CN105978666B - Space-time channel optimization MIMO wireless transmission system transmitter and processing method - Google Patents

Abstract

The present invention relates to communication technology. The present invention is to significantly improve the data transmission rate, system capacity and spectrum efficiency of the MIMO system, and provides a space-time channel optimization MIMO wireless transmission system transmitter and a processing method, and its technical solution can be summarized as: space-time channel optimization MIMO wireless Transmission system transmitter, including multi-channel signal transmitter, multiple virtual channel vector modules, feedback information receiver and space-time optimization module, each virtual channel vector module corresponds to at least one signal input terminal, and each signal input terminal corresponds to only one The virtual channel vector module, the output end of each virtual channel vector module is only connected to one signal transmitting end one by one, the feedback information receiving end is connected to the space-time optimization module, and the space-time optimization module is connected to each virtual channel vector module. A timing optimization module is connected to each signal input terminal. The beneficial effect of the present invention is that the data transmission rate is improved, and it is suitable for MIMO systems.

Description

Space-time channel optimization MIMO wireless transmission system transmitter and processing method

Technical Field

The present invention relates to communication technology, and in particular to MIMO wireless transmission technology.

Background Art

MIMO (Multiple Input Multiple Output) technology uses the wireless channel formed by the different spatial positions of multiple antennas at the transmitter and receiver to transmit multiple data streams in parallel, which can significantly improve the data transmission rate and system capacity of the wireless communication system. It is an important development direction of modern wireless communication technology and has broad application prospects.

及N_T根发射天线(N_T＝1，2，…),x_m(t)∈{±1}(m＝1,2,...,N_T)；每路数据流x_m(t)经射频调制后，变为高频信号，再进行放大后由相应的天线Ant.m(m＝1，2,…，N_T)发射出去；接收端配置L_R根接收天线(L_R＝1，2，…)，每根天线的射频信号经放大及解调器后得到基带信号；信号检测及处理模块对来自不同天线的L_R路基带信号进行优化合并、检测、判决等处理，最后得到N_T路输出数据流y₁(t)，y₂(t)，...，

y_m(t)∈{±1}(m＝1,2,...,N_T)，y_m(t)是发射端输入数据流x_m(t)的估计值

即

通常情况下，L_R≥N_T。The system block diagram of the existing MIMO system is shown in FIG1 , where the transmitter has N _T input baseband data streams x ₁ (t), x ₂ (t), ...,

and _NT transmitting antennas ( _NT = 1, 2, ...), _xm (t)∈{±1}(m=1,2,..., _NT ); each data stream _xm (t) is modulated by radio frequency and becomes a high-frequency signal, which is then amplified and transmitted by the corresponding antenna Ant.m (m=1,2,..., _NT ); the receiving end is configured with _LR receiving antennas ( _LR = 1,2,...), and the RF signal of each antenna is amplified and demodulated to obtain a baseband signal; the signal detection and processing module optimizes, merges, detects, and judges the _LR baseband signals from different antennas, and finally obtains _NT output data streams _y1 (t), _y2 (t),...,

y _m (t)∈{±1}(m＝1,2,..., _NT ), y _m (t) is the estimated value of the input data stream x _m (t) at the transmitter

Right now

Usually, _LR ≥ _NT .

Assume h _lm represents the spatial wireless channel between the lth receiving antenna and the mth transmitting antenna, then the signal on the lth receiving antenna is:

Where n _l (t) is the Gaussian white noise of the lth receiving antenna. In order to detect a certain data stream x _i (t), the maximum signal-to-noise ratio combining method can be used at the receiving end, and its calculation formula is:

At the receiving end, the channel h _lm can be estimated, and then the signals of each receiving antenna are combined to obtain the decision variable, that is:

Assume Q _D (.) is the decision function, Q _D (.)∈{±1}. Then:

代表有用信号分量；Here, Re(.) represents a real number operation.

Represents the useful signal component;

则代表来自其它数据流的干扰及各接收天线的噪声，只要将这些干扰及噪声控制在一定的范围内，接收端就可以正确地检测出各个发送的数据流。

It represents the interference from other data streams and the noise of each receiving antenna. As long as these interferences and noises are controlled within a certain range, the receiving end can correctly detect each transmitted data stream.

