CN104022858B - The signal detecting method and device of auxiliary are pre-processed in multi-input multi-output system - Google Patents

Abstract

This application discloses the signal detecting method that auxiliary is pre-processed in a kind of multi-input multi-output system, to solve the problems, such as the achievable rate that can not be optimal using LRA detections to signal in the prior art.Method includes：According to the parameter value related to the validity of data transfer, complex integers matrix is chosen from complex integers set of matrices；According to the complex integers matrix of reception signal, detection matrix and selection, it is determined that the estimate of the data flow of transmitting.The signal supervisory instrument that auxiliary is pre-processed in a kind of multi-input multi-output system is also disclosed in the application.

Description

Preprocessing-assisted signal detection method and device in multiple-input multiple-output system

technical field

The present application relates to the field of communication technologies, and in particular to a preprocessing-assisted signal detection method and device in a multiple-input multiple-output system.

Background technique

The multiple input multiple output (Multiple Input Multiple Output, MIMO) technology has become a commonly used technology in the fourth generation mobile communication because it can bring diversity gain and spatial multiplexing gain at the same time.

Figure 1 shows the schematic diagram of MIMO technology. The sending end transmits the data stream through N _t transmitting antennas After being transmitted through the space channel, it is received by N _r (N _{t ≤} N _r ) receiving antennas at the receiving end.

On the receiving end, each receive antenna receives a data stream from each transmit antenna. If a received signal y is used to represent the signals received by all receiving antennas (the signals received by the receiving antennas will be referred to as received signals in the following), then the received signal can be expressed as The relationship between x and y is shown in the following formula [1]:

y=Hx+n [1]

in, Represents a channel matrix of N _r ×N _t dimensions, is the zero-mean white Gaussian noise vector on the receiving antenna with variance σ ² .

The elements in H can be written as The value range of i is [1, N _t ]. The elements in h _i can be recorded as h _ji , h _ji represents the single-path channel fading value between the transmitting antenna i and the receiving antenna j, obeying the complex Gaussian random distribution with mean value 0 and variance 1, and the value range of j is [1, _Nr ].

In MIMO systems, there are two main categories of signal detection algorithms used to detect y. One is a linear detection algorithm, which is implemented by a linear detector; the other is a nonlinear detection algorithm, which is implemented by a nonlinear detector. Regardless of the algorithm, its purpose is to recover the data stream x transmitted by the transmitting antenna from the received signal y.

In order to obtain approximately optimal detection performance, based on the most basic linear detection algorithm, a preprocessing-assisted linear detection algorithm is also proposed in the prior art. The algorithm decomposes the channel matrix before performing linear detection on the received signal at the receiving end, and detects the received signal according to the decomposed channel matrix to obtain the detection vector; after that, the detection vector is processed.

Generally, the process of preprocessing-assisted linear detection is shown in Figure 2, which mainly includes the following steps:

Step 1: The receiving end estimates the channel and obtains the channel matrix H; decomposes H into the product of a reduction matrix and a full-rank complex integer matrix, as shown in formula [2].

H=QP[2]

Among them, Q is a reduction matrix, P is a matrix of complex integers, and P is a matrix of full rank.

Substituting formula [2] into formula [1], the following formula [3] is obtained:

y=Hx+n=QPx+n=Qz+n [3]

Wherein, z=Px is an equivalent transmission signal.

Step 2: Perform linear detection on y to obtain the detection vector ρ, as shown in the following formula [4].

ρ = Gy [4]

G is the detection matrix used by the receiving end.

Perform a linear transformation on the detection vector ρ to obtain an estimated value of x

In the prior art, a common linear detection assisted by preprocessing is Lattice-Reduction-Aided (LRA) detection. In LRA detection, the LRA algorithm is used to decompose the channel matrix H, and through iterative reduction and exchange operations, the correlation of each column vector of the decomposed reduction matrix and the difference of each column vector modulus are gradually reduced. , so that each column of the reduced matrix H _red obtained from the final decomposition is as orthogonal and equimodulus as possible.

