CN113098571B - Massive MIMO system signal detection method, system, base station and storage medium - Google Patents

Abstract

The present invention provides a signal detection method, system, base station and storage medium for a massive MIMO system. The detection method includes performing active tabu search detection on a first initial signal to obtain an initial estimation vector; the interference signal in the first initial signal to obtain the second initial signal; the message passing detection is performed on the second initial signal to obtain the output vector estimate; the symbol vector is reconstructed according to the output vector estimate to obtain the symbol vector reconstruction value; the symbol vector reconstruction value is used as the input of the active tabu search to perform an iterative operation, and the final symbol vector reconstruction value after the iteration is output as the detection result. By using the present invention, the poor performance of the RTS algorithm under high-order modulation can be improved, and it has lower complexity.

Description

Massive MIMO system signal detection method, system, base station and storage medium

technical field

The present invention relates to the technical field of multiple-input multiple-output signal detection, in particular to a signal detection method, system, base station and storage medium of a massive MIMO system.

Background technique

In recent years, great changes have taken place in mobile communication. The rapid popularization of intelligent terminal equipment, the rapid addition of wireless data communication services, and the new services of Internet of Things technology in emerging industries have created higher requirements for data communication. In order to solve the problem of shortage of spectrum resources, a large-scale multiple input multiple output (Multiple Input Multiple Output, MIMO) technology emerges. MIMO technology provides services to multiple users at the same time by configuring hundreds of antennas on the base station. Compared with traditional small-scale MIMO systems, massive MIMO systems significantly improve spectral efficiency and can meet the requirements of high data transmission rates, stable connections, and low latency. Massive MIMO technology with extremely high energy efficiency and spectrum utilization is widely regarded as the key technology of next-generation communication network architecture.

While MIMO brings significant performance gains, the large number of antennas and the high-dimensional channel matrix increase the complexity of the detection signal algorithm exponentially, which brings great challenges to the signal detection at the receiving end. The active tabu search algorithm is a heuristic algorithm with low complexity and good performance. It has great applicability in Massive MIMO signal detection and is a research hotspot in massive MIMO detection. There is a problem with poor performance.

SUMMARY OF THE INVENTION

In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a massive MIMO system signal detection method, system, base station and storage medium for solving the RTS algorithm in the massive MIMO system signal detection in the prior art Technical issues with poor performance in higher order modulation systems.

To achieve the above object and other related objects, the present invention provides a signal detection method for a massive MIMO system, including:

Perform active tabu search detection on the first initial signal to obtain an initial estimated vector;

cancel the interference signal in the first initial signal according to the initial estimation vector to obtain a second initial signal;

performing message passing detection on the second initial signal to obtain an output vector estimate;

Reconstructing the symbol vector according to the output vector estimate to obtain a symbol vector reconstruction value;

The iterative operation is performed using the reconstructed value of the symbol vector as the input of the active tabu search, and the final reconstructed value of the symbol vector after the iteration is output as the detection result.

In an optional embodiment, the step of canceling the interference signal in the first initial signal according to the initial estimation vector to obtain the second initial signal includes:

Obtain output bit values according to the initial estimation vector;

Obtain the total interference between signals according to the output bit value;

The total inter-signal interference is subtracted from the first initial signal to obtain the second initial signal.

In an optional embodiment, the output bit value is obtained according to the following formula

Q是星座图中点的个数，K为MIMO系统在上行链路中用户的个数，

为初始估计向量

的第i个分量。in,

Q is the number of points in the constellation diagram, K is the number of users in the uplink of the MIMO system,

is the initial estimate vector

the ith component of .

的表达式如下：In an optional embodiment, in the step of obtaining the total inter-signal interference according to the output bit value, the total inter-signal interference

The expression is as follows:

H为信道增益矩阵。in,

H is the channel gain matrix.

的表达式如下：In an optional embodiment, in the step of reconstructing the symbol vector according to the output vector estimate to obtain the reconstructed value of the symbol vector, the reconstructed value of the symbol vector is

The expression is as follows:

为输出向量估计。in,

Estimated for the output vector.

In an optional embodiment, in the step of performing the iterative operation using the reconstructed value of the symbol vector as the input of the active tabu search, the number of iterations is greater than or equal to three times.

