signal and the channel matrix, and taking an estimated value when the maximum iterative times is reached as an estimated value of the transmission vector;

and substituting the inverse matrix of the Gram matrix into a calculation formula for estimating a transmission vector in MMSE to obtain an estimated transmission vector in the Neumann iteration mode.

Further, the determining according to the estimated value of the transmission vector to obtain a detection value of the transmission signal includes:

and finding a group of symbols closest to the Euclidean distance of the estimated value of the transmission vector in a symbol set of a constellation diagram, and taking a vector formed by the group of symbols as a detection value of the transmission signal.

An MMSE detection apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements any one of the MMSE detection methods described above when executing the program.

A computer-readable storage medium storing computer-executable instructions for performing any of the MMSE detection methods described above.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.