Specifically, the process of LRA detection mainly includes the following steps:

Step 1: Decompose the channel matrix H to obtain the reduction matrix H _red and the complex integer matrix T:

H＝H _red T [6]

Corresponding to formula [2], it can be seen that Q=H _red , P=T.

Substituting formula [6] into formula [1], the following formula [7] is obtained:

y=H _red Tx+n=H _red z+n [7]

The second step: perform linear detection on y, and perform linear transformation on the obtained detection vector (as shown in formula [4]), and obtain the estimated value of x (Shown in formula [5]).

A manner of performing linear detection on y may be, for example, zero forcing (Zero Forcing, ZF) detection or minimum mean square error (Minimum Mean Square Error, MMSE) detection, and so on.

The advantage of LRA detection is that it can obtain the full receiving diversity gain of order _Nr . The defect of LRA detection is that the criterion for decomposing the channel matrix H is to make each column of the reduction matrix H _red as orthogonal and equal-modulus as possible, so as to minimize the interference between the data streams sent by different transmitting antennas, but Using this criterion cannot achieve the optimal reachable rate.

Contents of the invention

An embodiment of the present application provides a signal detection method assisted by preprocessing in a MIMO system, to solve the problem in the prior art that LRA detection for signals cannot achieve an optimal achievable rate.

The embodiment of the present application also provides a signal detection device assisted by preprocessing in a multiple-input multiple-output system to solve the problem in the prior art that LRA detection for signals cannot achieve an optimal attainable rate.

The embodiment of the application adopts the following technical solutions:

A preprocessing-assisted signal detection method in a multiple-input multiple-output system, comprising: selecting a complex integer matrix from a set of complex integer matrices according to parameter values related to the validity of data transmission; wherein, the set of complex integer matrices is composed of The complex integer matrix obtained by decomposing the channel matrix is formed; according to the received signal, the detection matrix and the selected complex integer matrix, the estimated value of the transmitted data stream is determined; wherein, the detection matrix is based on the channel matrix and the selected The matrix of complex integers is determined.

A preprocessing-assisted signal detection device in a multiple-input multiple-output system, comprising: a matrix selection unit, used to select a complex integer matrix from a set of complex integer matrices according to parameter values related to the validity of data transmission; wherein, the The complex integer matrix set is composed of complex integer matrices obtained by decomposing the channel matrix; the sending signal determination unit is used to determine the estimated value of the transmitted data stream according to the complex integer matrix selected by the receiving signal, detection matrix and matrix selection unit; Wherein, the detection matrix is determined according to the channel matrix and the selected matrix of complex integers.

The above at least one technical solution adopted in the embodiment of the present application can achieve the following beneficial effects:

Since the parameter values related to the effectiveness of data transmission are used as the basis for determining the matrix of complex integers, it is possible to take into account the effectiveness of data transmission when detecting the received signal, and to avoid the failure to achieve the optimal detection of signals using LRA in the prior art. The problem of optimal attainable speed.

Description of drawings

The drawings described here are used to provide a further understanding of the application and constitute a part of the application. The schematic embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation to the application. In the attached picture:

FIG. 1 is a schematic diagram of MIMO technology in the prior art;

2 is a schematic diagram of a detection-assisted linear detection process in the prior art;

FIG. 3 is a specific implementation flowchart of a preprocessing-assisted signal detection method in a multiple-input multiple-output system provided in Embodiment 1 of the present application;

FIG. 4 is a schematic structural diagram of a signal detection device assisted by preprocessing in a multiple-input multiple-output system provided in Embodiment 2 of the present application;

Fig. 5 is a schematic diagram comparing the attainable rate of the data stream at the receiving end achieved by using the signal detection scheme provided by the embodiment of the present application and the attainable rate of the data stream at the receiving end achieved by using the LRA detection of the prior art.

detailed description

In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and corresponding drawings. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

The technical solutions provided by various embodiments of the present application will be described in detail below in conjunction with the accompanying drawings.