In an optional embodiment, in the step of performing the iterative operation using the reconstructed value of the symbol vector as the input of the active tabu search, the number of iterations is three times.

To achieve the above object and other related objects, the present invention also provides a massive MIMO system signal detection system, including:

an active tabu search module, which is used to perform active tabu search detection on the first initial signal to obtain an initial estimated vector;

an interference signal cancellation module, configured to cancel the interference signal in the first initial signal according to the initial estimation vector to obtain a second initial signal;

a vector estimation obtaining module, configured to perform message passing detection on the second initial signal to obtain an output vector estimation;

a symbol vector reconstruction module, configured to reconstruct the symbol vector according to the output vector estimation to obtain a symbol vector reconstruction value;

The iterative output module is configured to perform an iterative operation using the reconstructed value of the symbol vector as an input of the active tabu search, and output the final reconstructed value of the symbol vector after the iteration is completed as a detection result.

To achieve the above object and other related objects, the present invention also provides a base station, the base station includes:

The base station body, a plurality of receiving antennas and control units arranged on the base body;

Wherein, the receiving antenna is used to receive a symbol vector sent by a user of the MIMO system, and transmit the symbol vector as a first initial signal to the control unit for signal detection; the control unit includes a mutually coupled processor and A memory, where program instructions are stored in the memory, and when the program instructions stored in the memory are executed by the processor, any one of the above-mentioned methods for detecting a signal in a massive MIMO system is implemented.

In order to achieve the above object and other related objects, the present invention also provides a storage medium including a program, when the program runs on a computer, the computer enables the computer to execute the signal detection method for a massive MIMO system.

The present invention provides a massive MIMO system signal detection method, system, base station and storage medium, proposes a combined active tabu search and message passing detection algorithm for massive MIMO signal detection under high-order modulation, and combines MPD algorithm with RTS algorithm In , the MPD algorithm is used to correct the error of the last bit of the symbol, which can improve the poor performance of the RTS algorithm under high-order modulation, and has a lower complexity.

Description of drawings

FIG. 1 shows a model diagram of a massive MIMO system of the present invention.

FIG. 2 is a flowchart of a signal detection method for a massive MIMO system according to an embodiment of the present invention.

FIG. 3 is a structural block diagram of a signal detection system of a massive MIMO system according to an embodiment of the present invention.

FIG. 4 is a structural block diagram of a control unit according to an embodiment of the present invention.

Figure 5 shows the performance curve of the RTS algorithm with different numbers of antennas under 4-QAM modulation.

Figure 6 shows the performance comparison of the RTS algorithm under 16-QAM and 64-QAM modulation.

FIG. 7 is a graph showing the comparison of detection performance between the RTS-MPD algorithm of the present invention and the RTS algorithm.

Detailed ways

The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

See Figures 1-7. It should be noted that the diagrams provided in this embodiment are only to illustrate the basic concept of the present invention in a schematic way, so the diagrams only show the components related to the present invention rather than the number, shape and the number of components in the actual implementation. For dimension drawing, the type, quantity and proportion of each component can be changed at will in actual implementation, and the component layout may also be more complicated.

FIG. 1 shows a model diagram of a massive MIMO (multiple-input multiple-output, multiple-input multiple-output) system of the present invention. Consider that a massive MIMO system has K users 1 in the uplink, each user has a transmit antenna 11, and communicates with a base station 2 equipped with N receive antennas 21, and the value of N ranges from tens to hundreds between. α=K/N is the loading factor of the system (α≤1). The system model is shown in Figure 1.

表示在第t个信道的信道增益矩阵，

表示从第j个用户天线(发射天线11)到第i个基站天线(接收天线21)的复数信道增益，信道增益

是服从均值为0方差为

模型由于路径损耗等导致来自用户j的接收功率不平衡，

对应于完全功率控制情况。用

表示调制符号向量传输在第t个信道中，其中

的第j个元素表示通过第j个用户传输的调制符号。假设完全同步，在基站2第t个信道接收向量为

可以写为：use

represents the channel gain matrix at the t-th channel,

Represents the complex channel gain from the jth user antenna (transmitting antenna 11) to the ith base station antenna (receiving antenna 21), the channel gain

is subject to the mean of 0 and the variance is

The model causes the received power from user j to be unbalanced due to path loss, etc.,