In the existing MIMO system, the transmitter uses multiple antennas to simultaneously transmit multiple signals or data streams in the same frequency band, which can improve the data transmission rate or increase the system capacity. An N×N (N transmitting antennas and N receiving antennas) MIMO system can increase the data transmission rate by N times at most, or increase the system capacity by N times. The more the data transmission rate is improved or the system capacity is increased, the more the number of antennas will increase. However, in practical applications, the increase in the number of antennas is often restricted by factors such as cost and spatial scale, thus limiting the degree of improvement in system performance. That is, relative to the increase in the number of additional antennas, the investment in components and costs, the improvement in data transmission rate, system capacity or performance obtained is very limited and not ideal. This is a shortcoming of the existing technical solutions. In addition, there is often correlation between wireless channels. The correlation of channels will significantly weaken the performance of the MIMO system, making it difficult to exert its potential advantages. This is another shortcoming of the existing technical solutions.

Summary of the invention

The purpose of the present invention is to significantly improve the data transmission rate, system capacity and spectrum efficiency of the MIMO system, optimize the transmission channel, improve the system performance, and provide a space-time channel optimized MIMO wireless transmission system transmitter and processing method.

The present invention solves the technical problem and adopts a technical solution of: a space-time channel optimization MIMO wireless transmission system transmitter, including a multi-channel signal transmitter, each of which includes a modulation filter amplifier module and a transmitting antenna, characterized in that it also includes a plurality of virtual channel vector modules, a feedback information receiving end and a space-time optimization module, each virtual channel vector module corresponds to at least one signal input end, each signal input end corresponds to only one virtual channel vector module, the output end of each virtual channel vector module is connected to only one signal transmitter, the feedback information receiving end is connected to the space-time optimization module, the space-time optimization module is connected to each virtual channel vector module, and the space-time optimization module is connected to each signal input end;

The virtual channel vector module is used to perform a complex weighting operation on the baseband signal input from each signal input terminal connected thereto according to the set complex weighting value, and combine all the complex weighted baseband signals and transmit them to the corresponding signal transmitting terminal;

The feedback information receiving end is used to receive feedback information sent by the system receiving end and transmit it to the space-time optimization module;

The space-time optimization module is used to calculate each complex weighted value in each virtual channel vector module using a space-time optimization algorithm according to the received feedback information, and set it.

Specifically, the virtual channel vector module includes complex weighted modules corresponding to the number of signal input terminals and an adder. The input terminal of each complex weighted module is connected one-to-one with a signal input terminal, the output terminal of each complex weighted module is connected one-to-one with an input terminal of the adder, the output terminal of each adder is connected one-to-one with a signal transmitting terminal as the output terminal of the virtual channel vector module, and the space-time optimization module is connected to each complex weighted module respectively.

Furthermore, the baseband signals input to each of the signal input terminals are different; or the baseband signals input to some of the signal input terminals are the same, and the baseband signals input to other signal input terminals are different.

Specifically, the number of signal input terminals corresponding to each virtual channel vector module may be the same or different.

Furthermore, the feedback information includes channel identification and system status information.

Specifically, the channel identification and system status information includes a signal-to-noise ratio, a bit error rate, an error value, and a channel estimation value.

The processing method of the transmitting end of the space-time channel optimization MIMO wireless transmission system is applied to the transmitting end of the space-time channel optimization MIMO wireless transmission system, and is characterized in that it includes the following steps:

A. The signal input end receives an input baseband signal and transmits the baseband signal to its corresponding virtual channel vector module;

B. Each virtual channel vector module performs a complex weighting operation on the baseband signal input from each signal input terminal connected thereto according to the set complex weighting value, and combines all the complex weighted baseband signals and transmits them to the corresponding signal transmitting terminal for transmission;

C. The feedback information receiving end receives the feedback information sent by the system receiving end in real time and transmits it to the space-time optimization module. The space-time optimization module uses the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module based on the received feedback information, sets it, and returns to step B.