Example 1

In order to solve the problem in the prior art that using LRA detection for signals cannot achieve the optimal reachable rate and optimal reachable rate, Embodiment 1 of the present application provides a preprocessing-assisted signal detection method in a multiple-input multiple-output system . The specific implementation flow chart of the method is shown in Figure 3, which mainly includes the following steps:

Step 31: Select a complex integer matrix from the set of complex integer matrices according to parameter values related to the validity of data transmission.

Wherein, the above-mentioned parameter value related to the effectiveness of data transmission may be, but not limited to, the achievable rate at the receiving end of data streams transmitted by different transmitting antennas.

The set of complex integer matrices described above is composed of complex integer matrices obtained by decomposing the channel matrix.

In Embodiment 1, the method for decomposing the channel matrix may be, but not limited to, any method for decomposing the channel matrix existing in the prior art.

Step 32, determine the estimated value of the transmitted data stream according to the received signal, the detection matrix and the selected matrix of complex integers. Wherein, the detection matrix is determined according to the channel matrix and the selected matrix of complex integers.

Specifically, according to the formulas [4] and [5], the calculation formula of the estimated value of the data stream transmitted by the transmitting antenna can be determined as shown in the following formula [8]:

Wherein, y is a received signal, G is a detection matrix, and P is a matrix of complex integers selected by performing step 31 .

In Embodiment 1, G may be, but not limited to, adopt the detection matrix used in existing ZF detection, MMSE detection or other detection algorithms. Taking ZF detection as an example, the detection matrix G can be G=P(H ^H H) ^-1 H ^H ; taking MMSE detection as an example, the detection matrix G can be G=P(H ^H H+σ ² I) ^-1 H ^H.

Using the above method provided in Embodiment 1, since the parameter values related to the validity of data transmission are used as the basis for determining the matrix of complex integers, it is possible to take into account the validity of data transmission when detecting the received signal, avoiding the prior art In the middle, the problem that the LRA detection for the signal cannot achieve the optimal attainable rate.

Taking the achievable rate of the data stream transmitted by the transmitting antenna at the receiving end as an example, the following explains in detail how to select a complex integer matrix from a set of complex integer matrices according to the parameter values related to the validity of data transmission:

First, briefly introduce the concept of attainable rate.

When the system transmission rate R _TX satisfies the formula [9], if the system can ensure that the average bit error rate of transmission is lower than any ε (ε>0), then R is the achievable rate of the system.

R _TX ≤ R [9]

In the case where the parameter value related to validity is the value of the attainable rate of the data stream transmitted by different transmitting antennas at the receiving end, the implementation of step 31 mentioned above may include the following substeps A to C:

Sub-step A: According to the mapping relationship between the achievable rate of the data stream sent by the transmitting antenna at the receiving end and the specific parameter, and the value of the specific parameter, determine the minimum value of the achievable rate of the data stream transmitted by different transmitting antennas at the receiving end;

Sub-step B: determining the maximum value from the determined minimum values of each attainable rate;

Sub-step C: From the set of complex integer matrices, select the complex integer matrix to which the determined maximum value is mapped.

According to the description of the above sub-step A ~ sub-step C, if it is assumed that from the set of complex integer matrices (expressed as {P ₁ , P ₂ , ..., P _k , ..., P _L }) composed of L complex integer matrices Select a complex integer matrix, then, according to the mapping relationship shown in the following formula [10], each complex integer matrix in this set can be determined separately to calculate the achievable rate of data streams transmitted by different transmitting antennas at the receiving end value.

Among them, R _k,m represents the value of the achievable rate at the receiving end of the data stream transmitted by the transmitting antenna numbered m received by the receiving end using the complex integer matrix P _k ; it is the value of the complex integer matrix in the complex integer matrix set number, whose value range is [1, L], and m is the number of the transmitting antenna, whose value range is [1, N _t ].

According to the mapping relationship shown in formula [10], a set of values of R _k,m can be calculated for each complex integer matrix P _k , and then a certain set of attainable rate values determined according to the target criterion can be used to obtain Implements the selection of a matrix of complex integers.