Corresponds to the full power control case. use

represents that the modulation symbol vector is transmitted in the t-th channel, where

The jth element of is the modulation symbol transmitted by the jth user. Assuming complete synchronization, the received vector on the t-th channel of base station 2 is

can be written as:

为噪声向量，为了方便删除信道索引，(1)实数系统模型写为：in

is the noise vector, in order to conveniently delete the channel index, (1) the real number system model is written as:

y=Hx+n (2)

和

分别表示实数和复数部分，同时，

对于

其中

是脉冲幅度调制(PluseAmplitude Modulation,PAM)字母表，表示在复数星座图Θ中同向或正交部分的点。由于

传输符号的星座图大小从Q减小到

其中,Q为星座图中点的个数。现在一个复数N×K MIMO复数星座图Θ等价于一个实数2N×2K MIMO实数星座图

n是噪声向量服从独立同分布

每根接收天线21的平均接收信噪比

其中E_s表示传输符号的平均能量。in

and

represent the real and complex parts, respectively, and, at the same time,

for

in

is the Pulse Amplitude Modulation (PAM) alphabet, representing points in the in-direction or quadrature portion of the complex constellation Θ. because

The constellation size of the transmitted symbols is reduced from Q to

Among them, Q is the number of points in the constellation diagram. Now a complex N×K MIMO complex constellation Θ is equivalent to a real 2N×2K MIMO real constellation

n is the noise vector that is independent and identically distributed

Average received signal-to-noise ratio of each receive antenna 21

where _Es represents the average energy of the transmitted symbols.

The principles of the Reactive Tabu Search (RTS for short) algorithm, the Message Passing Detection (MPD for short) algorithm and the RTS-MPD joint algorithm of the present invention will be respectively described below.

A. RTS detection algorithm

For the real number system model, the maximum likelihood (ML) detection model can be expressed as

When the prior probability of the transmitted bits is the same, the likelihood detection is equivalent to the maximum a posteriori detection, which can be expressed as:

Among them, the exact calculation complexity of formula (3) and formula (4) is the exponential level of K.

From formula (3), the maximum likelihood model can be written in the following form:

f(x)为传输信号向量的代价函数。in,

f(x) is the cost function of the transmitted signal vector.

Suppose g ^(m) represents the optimal solution vector (ML) after m iterations with the smallest cost function value, L _rep represents the average length between two repeated solution vectors (ie, the average number of iterations), and N _rep represents the solution vector The number of repetitions, P represents the taboo period, and lflag∈{0,1} is the sign of the local minimum, which is used to judge whether the local minimum is reached in this iteration.

并计算出y_MF和R的值。下面给出了在每一次迭代时所需要的步骤，考虑RTS算法检测过程的第m(m>0)次迭代：The RTS algorithm starts with an initial solution vector, denoted as x ⁽⁰⁾ , which can be the output of a known detector ZF (ZeroForcing)/MMSE (Minimum Mean Squared Error), or a random Generated. Let g ⁽⁰⁾ = x ⁽⁰⁾ , L _rep = 0, N _rep = 0, p = p ₀ (p ₀ is the initial taboo period value, which is a positive integer), and the taboo matrix T=0, define

And calculate the value of y _MF and R. The steps required at each iteration are given below, considering the mth (m>0) iteration of the RTS algorithm detection process:

e＝Z^(m)(u,v)-x^(m)，则x^(m)的KM(M为符号邻居的数目)个向量邻居为Z^(m)(u,v)，u＝1,2,…,K，v＝1,2,…,M的ML代价函数值f(z^(m)(u,v))计算如下：Step 1: Initialize lflag=0. Assume

e=Z ^(m) (u,v)-x ^(m) , then the KM (M is the number of symbol neighbors) vector neighbors of x ^(m) is Z ^(m) (u,v), u=1, The ML cost function value f(z ^(m) (u,v)) for 2,…,K, v=1,2,…,M is calculated as follows:

in,

(u ₁ ,v ₁ )=arg min _u,v f(z ^(m) (u,v))=arg min _u,v (u,v) (7)

Then, the conditions for judging that the move move(u ₁ , v ₁ ) can occur are as follows:

f(z ^(m) (u ₁ ,v ₁ ))<f(g ^(m) ) (8)

T((u ₁ -1)M+q,v ₁ )=0 (9)

The move move(u ₁ , v ₁ ) can occur as long as any one of the above conditions is satisfied, ie the current solution vector x ^(m) is executed.