Specifically, in step B, the signal output by the mth virtual channel vector module is

w_mn表示每个基带输入信号x_mn(t)所对应的虚拟信道，具体为：

x_mn(t)为复数信号，N_T为发射天线数量，也为输入信号向量的数量，N_m指代第m个虚拟信道向量模块所对应的信号输入端的数量，n＝1,2,……,N_m，x_mn(t)是指第m个虚拟信道向量模块中第n个信号输入端输入的基带输入信号，向量x_m(t)是指第m个虚拟信道向量模块的输入信号向量，表示为：

Wherein, vector _wm represents the mth virtual channel vector, including _Nm virtual channels _wmn , which is expressed as:

w _mn represents the virtual channel corresponding to each baseband input signal x _mn (t), specifically:

x _mn (t) is a complex signal, _NT is the number of transmitting antennas, which is also the number of input signal vectors, _Nm refers to the number of signal input terminals corresponding to the mth virtual channel vector module, n = 1, 2, ..., _Nm , _xmn (t) refers to the baseband input signal input to the nth signal input terminal in the mth virtual channel vector module, and the vector _xm (t) refers to the input signal vector of the mth virtual channel vector module, which is expressed as:

Furthermore, in step C, the space-time optimization module calculates each complex weighted value in each virtual channel vector module using a space-time optimization algorithm according to the received feedback information by:

The space-time optimization module uses the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module according to the received feedback information. The calculation formula is:

这里，向量w_m表示第m个虚拟信道向量，包括N_m个虚拟信道w_mn，表示为：

w_mn表示每个基带输入信号x_mn(t)所对应的虚拟信道，具体为：

N_T为发射天线数量，也为输入信号向量的数量，N_m指代第m个虚拟信道向量模块所对应的信号输入端的数量，n＝1,2,……,N_m，x_mn(t)是指第m个虚拟信道向量模块中第n个信号输入端输入的基带输入信号；Wherein, w _opt is the optimal value of the complex weight vector w to be obtained, and the complex weight vector w is also called the system virtual channel vector, which is expressed as:

Here, the vector _wm represents the mth virtual channel vector, including _Nm virtual channels _wmn , which is expressed as:

w _mn represents the virtual channel corresponding to each baseband input signal x _mn (t), specifically:

_NT is the number of transmitting antennas, and also the number of input signal vectors. _Nm refers to the number of signal input terminals corresponding to the mth virtual channel vector module, n=1, 2, ..., _Nm . _xmn (t) refers to the baseband input signal input to the nth signal input terminal in the mth virtual channel vector module.

是对应于矩阵R的最大特征值的特征向量，且

is the eigenvector corresponding to the largest eigenvalue of the matrix R, and

信号传输矩阵，是指：R is a

Signal transmission matrix refers to:

Where, R _ij =E[ _xi (t) _xj (t) ^H ] is a _Ni × _Nj input correlation matrix, i=1,2,…, _NT , j=1,2,…, _NT ;

所有输入信号向量组成系统发射信号向量

The vector x _m (t) refers to the input signal vector of the mth virtual channel vector module, which includes N _m baseband input signals x _mn (t) (n=0, 1, ..., N _m ), where x _mn (t) is a complex signal, expressed as:

All input signal vectors constitute the system transmission signal vector

λ _ij =E[ _hi ^H h _j ] is a scalar, and the spatial wireless channel matrix of the system is expressed as

其中

h_lm表示第l根接收天线到第m根发射天线之间的空间无线信道，l＝1,2,…,L_R，L_R为接收天线数量。H can be simplified as

in

h _lm represents the spatial wireless channel between the lth receiving antenna and the mth transmitting antenna, l = 1, 2, …, _LR , _LR is the number of receiving antennas.

Specifically, in step C, the space-time optimization module may calculate each complex weighted value in each virtual channel vector module using a space-time optimization algorithm according to the received feedback information by using a particle swarm algorithm to search for a global optimal system virtual channel vector, wherein:

这里，向量w_m表示第m个虚拟信道向量，包括N_m个虚拟信道w_mn，表示为：

w_mn表示每个基带输入信号x_mn(t)所对应的虚拟信道，具体为：

N_m指代第m个虚拟信道向量模块所对应的信号输入端的数量，n＝1,2,……,N_m，x_mn(t)是指第m个虚拟信道向量模块中第n个信号输入端输入的基带输入信号，向量x_m(t)是指第m个虚拟信道向量模块的输入信号向量，其包括N_m个基带输入信号x_mn(t)(n＝0,1,…,N_m)，x_mn(t)为复数信号，表示为