For example, taking the complex integer matrix P _k as an example, the data streams transmitted by different transmitting antennas received by the receiving end can be respectively determined according to the N _t column vectors formed by each column of matrix elements of P _k (a total of N _t columns) The value of the attainable rate at the receiving end finally obtains a set of R _k,m values, that is, N _t values of the attainable rate R _k,m . Afterwards, the selection of complex integer matrices can be realized according to the following objective criteria:

First, determine the minimum value of the obtained N _t R _k,m . For example, taking the set of complex integer matrices {P ₁ , P ₂ , ..., P _L } as an example, by performing the above operations on all the complex integer matrices in the set of complex integer matrices, it is possible to determine the L minimum achievable rate value.

Then, select the maximum attainable rate value from the determined L minimum attainable rate values.

Finally, based on the value of the selected maximum achievable rate, from the set of complex integer matrices {P ₁ , P ₂ , ..., P _L }, select the complex integer matrix mapped to the value of the maximum achievable rate .

In summary, when the parameter value related to effectiveness is the value of the achievable rate of the data stream transmitted by different transmitting antennas at the receiving end, the selected complex integer matrix is the matrix that maximizes the minimum value of formula [10]. That is, if it is assumed that the selected matrix of complex integers can be denoted as P _opt , then the following formula [11] holds:

Due to the adoption of the above-mentioned complex integer matrix selection method, under the current channel conditions, the value of the achievable rate of the data stream with the lowest achievable rate after detection can be made as high as possible, thereby improving the effectiveness of data transmission.

The method of obtaining the matrix set of complex integers described in Embodiment 1 is further introduced below.

As mentioned above, the set of complex integer matrices is composed of complex integer matrices obtained by decomposing the channel matrix. There are many specific matrix decomposition methods, such as the exhaustive method, that is, to traverse all the complex integers that may be matrix elements of the complex integer matrix in a given interval, and then generate multiple complex integer matrices based on these complex integers, so that Form a set of complex integer matrices.

In Embodiment 1, a low-complexity complex integer matrix set acquisition method different from the prior art is proposed. The method is described in detail below.

First of all, the method of rounding and Schmidt orthogonalization of the matrix adopted in Embodiment 1 will be described first:

Suppose any matrix V can be expressed as The elements in V can be written as The value range of i is [1, N _t ], then, after V is rounded and Schmidt-orthogonalized, the matrix can be obtained The elements in B can be written as The relationship between the elements in V and B is shown in the following formula [12] and formula [13]:

β ₁ =v ₁ [12]

In the above formula, the value range of n is [2, N _t ], <·> represents the vector inner product, and [·] represents the rounding operation.

The relationship between V and B is shown in formula [14].

V=BA[14]

Among them, A is a matrix of complex integers obtained by decomposing V, which can be expressed in the form shown in formula [15]:

In Embodiment 1, in order to reduce the computational complexity of matrix B, the following operations can be performed:

First, perform autocorrelation on matrix V to obtain matrix R. As shown in formula [16].

Then, separately for the matrix elements in each row of R: divide by the matrix elements in that row that are on the main diagonal. Thus the matrix K shown in formula [17] is obtained.

Since in the matrix shown in formula [17], except for the matrix element of "1", other matrix elements are the same as in formula [13] is corresponding, therefore, according to the formula [17], the matrix elements in the matrix B can be easily calculated, thereby reducing the computational complexity of B.

It should be noted that if the matrix elements of a certain column of K are all 0, then V does not need to be orthogonalized. This situation is called invalid orthogonalization.

Based on the above-mentioned method for rounding-Schmidt orthogonalization of the matrix, the following further introduces the specific details of the low-complexity channel matrix decomposition using the method of multiple rounding-Schmidt orthogonalization proposed in Embodiment 1 process. In this process, the number of rounded-Schmidt orthogonalization can be determined by the actual situation, and it is assumed that the number of times is D here.