推算出来的。如果移动move(u₁,v₁)对应的向量邻居不满足式(8)中的条件，且禁忌矩阵T也不满足式(9)中的条件，则用(u₂,v₂)进行条件判断，其中Transition to the (u ₁ , v ₁ )-th vector neighbor z ^(m) (u ₁ , v ₁ ). In the above formula (9), q is based on

inferred. If the vector neighbor corresponding to move move(u ₁ , v ₁ ) does not satisfy the condition in equation (8), and the taboo matrix T does not satisfy the condition in equation (9), then use (u ₂ , v ₂ ) to condition judgment, which

If move(u ₂ , v ₂ ) cannot occur, continue to use (u _k , v _k ), k=3,4,...,kM to perform conditional judgment until x ^(m) can be transferred to a vector neighbor direction until. If all the KM directions to be transferred are taboo, find the minimum value in the taboo matrix T, subtract this minimum value from all elements in the taboo matrix T, and then repeat the above process again to judge whether the transfer can be performed. Assuming that the cost function value of the vector neighbor z ^(m) (u', v') corresponding to (u', v') is the current minimum, and the move (u', v') can occur, then we have

x ^(m+1) = z ^(m) (u′,v′) (11)

Step 2: Check the repeatability of the solution on the solution vector obtained in the first step. If the newly obtained solution vector and the solution vector obtained by the previous iteration are repeated, N _rep =N _rep +1, and the value of l _rep is calculated and updated at the same time. The value of the taboo period P is adjusted to P+1. But if for a fixed β, the current value of P exceeds the value of βl _rep , then let P=max(1,P-1), that is

If f(x ^(m+1) )<f(g ^(m) ), update some tabu values of the tabu matrix and the optimal solution vector according to (13) and (14):

T((u′-1)M+q′,v′)=T((u′-1)M+q″,v″)=0 (13)

lflag=0,g ^(m+1) =x ^(m+1) (14)

Otherwise, update according to equations (15) and (16):

T((u′-1)M+q′,v′)=T((u′-1)M+q″,v″)=P+1 (15)

lflag=1,g ^(m+1) =g ^(m+1) (16)

Step 3: Update the value of the taboo matrix T according to equation (17)

T=max{T-1,0} (17)

At the same time, according to formula (18) and the new value of f ^(m) , as follows

where R _u' denotes the u'th column of R. At this point, it is judged whether the termination condition is met, if so, the iteration process is terminated, and the detection result is returned as the final solution vector; if the termination condition is not met, skip to the first step and continue to execute the above three steps.

B. MPD detection algorithm

The Message Passing Detection (MPD) algorithm is a low-complexity message passing method utilizing the channel hardening phenomenon.

Multiplying H ^T by y=Hx+n and dividing by N, we get

Z=Jx+V (19)

其中

和

g_i被假设是一个服从均值为μ_i和方差为

高斯分布Wherein Z= ^HT y/N, J= ^HT H/N, V= ^HT n/N. The ith element of Z is

in

and

g _i is assumed to be a subject with mean μ _i and variance of

Gaussian distribution

计算E(g_i)和Var(g_i)，需要计算p_j(s_k)的后验概率，即x_j成为s_k∈θ的概率。符号x_j的对数似然比定义为

其中in

To calculate E( _gi ) and Var( _gi ), we need to calculate the posterior probability of p _j (s _k ), that is, the probability that x _j becomes s _k ∈ θ. The log-likelihood ratio of the symbol x _j is defined as

in

此外

also

Finally, the symbol _xj is determined as the maximum probability of _sk .