Assume that the number of transmitting antennas is _NT , which is also the number of input signal vectors, the number of receiving antennas is _LR , and w is the system virtual channel vector, which can be expressed as:

Here, the vector _wm represents the mth virtual channel vector, including _Nm virtual channels _wmn , which is expressed as:

w _mn represents the virtual channel corresponding to each baseband input signal x _mn (t), specifically:

N _m refers to the number of signal input terminals corresponding to the mth virtual channel vector module, n = 1, 2, ..., N _m , x _mn (t) refers to the baseband input signal input to the nth signal input terminal in the mth virtual channel vector module, and the vector x _m (t) refers to the input signal vector of the mth virtual channel vector module, which includes N _m baseband input signals x _mn (t) (n = 0, 1, ..., N _m ), and x _mn (t) is a complex signal, which can be expressed as

Assume the number of particles is S _E , and take each system virtual channel vector of the transmitting end of the space-time channel optimization MIMO wireless transmission system as the position of a particle;

其中，

是第s个粒子位置中的第m个虚拟信道向量；At the kth iteration, the position of the sth particle, that is, the sth system virtual channel vector is expressed as

in,

is the mth virtual channel vector in the sth particle position;

At the kth iteration, the moving speed of the sth particle is expressed as:

其中，

是虚拟信道向量

的相应移动速度；

in,

is the virtual channel vector

The corresponding moving speed;

表示在第k次迭代时刻，第s个粒子迄今为止搜索到的个体最优位置，其中，

是第s个粒子个体最优位置中的第m个虚拟信道向量；make

represents the individual optimal position searched by the sth particle so far at the kth iteration, where

is the mth virtual channel vector in the optimal position of the sth particle individual;

表示在第k次迭代时刻，整个粒子群迄今为止搜索到的全局最优位置，其中，

是全局最优位置中的第m个虚拟信道向量；make

represents the global optimal position searched by the entire particle swarm so far at the kth iteration, where

is the mth virtual channel vector in the global optimal position;

表示参考信号向量，其中x_Rm(t)是对应于输入信号向量x_m(t)的第m个参考信号向量，将参考信号向量在w^(s)(k)作用条件下的估计值表示为

其相应的误差表示为

同理，在w^(s)(k)作用条件下的误码率BER表示为

Let the reference signal occupy one time slot in each data frame,

represents the reference signal vector, where x _Rm (t) is the mth reference signal vector corresponding to the input signal vector x _m (t), and the estimated value of the reference signal vector under the action of w ^(s) (k) is expressed as

The corresponding error is expressed as

Similarly, the bit error rate BER under the action of w ^(s) (k) is expressed as

Therefore, the specific steps of using the particle swarm algorithm to search for the global optimal system virtual channel vector are as follows:

Step 1. At the transmitting end of the space-time channel optimization MIMO wireless transmission system, set constants according to the actual communication environment: c ₁ , c ₂ , r ₁ , r ₂ , ε ₁ , ε ₂ , A, B, _GT , v _min , v _max , where c ₁ and c ₂ are learning factors, which enable particles to have the ability to self-summarize and learn from excellent individuals in the group, so as to approach their own historical optimal points and the historical optimal points within the group; r ₁ and r ₂ are random numbers between [0, 1]; ε ₁ and ε ₂ are smaller constants set according to the actual communication environment; A is the initial inertia weight; B is the update coefficient of the inertia weight; _GT is the virtual channel gain constraint constant; v _min and v _max are the minimum and maximum speeds of particle movement, respectively, which limit the speed range of particles;

和

采用得到的每一个w^(s)(0)，分时隙发送一个参考信号序列x_R(t)，一共S_E个不同时隙，每个时隙采用一个不同的位置向量w^(s)(0)(s＝1,2,…,S_E)；Step 2: At the transmitting end of the space-time channel optimization MIMO wireless transmission system, set k = 0, randomly initialize the position and moving speed of each particle, and obtain

and

Using each obtained w ^(s) (0), a reference signal sequence x _R (t) is sent in time slots, for a total of S _E different time slots, each time slot using a different position vector w ^(s) (0) (s = 1, 2, ..., S _E );