The first step of the process is: by exchanging different column vectors of the channel matrix H, each permuted channel matrix is obtained. For ease of description, the set formed by all the permuted channel matrices obtained here will be referred to as a set of permutation matrices hereinafter.

The second step is: use the permuted channel matrices to perform rounding-Schmidt orthogonalization processing on H to obtain a set of complex integer matrices.

For the first step, if the size of H is N _r ×N _t , then it can be assumed that the set of permutation matrices is Among them, each matrix in Swap satisfies: each column contains only one element 1, and the 1s of any two columns are in different rows. The matrix in Swap can be expressed as S _l , and the value range of l is [1, (N _t )! ].

For the second step, its implementation can be as follows:

The channel matrix H is used as a set of channel matrices to be processed, and specific operations are cyclically performed on the set of channel matrices to be processed until D complex integer matrix subsets are obtained. These D complex integer matrix subsets constitute the desired complex integer matrix set.

Among them, the above specific operations may include: using Perform rounding-Schmidt orthogonalization processing on each matrix in the set of channel matrices to be processed to obtain a subset of complex integer matrices and a set of processed channel matrices; update the set of channel matrices to be processed to the set of processed channel matrices. As an example, the implementation of this particular operation is as follows:

First, use Carry out rounded Schmidt orthogonalization processing as shown in the formula [18] on the channel matrix H which is the set of channel matrices to be processed, and obtain the complex integer matrix subset A _{schmit_1} and the processed channel matrix set H _{schmit_1} . Among them, the elements in A _{schmit_1} can be expressed as Elements in H _{schmit_1} can be denoted as H _{schmit_1_l} . The value range of l is [1, (N _t )! ].

H·S _l ＝ H _{schmit_1_l} A _{schmit_1_l} [18]

Then, take H _{schmit_1} as the set of channel matrices to be processed, and use Perform rounding-Schmitt orthogonalization processing as shown in the formula [19] to obtain the complex integer matrix subset A _{schmit_2} and the processed channel matrix set H _{schmit_2} . Among them, the elements in A _{schmit_2} can be expressed as Elements in H _{schmit_2} can be denoted as H _{schmit_2_l} . The value range of l is [1, (N _t )! ].

H _{schmit_1} · S _l ＝ H _{schmit_2_l} A _{schmit_2_l} [19]

By analogy, it can be determined that the formula on which the D-th rounding-Schmidt orthogonalization process is performed is shown in the following formula [20].

H _{schmit_D-1} · S _l ＝ H _{schmit_D_l} A _{schmit_D_l} [20]

By performing the above specific operations, multiple subsets of complex integer matrices can be obtained, namely A _{schmit_1} , A _{schmit_2} , . . . , A _{schmit_D} . These subsets of complex integer matrices together constitute the complex integer matrix set expected to be obtained in Embodiment 1.

Compared with the method of using the exhaustive method to determine the set of complex integer matrices, the advantage of determining the set of complex integer matrices by performing rounding Schmidt orthogonalization multiple times in Embodiment 1 is that the final obtained complex integer matrix The size of the collection is limited within an acceptable range, reducing the data size of the complex integer matrix collection. Compared with the method of using the exhaustive method to determine the complex integer matrix set, this method can complete the acquisition of the complex integer matrix set with higher efficiency when the signal-to-noise ratio is high or the number of transmitting and receiving antennas is large.

Example 2

Embodiment 2 provides a signal detection device assisted by preprocessing in a multiple-input multiple-output system, which is used to solve the problem in the prior art that LRA detection for signals cannot achieve an optimal attainable rate. The specific structural diagram of the signal detection device is shown in FIG. 4 , which includes a matrix selection unit 41 and a transmission signal determination unit 42 . The specific introduction of these two functional units is as follows:

The matrix selection unit 41 is configured to select a complex integer matrix from the set of complex integer matrices according to parameter values related to the validity of data transmission. Wherein, the set of complex integer matrices is composed of complex integer matrices obtained by decomposing the channel matrix.

The sending signal determining unit 42 is configured to determine the estimated value of the transmitted data stream according to the received signal, the detection matrix and the complex integer matrix selected by the matrix selecting unit 41 .