C. The basic principle of RTS-MPD joint algorithm

为RTS检测器的输出、

为

调制的字母表并且x的取值来自此集合。考虑符号与比特之间的映射，这里将

的每一项的值写成成比特的线性组合：Define x as the transmission vector,

is the output of the RTS detector,

for

The modulated alphabet and the value of x comes from this set. Consider the mapping between symbols and bits, here will be

The value of each term is written as a linear combination of bits:

Q是星座图中点的个数，

为

的第i个分量，根据前面介绍的RTS检测算法可知，RTS检测算法的输出的解向量为局部最小值。所以，

满足以下不等式：in

Q is the number of points in the constellation diagram,

for

The i-th component of , according to the RTS detection algorithm introduced above, the solution vector output by the RTS detection algorithm is a local minimum value. so,

Satisfy the following inequalities:

e_i表示特征矩阵的第i列。定义h_i表示H的第i列、

则式(25)可表示为in

e _i represents the i-th column of the feature matrix. Define h _i to represent the i-th column of H,

The formula (25) can be expressed as

In the above formula (26), f _ij is the element of the i-th row and j-column of F. In the case of high signal-to-noise ratio, ignoring the noise, it can be further transformed into

Among them, f _i is the ith row of F, and in the Rayleigh fading channel, f _ii obeys a chi-square distribution with 2K degrees of freedom and a mean of N. When i≠j, it can be known from the central limit theorem that f _ii is close to 0 mean, and the variance is a normal distribution of K/4. Equation (27) can be transformed into

若

则：In formula (28):

like

but:

FIG. 2 shows a schematic flowchart of the signal detection method for a massive MIMO system according to the present invention, and the implementation flow of the signal detection method for a massive MIMO system according to an embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

Please refer to FIG. 2, the signal detection method of the massive MIMO system includes:

具体地，单天线用户1通过发射天线11发射信号x，发射信号x经过调制，在经过信道传到基站2，生成接收信号y，作为所述第一初始信号y，将接收信号y经过RTS检测器进行RTS检测，以获取初始估计向量

Step S10, perform active tabu search detection on the first initial signal to obtain an initial estimated vector

Specifically, the single-antenna user 1 transmits a signal x through the transmit antenna 11, the transmit signal x is modulated, and then transmitted to the base station 2 through a channel to generate a received signal y, which is used as the first initial signal y, and the received signal y is detected by RTS the RTS detection to obtain the initial estimate vector

消除所述第一初始信号中的干扰信号，以获取第二初始信号

具体地，可通过式(24)得到输出的比特值

i＝1，…,2K，

由比特值

得出总的信号间干扰(也即信号间总干扰)：

其中

H为信道增益矩阵；用所述接收信号y减去所述信号间总干扰

以获取所述第二初始信号

也即

Step S20, according to the initial estimation vector

Eliminate the interference signal in the first initial signal to obtain the second initial signal

Specifically, the output bit value can be obtained by formula (24)

i=1,...,2K,

by bit value

The total inter-signal interference (that is, the total inter-signal interference) is obtained:

in

H is the channel gain matrix; subtract the total interference between the signals from the received signal y

to obtain the second initial signal

that is

进行消息传递检测以获取输出向量估计

具体地，将步骤S20中获取的第二初始信号

输入到MPD检测模块进行MPD检测可以得到得到输出向量估计为

Step S30, for the second initial signal

Do message passing detection to get output vector estimates

Specifically, the second initial signal obtained in step S20 is

Input to the MPD detection module for MPD detection, the output vector can be estimated as

对符号向量x进行重构，以获取符号向量重构值

其中，

Step S40, estimate according to the output vector

Reconstruct the symbol vector x to obtain the symbol vector reconstruction value

in,

作为检测结果输出。具体地，将步骤S40获取的符号向量重构值

作为RTS检测器的输入，重复执行执行步骤S10-S40的步骤，直到迭代结束，其中，迭代结束的依据是迭代次数达到三次或者大于三的任一整数值。Step S50, performing an iterative operation using the reconstructed value of the symbol vector as the input of the active tabu search, until the iteration conditions are met, and the final reconstructed value of the symbol vector after the iteration ends

output as the detection result. Specifically, reconstruct the value of the symbol vector obtained in step S40

As the input of the RTS detector, the steps of performing steps S10-S40 are repeatedly performed until the iteration ends, where the iteration is terminated based on the number of iterations reaching three or any integer value greater than three.