然后用不同的位置向量w^(s)(0)计算误差：Step 3: Detect the reference signal at the receiving end of the system and obtain the vector estimation value of S _E reference signals, that is,

The error is then calculated using different position vectors w ^(s) (0):

或者计算

Or calculate

或

到空时信道优化MIMO无线传输系统发射端；As a feedback signal, send each

or

To the transmitter of the space-time channel optimized MIMO wireless transmission system;

Step 4: Set the best individual position at the transmitting end of the space-time channel optimized MIMO wireless transmission system: p ^(s) (0) = w ^(s) (0) (s = 1, 2, ..., S _E ), find the minimum feedback signal value among all feedback signals, and assume that the particle position corresponding to the minimum feedback signal value is w ^(g) (0), then the best global position is b (0) = w ^(g) (0);

Step 5: Update the inertia weight at the transmitter of the space-time channel optimized MIMO wireless transmission system: α = B(k+1) + A. For each particle, calculate its velocity and position vector as follows:

v ^(s) (k+1)=αv ^(s) (k)+c ₁ r ₁ [p ^(s) (k)-w ^(s) (k)]+c ₂ r ₂ [b(k)- w ^(s) (k)]

w ^(s) (k+1)＝w ^(s) (k)+v ^(s) (k+1)

Where s = 1, 2, …, S _E , the value of each element in the vector v ^(s) (k+1) is in the range [v _min , v _max ]. In addition, the transmit power is limited as follows:

Then, the reference signal is sent to the system receiving end in different time slots, a total of S _E time slots, and each time slot uses a different position vector w ^(s) (k+1) (s = 1, 2, ..., S _E );

然后，用不同的位置向量w^(s)(k+1)计算误差：Step 6: Detect the reference signal at the receiving end of the system and obtain the vector estimation value of S _E reference signals, that is,

Then, the error is calculated using different position vectors w ^(s) (k+1):

或者计算

Or calculate

或

到空时信道优化MIMO无线传输系统发射端；Then use it as a feedback signal to send each

or

To the transmitter of the space-time channel optimized MIMO wireless transmission system;

或者

则p^(s)(k+1)＝w^(s)(k+1)；否则，P^(s)(k+1)＝P^(s)(k)；Step 7: At the transmitting end of the space-time channel optimization MIMO wireless transmission system, the best individual position is updated according to the feedback signal. If

or

Then p ^(s) (k+1) = w ^(s) (k+1); otherwise, P ^(s) (k+1) = P ^(s) (k);

或者

则最佳全局位置为b(k+1)＝w^(g)(k+1)；否则，则最佳全局位置为b(k+1)＝b(k)；Step 8: At the transmitting end of the space-time channel optimized MIMO wireless transmission system, the best global position is updated according to the feedback signal, and the minimum feedback signal value is found among all feedback signals. The particle position corresponding to the minimum feedback signal value is set to be w ^(g) (k+1). If

or

Then the best global position is b(k+1)=w ^(g) (k+1); otherwise, the best global position is b(k+1)=b(k);

Step 9: If e _R,b(k+1) <ε ₁ or BER _b(k+1) <ε ₂ , the operation stops and data transmission begins; otherwise, k→k+1, and returns to step 5.

The beneficial effect of the present invention is that, in the scheme of the present invention, by optimizing the transmitting end and processing method of the MIMO wireless transmission system through the above-mentioned space-time channel, the number of transmission channels of each transmitting antenna in the MIMO wireless communication system can be greatly increased, thereby increasing the number of signal or data streams transmitted by each antenna, and thus significantly improving the data transmission rate, system capacity and spectrum efficiency of the MIMO system without increasing the number of antennas. When transmitting the same data stream, the MIMO system of the present invention requires fewer antennas than the existing MIMO system, thereby reducing the number of transmitting antennas, reducing system complexity, reducing system costs, and performing dynamic virtual channel adjustment according to feedback information, significantly reducing the receiving bit error rate, and improving the reliability of signal transmission.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a system block diagram of an existing MIMO wireless communication system.

FIG2 is a system block diagram of the transmitting end of the space-time channel optimized MIMO wireless transmission system of the present invention.

FIG3 is a system block diagram of a space-time channel optimized MIMO wireless transmission system according to an embodiment of the present invention.