Optionally, when the parameter value related to the validity of data transmission is the value of the attainable rate of the data stream transmitted by different transmitting antennas at the receiving end, the matrix selection unit 41 can be divided into a minimum attainable rate determination module, a maximum Determination module and matrix selection module. The introduction of each functional module is as follows:

The minimum achievable rate determination module is used to determine the achievable rate of the data streams transmitted by different transmitting antennas at the receiving end according to the mapping relationship between the achievable rate of the data streams sent by the transmitting antennas at the receiving end and the specific parameter and the value of the specific parameter Minimum value; wherein, the value of the specific parameter includes: the detection matrix, the power value of Gaussian white noise, the channel matrix and the complex integer matrix in the complex integer matrix set;

a maximum value determination module, configured to determine the maximum value from the minimum values of each attainable rate determined by the minimum value of the attainable rate determination module;

The matrix selection module is used to select the complex integer matrix mapped to the maximum value determined by the maximum value determination module from the set of complex integer matrices.

Using the device provided in Embodiment 2, since the parameter values related to the validity of data transmission are used as the basis for determining the matrix of complex integers, it can be realized that the validity of data transmission is taken into account when detecting the received signal, avoiding the problem in the prior art. The problem that using LRA detection for signals cannot achieve the optimal reachable rate.

In the embodiment of the present application, through experiments, the achievable rate of the data stream at the receiving end that can be achieved by using the signal detection scheme provided by the embodiment of the present application is compared with the data stream at the receiving end that can be achieved by using the LRA detection of the prior art. The achievable speed of the terminal. The comparison results are shown in Figure 5.

In the coordinate system shown in FIG. 5 , the abscissa is the SNR (that is, the SINR); the ordinate is the achievable rate of the data stream at the receiving end. The lower line is the achievable rate curve of the data stream at the receiving end that can be achieved by using LRA detection, and the upper line is the signal detection scheme provided by the embodiment of the present application. When the parameters related to the validity In the case of the value of the achievable rate of the data stream at the receiving end, the achievable curve of the achievable rate of the data stream at the receiving end.

It can be seen from FIG. 5 that, compared with LRA detection, adopting the signal detection scheme provided by the embodiment of the present application can increase the achievable rate of the data stream at the receiving end, that is, the transmission effectiveness of the system is improved.

Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

Memory may include non-permanent storage in computer readable media, in the form of random access memory (RAM) and/or nonvolatile memory such as read only memory (ROM) or flash RAM. Memory is an example of computer readable media.

Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented by any method or technology for storage of information. Information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Flash memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cartridge, tape magnetic disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media excludes transitory computer-readable media, such as modulated data signals and carrier waves.

It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes Other elements not expressly listed, or elements inherent in the process, method, commodity, or apparatus are also included. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The above descriptions are only examples of the present application, and are not intended to limit the present application. For those skilled in the art, various modifications and changes may occur in this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims (6)