As shown in FIG. 3 , an embodiment of the present invention further discloses a massive MIMO system signal detection system 3 . The massive MIMO system signal detection system 3 includes an active taboo search module 31 , an interference signal elimination module 32 , and a vector estimation acquisition module 31 . module 33 , symbol vector reconstruction module 34 and iterative output module 35 . an active tabu search module, configured to perform active tabu search detection on the first initial signal to obtain an initial estimation vector; the interference signal elimination module 31 is used to eliminate interference signals in the first initial signal according to the initial estimation vector, to obtain the second initial signal; the vector estimation obtaining module 32 is used to perform message passing detection on the second initial signal to obtain an output vector estimate; the symbol vector reconstruction module 33 is used to estimate the symbol vector according to the output vector estimation Reconstruction is performed to obtain the reconstruction value of the symbol vector; the iterative output module 34 is used to perform an iterative operation using the reconstructed value of the symbol vector as the input of the active tabu search, and the final reconstructed value of the symbol vector after the iteration ends output as the detection result.

It should be noted that the massive MIMO system signal detection system 3 of the present invention is a system corresponding to the above-mentioned massive MIMO system signal detection method, and the functional modules in the massive MIMO system signal detection system 3 respectively correspond to massive MIMO system signals corresponding steps in the detection method. The massive MIMO system signal detection system 3 of the present invention can be implemented in cooperation with the massive MIMO system signal detection method. The related technical details mentioned in the massive MIMO system signal detection method of the present invention are still valid in the massive MIMO system signal detection system 3, and are not repeated here in order to reduce repetition. Correspondingly, the relevant technical details mentioned in the massive MIMO system signal detection system 3 of the present invention can also be applied to the above-mentioned massive MIMO system signal detection method.

It should be noted that the above-mentioned functional modules may be integrated into one physical entity in whole or in part during actual implementation, or may be physically separated. And these units can all be implemented in the form of software calling through processing elements; they can also all be implemented in hardware; some units can also be implemented in the form of calling software through processing elements, and some units can be implemented in hardware. In addition, all or part of these units can be integrated together, and can also be implemented independently. The processing element described here may be an integrated circuit with signal processing capability. In the implementation process, each step of the above-mentioned method or each of the above-mentioned modules can be completed by an integrated logic circuit of hardware in the element of the processor 41 or an instruction in the form of software.

It should be noted that, as shown in FIG. 4 , the signal detection method of the massive MIMO system of the present invention can also be implemented by a control unit 4 arranged on the base station (of course, it can also be arranged on other main bodies). 4 includes a memory 43 and a processor 41 that are connected to each other, the memory 43 stores program instructions, and when the program instructions are executed by the processor 41, the above-mentioned massive MIMO system signal detection method is implemented. It should be noted that, when communication with the outside is required, the control unit 4 further includes a communicator 42 , and the communicator 42 is connected to the processor 41 .

The above-mentioned processor 41 may be a general-purpose processor, including a central processing unit (Central Processing Unit, referred to as CPU), a network processor (Network Processor, referred to as NP), etc.; may also be a digital signal processor (Digital Signal Processing, referred to as DSP), dedicated integrated Circuit (Application Specific Integrated Circuit, referred to as ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, referred to as FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components; the above-mentioned memory 43 may contain random The access memory (Random Access Memory, RAM for short) may also include a non-volatile memory (Non-volatile Memory), such as at least one disk memory.

It should be noted that the control unit 4 in the above-mentioned memory 43 may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several The instructions are used to cause a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention.

The present invention can also provide a storage medium, which stores a program, and when the program is executed by the processor 41, implements the above-mentioned massive MIMO system signal detection method; the storage medium includes all forms of non-volatile memory, medium and Memory devices, including, for example: semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

In order to verify the signal detection method of the massive MIMO system of the present invention, the inventor has conducted a simulation experiment. This embodiment presents the Matlab Monte Carlo simulation results. Simulation parameters: QAM is selected as the modulation mode; the antenna scale is 16×16, 32×32, 64×64. The transmission channel is a Rayleigh fading channel, the channel characteristics are time-varying, and the noise is additive white Gaussian noise. This section mainly studies the comparison of bit error rates between RTS and RTS-MPD algorithms under different modulation orders and different antenna configurations.

Figure 5 plots the performance of the RTS detection algorithm when the modulation scheme is 4QAM and the antenna configurations are 16×16, 32×32, and 64×64, respectively. The SISO AWGN performance curve is also added to the figure for comparison. It can be seen from the figure that RTS has good performance in the case of low-order modulation, and with the increase of the number of antennas, its performance improves significantly, which is close to SISO AWGN. Therefore, RTS is suitable for signal detection in massive MIMO systems with a low modulation order and a large number of antennas.