DETAILED DESCRIPTION

The technical solution of the present invention is described in detail below in conjunction with the embodiments and drawings.

FIG2 is a system block diagram of the transmitting end of the space-time channel optimized MIMO wireless transmission system of the present invention. The transmitting end of the space-time channel optimization MIMO wireless transmission system of the present invention comprises a multi-channel signal transmitting end, each of which comprises a modulation filtering and amplification module and a transmitting antenna, and also comprises a plurality of virtual channel vector modules, a feedback information receiving end and a space-time optimization module, each of which corresponds to at least one signal input end, each of which corresponds to only one virtual channel vector module, and the output end of each virtual channel vector module is connected to only one signal transmitting end in a one-to-one correspondence, the feedback information receiving end is connected to the space-time optimization module, the space-time optimization module is connected to each virtual channel vector module, and the space-time optimization module is connected to each signal input end, wherein the virtual channel vector module is used to perform a complex weighting operation on the baseband signal inputted by each signal input end connected thereto according to the set complex weighting value, and merge all the complex weighted baseband signals and transmit them to the corresponding signal transmitting end; the feedback information receiving end is used to receive the feedback information sent by the system receiving end, and transmit it to the space-time optimization module; the space-time optimization module is used to calculate the complex weighting values in each virtual channel vector module by using the space-time optimization algorithm according to the received feedback information, and set them.

The processing method of the transmitting end of the space-time channel optimized MIMO wireless transmission system of the present invention is applied to the transmitting end of the above-mentioned space-time channel optimized MIMO wireless transmission system. First, the signal input end receives the input baseband signal and transmits the baseband signal to its corresponding virtual channel vector module. Each virtual channel vector module performs a complex weighted operation on the baseband signal input by each signal input end connected to it according to the set complex weight value, and combines all the complex weighted baseband signals and transmits them to the corresponding signal transmitting end for sending. The feedback information receiving end receives the feedback information sent by the system receiving end in real time and transmits it to the space-time optimization module. The space-time optimization module uses the space-time optimization algorithm to calculate the complex weight values in each virtual channel vector module according to the received feedback information, and sets them.

Example

The system block diagram of the transmitting end of the space-time channel optimization MIMO wireless transmission system of the embodiment of the present invention is shown in FIG2, which includes a multi-channel signal transmitting end, each of which includes a modulation filtering and amplification module and a transmitting antenna, and also includes multiple virtual channel vector modules, a feedback information receiving end and a space-time optimization module, each virtual channel vector module corresponds to at least one signal input end, each signal input end corresponds to only one virtual channel vector module, and the output end of each virtual channel vector module is connected to only one signal transmitting end in a one-to-one correspondence, the feedback information receiving end is connected to the space-time optimization module, the space-time optimization module is connected to each virtual channel vector module, and the space-time optimization module is connected to each signal input end, wherein the virtual channel vector module is used to perform a complex weighting operation on the baseband signal input by each signal input end connected thereto according to the set complex weighting value, and merge all the complex weighted baseband signals and transmit them to the corresponding signal transmitting end; the feedback information receiving end is used to receive the feedback information sent by the system receiving end, and transmit it to the space-time optimization module; the space-time optimization module is used to calculate the complex weighting values in each virtual channel vector module using the space-time optimization algorithm according to the received feedback information, and set them.

In this example, the virtual channel vector module includes complex weighted modules corresponding to the number of signal input terminals and an adder. The input terminal of each complex weighted module is connected one-to-one with a signal input terminal, the output terminal of each complex weighted module is connected one-to-one with an input terminal of the adder, the output terminal of each adder is connected one-to-one with a signal transmitting terminal as the output terminal of the virtual channel vector module, and the space-time optimization module is connected to each complex weighted module.

The baseband signals input to each signal input terminal can be different, or some can be the same and some can be different. Of course, they can also be all the same, and the number of signal input terminals corresponding to each virtual channel vector module can also be different or the same. The feedback information includes channel identification and system status information, such as signal-to-noise ratio, bit error rate, error value and channel estimation value.