1. A signal detection method assisted by preprocessing in a MIMO system, characterized in that, comprising: Selecting a complex integer matrix from a complex integer matrix set according to parameter values related to the validity of data transmission; wherein, the complex integer matrix set is formed by a complex integer matrix obtained by decomposing a channel matrix; determining an estimated value of the transmitted data stream based on the received signal, the detection matrix and the selected matrix of complex integers; Wherein, the detection matrix is determined according to the channel matrix and the selected complex integer matrix, and the parameter values related to the validity of data transmission include the achievable rate of data streams transmitted by different transmitting antennas at the receiving end value; When the parameter value related to the validity of data transmission is the value of the achievable rate of the data stream transmitted by different transmitting antennas at the receiving end, according to the parameter value related to the validity of data transmission, from the set of complex integer matrices Select a matrix of complex integers in , including: According to the mapping relationship between the achievable rate of the data stream transmitted by the transmitting antenna at the receiving end and the specific parameter and the value of the specific parameter, respectively determine the minimum value of the achievable rate of the data stream transmitted by the different transmitting antennas at the receiving end; wherein, The value of the specific parameter includes: the detection matrix, the power value of Gaussian white noise, the channel matrix and the complex integer matrix in the complex integer matrix set; Determining the maximum value from the minimum values of all achievable rates determined; From the set of complex integer matrices, select a complex integer matrix mapped by the maximum value. 2. the method for claim 1, is characterized in that, adopts following mode to obtain described complex integer matrix set: Determine each channel matrix after the permutation; each channel matrix after the permutation is obtained by exchanging different column vectors of the channel matrix; Using the permuted channel matrices, perform rounding-Schmidt orthogonalization processing on the channel matrices before permutation to obtain the set of complex integer matrices. 3. the method for claim 2, is characterized in that, utilizes each channel matrix after described permutation, carries out rounding Schmidt orthogonalization process to described channel matrix before permutation, obtains described complex integer matrix collection, including: Using the channel matrix before the permutation as a set of channel matrices to be processed, and cyclically performing specific operations on the set of channel matrices to be processed until a predetermined number of complex integer matrix subsets are obtained; Wherein, the predetermined number of complex integer matrix subsets constitute the complex integer matrix set; The specific operation includes: using the permuted channel matrices, performing rounding-Schmidt orthogonalization processing on each matrix in the set of channel matrices to be processed, to obtain a subset of complex integer matrices and a set of processed channel matrices; Update the set of channel matrices to be processed to the set of processed channel matrices. 4. A preprocessing-assisted signal detection device in a multiple-input multiple-output system, characterized in that it comprises: The matrix selection unit is used to select a complex integer matrix from a set of complex integer matrices according to parameter values related to the validity of data transmission; wherein, the set of complex integer matrices is composed of complex integer matrices obtained by decomposing the channel matrix; The sending signal determining unit is used to determine the estimated value of the transmitted data stream according to the receiving signal, the detection matrix and the complex integer matrix selected by the matrix selecting unit; Wherein, the detection matrix is determined according to the channel matrix and the selected complex integer matrix, and the parameter values related to the validity of data transmission include the achievable rate of data streams transmitted by different transmitting antennas at the receiving end value; When the parameter value related to the validity of data transmission is the value of the attainable rate of the data stream transmitted by different transmitting antennas at the receiving end, the matrix selection unit includes: The minimum achievable rate determination module is used to determine the data streams transmitted by the different transmitting antennas according to the mapping relationship between the achievable rate of the data streams transmitted by the transmitting antennas at the receiving end and the specific parameter, and the value of the specific parameter. The minimum value of the achievable rate of the receiving end; wherein, the value of the specific parameter includes: the detection matrix, the power value of Gaussian white noise, the channel matrix and the complex integer matrix in the set of complex integer matrices; a maximum value determination module, configured to determine the maximum value from the minimum values of each attainable rate determined by the minimum value of the attainable rate determination module; The matrix selection module is configured to select the complex integer matrix mapped to the maximum value determined by the maximum value determination module from the set of complex integer matrices. 5. The device of claim 4, further comprising: A matrix determination unit, configured to determine each channel matrix after permutation; each channel matrix after permutation is obtained by exchanging different column vectors of the channel matrix; The orthogonalization processing unit is configured to use the permuted channel matrices determined by the matrix determination unit to perform rounding-Schmidt orthogonalization processing on the channel matrix before permutation to obtain the set of complex integer matrices. 6. The device according to claim 5, wherein the orthogonalization processing unit is specifically configured to: use the channel matrix before permutation as a set of channel matrices to be processed, and perform a special Operate until a predetermined number of complex integer matrix subsets are obtained; Wherein, the predetermined number of complex integer matrix subsets constitute the complex integer matrix set; The specific operation includes: using the permuted channel matrices, performing rounding-Schmidt orthogonalization processing on each matrix in the set of channel matrices to be processed, to obtain a subset of complex integer matrices and a set of processed channel matrices; Update the set of channel matrices to be processed to the set of processed channel matrices.