Figure 6 shows the performance curve of the RTS algorithm under high-order modulation. It can be seen from Figure 5 that in the case of 4-QAM (Quadrature Amplitude Modulation, quadrature amplitude modulation) modulation, the bit error rate of the RTS algorithm is about 11db when the signal-to-noise ratio is about 11db. It can reach 10 ^-3 , but it can be seen from Figure 6 that under 16-QAM high-order modulation, the performance of the RTS algorithm is not good, and it can only reach 10 ^-3 when the signal-to-noise ratio is as high as 30; in Figure 6, also It can be seen that when the modulation order becomes higher, the detection performance of the RTS algorithm further deteriorates significantly, and the performance of the RTS algorithm under high-order modulation is very poor.

The test parameters in the simulation test are shown below. In order to simply and intuitively verify the detection performance of the RTS-MPD algorithm introduced, the experimental simulation comparison diagrams of the RTS algorithm and the RTS-MPD algorithm under 16QAM and 64QAM modulation are given, as shown in Figure 7. shown. It can be seen from the figure that in the case of 16QAM modulation, when BER=10 ^-3 , the RTS-MPD joint algorithm is better than the RTS algorithm, and the performance is improved by about 1db; in the case of 64QAM modulation, when BER=10 When ^-3 , the RTS-MPD joint algorithm is better than the RTS algorithm, and the performance improvement is more obvious, reaching about 2db.

To sum up, the method, system, base station and storage medium for massive MIMO system signal detection of the present invention propose a joint algorithm of active tabu search and message passing detection for massive MIMO signal detection under high-order modulation. The algorithm is integrated into the RTS algorithm, and the MPD algorithm is used to correct the error of the last bit of the symbol, which can improve the poor performance of the RTS algorithm under high-order modulation, and has a lower complexity.

In the description herein, numerous specific details are provided, such as examples of components and/or methods, in order to provide a thorough understanding of embodiments of the present invention. However, one skilled in the art will recognize that embodiments of the invention may be practiced without one or more of the specific details or with other devices, systems, assemblies, methods, components, materials, parts, and the like. In other instances, well-known structures, materials, or operations are not specifically shown or described in detail to avoid obscuring aspects of the embodiments of the invention.

Reference throughout this specification to "one embodiment," "anembodiment," or "a specific embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment includes In at least one embodiment of the invention, and not necessarily in all embodiments. Thus, the phrases "in one embodiment", "in an embodiment" or "in a specific embodiment" are used in various places throughout the specification. Appearances are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics of any particular embodiment of the present invention may be combined in any suitable manner with one or more other embodiments. It should be understood that other variations and modifications of the embodiments of the invention described and illustrated herein are possible in light of the teachings herein and are to be considered part of the spirit and scope of the invention.

It should also be understood that one or more of the elements shown in the figures may also be implemented in a more discrete or integrated manner, or even removed as inoperable in certain circumstances or as may be useful according to a particular application. Provided.

Additionally, any identifying arrows in the accompanying drawings should be regarded as illustrative only and not restrictive unless expressly indicated otherwise. In addition, the term "or" as used herein is generally intended to mean "and/or" unless stated otherwise. Combinations of components or steps will also be considered to have been specified where the term is foreseen because the ability to provide separation or combination is unclear.

As used in the description herein and throughout the claims below, "a (a)," "an (an)," and "the (the)" include plural references unless otherwise indicated. Likewise, as used in the description herein and throughout the claims below, unless otherwise specified, the meaning of "in" includes "in" and "on" .

The above description of illustrated embodiments of the present invention, including what is described in the Abstract, is not intended to be exhaustive or to limit the invention to the precise form disclosed herein. While specific embodiments of, and examples for, the invention are described herein for illustrative purposes only, various equivalent modifications are possible within the spirit and scope of the invention, as those skilled in the art will recognize and appreciate of. As indicated, these modifications may be made to the present invention in light of the foregoing description of the described embodiments of the present invention and are intended to be within the spirit and scope of the present invention.

The systems and methods have generally been described herein with details that are helpful in understanding the invention. Furthermore, various specific details have been set forth in order to provide a general understanding of embodiments of the present invention. One skilled in the relevant art will recognize, however, that embodiments of the invention may be practiced without one or more of the specific details, or with other devices, systems, accessories, methods, components, materials, parts, etc. practice. In other instances, well-known structures, materials and/or operations have not been specifically shown or described in detail to avoid obscuring aspects of the embodiments of the invention.

Thus, although the invention has been described herein with reference to specific embodiments thereof, freedom of modification, various changes and substitutions is within the above disclosure, and it should be understood that, in certain circumstances, without departing from the scope and scope of the proposed invention, Some features of the present invention will be employed without the corresponding use of other features in the spirit of the present invention. Therefore, many modifications may be made to adapt a particular environment or material to the essential scope and spirit of the invention. It is not intended that the invention be limited to the specific terms used in the following claims and/or the specific embodiments disclosed as the best modes contemplated for carrying out the invention, but the invention is to be included within the scope of the appended claims any and all embodiments and equivalents within. Accordingly, the scope of the present invention should be determined only by the appended claims.

Claims (6)

1. a massive MIMO system signal detection method, is characterized in that, comprises: Perform active tabu search detection on the first initial signal to obtain an initial estimated vector; Obtain output bit values according to the initial estimation vector; Obtain the total interference between signals according to the output bit value; subtracting the total inter-signal interference from the first initial signal to obtain a second initial signal; performing message passing detection on the second initial signal to obtain an output vector estimate; Reconstructing the symbol vector according to the output vector estimate to obtain a symbol vector reconstruction value; Performing an iterative operation using the symbol vector reconstruction value as the input of the active tabu search, and outputting the final symbol vector reconstruction value after the iteration ends as a detection result; Wherein, the output bit value is obtained according to the following formula

in,

Q is the number of points in the constellation diagram, K is the number of users in the uplink of the MIMO system,

is the initial estimate vector

the ith component of ; In the step of obtaining the total inter-signal interference according to the output bit value, the total inter-signal interference

The expression is as follows:

in,

H is the channel gain matrix. 2. The signal detection method for a massive MIMO system according to claim 1, wherein, in the step of reconstructing the symbol vector according to the output vector estimate to obtain a reconstructed value of the symbol vector, the symbol vector repeats construct value

The expression is as follows:

in,

Estimated for the output vector. 3 . The signal detection method for a massive MIMO system according to claim 1 or 2 , wherein, in the step of performing an iterative operation using the symbol vector reconstruction value as the input of the active tabu search, the number of iterations is greater than or equal to three times. 4 . 4. A massive MIMO system signal detection system, characterized in that, comprising: an active tabu search module, which is used to perform active tabu search detection on the first initial signal to obtain an initial estimated vector; an interference signal elimination module, configured to obtain an output bit value according to the initial estimation vector, obtain the total interference between signals according to the output bit value, and subtract the total interference between signals from the first initial signal to obtain a second initial signal, where the output bit value is obtained according to

in,

Q is the number of points in the constellation diagram, K is the number of users in the uplink of the MIMO system,

is the initial estimate vector

the ith component of ; In the step of obtaining the total inter-signal interference according to the output bit value, the total inter-signal interference

The expression is as follows:

in,

H is the channel gain matrix; a vector estimation obtaining module, configured to perform message passing detection on the second initial signal to obtain an output vector estimation; a symbol vector reconstruction module, configured to reconstruct the symbol vector according to the output vector estimation to obtain a symbol vector reconstruction value; The iterative output module is configured to perform an iterative operation using the reconstructed value of the symbol vector as an input of the active tabu search, and output the final reconstructed value of the symbol vector after the iteration is completed as a detection result. 5. A base station, wherein the base station comprises: The base station body, a plurality of receiving antennas and control units arranged on the base station body; The receiving antenna is used to receive a symbol vector sent by a user of the MIMO system, and transmit the symbol vector as a first initial signal to the control unit for signal detection; the control unit includes a processor coupled to each other and a A memory, where a computer program is stored in the memory, and when the computer program stored in the memory is executed by the processor, the signal detection method for a massive MIMO system according to any one of claims 1-3 is implemented. 6 . A storage medium, characterized by comprising a program, which, when the program runs on a computer, causes the computer to execute the signal detection method for a massive MIMO system according to any one of claims 1 to 3 .

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