In this example, the system block diagram of the space-time channel optimized MIMO wireless transmission system composed of the space-time channel optimized MIMO wireless transmission system transmitter is shown in Figure 3, including its corresponding system receiving end, the system receiving end includes multiple receiving antennas, corresponding demodulation filtering and amplification modules, corresponding signal detection and processing modules, channel identification and system status information acquisition modules, and feedback information sending ends. The channel identification and system status information acquisition module and the feedback information sending end are parts of some existing receiving ends and will not be described in detail here.

When used, the processing method is as follows:

A. The signal input end receives an input baseband signal and transmits the baseband signal to its corresponding virtual channel vector module;

B. Each virtual channel vector module performs a complex weighting operation on the baseband signal input from each signal input terminal connected thereto according to the set complex weighting value, and combines all the complex weighted baseband signals and transmits them to the corresponding signal transmitting terminal for transmission;

C. The feedback information receiving end receives the feedback information sent by the system receiving end in real time and transmits it to the space-time optimization module. The space-time optimization module uses the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module based on the received feedback information, sets it, and returns to step B.

In this step, the specific method and principle of the space-time optimization module to calculate each complex weight value in each virtual channel vector module using the space-time optimization algorithm according to the received feedback information is as follows:

即向量x_m(t)包括N_m个基带输入信号x_mn(t)(n＝0,1,…,N_m)，x_mn(t)为复数信号。Assume that the transmitter of the space-time channel optimized MIMO wireless transmission system (hereinafter referred to as the transmitter) has _NT transmitting antennas, and its corresponding receiver has _LR receiving antennas. Generally, _LR ≥ _NT , then the transmitter has _NT input signal vectors, each of which includes multiple baseband input signals. Assume that the mth input signal vector is

That is, the vector x _m (t) includes N _m baseband input signals x _mn (t) (n=0, 1, ..., N _m ), and x _mn (t) is a complex signal.

每个基带输入信号x_mn(t)经过一个对应的虚拟信道

用w_m表示第m个虚拟信道向量,则

向量w_m包括N_m个虚拟信道w_mn。在发射端，N_T个虚拟信道向量w_m与N_T个输入信号向量x_m(t)一一对应，可以用一个系统虚拟信道向量来表示，即

All N _T input signal vectors at the system transmitter constitute the system transmission signal vector

Each baseband input signal _xmn (t) passes through a corresponding virtual channel

Let _wm represent the mth virtual channel vector, then

The vector w _m includes N _m virtual channels w _mn . At the transmitting end, the N _T virtual channel vectors w _m correspond one-to-one to the N _T input signal vectors x _m (t), and can be represented by a system virtual channel vector, that is,

At the receiving end, the lth receiving antenna receives signals from all N _T transmitting antennas. Let h _lm represent the spatial wireless channel between the lth receiving antenna and the mth transmitting antenna. The signal x _mn (t) passes through two transmission paths from the mth transmitting antenna to the lth receiving antenna, namely the virtual channel w _mn and the spatial wireless channel h _lm . The two channels are cascaded to form the overall transmission channel w _mn ^* h _lm , which is called the coordinated spatial division channel. Therefore, the signal sent by the mth transmitting antenna is

The signal received by the lth receiving antenna is

Where q _l (t) is the Gaussian white noise of the lth receiving antenna. The spatial wireless channel matrix of the system is expressed as

其中

设

为接收端的接收信号向量，则H can be simplified as

in

set up

is the received signal vector at the receiving end, then

是接收端的噪声向量。In the formula,

is the noise vector at the receiver.

In this system, the system cooperative space division channel matrix is expressed as

Where g _m = h _m w _m ^H is an _LR × N _m matrix, which represents the coordinated spatial channel corresponding to the mth transmitting antenna. Therefore, the received signal vector can be further expressed as

By adjusting and optimizing the virtual channel w _mn , the coordinated space division channel w _mn ^* h _lm (m＝1,2,…, _NT ; n＝1,2,…, _Nm ; l＝1,2,…, _LR ) can be adjusted and optimized to make the overall transmission channel of the system reasonably arranged, which is most conducive to the signal detection at the receiving end and the optimization of the system transmission performance.

At the receiving end of the system, the signal-to-noise ratio is

Here, _PR = E[(gx(t)) ^H (gx(t))] is the received signal power at the receiving end, and ^σ2 = E[q(t) ^H q(t)] is the noise power at the receiving end. Expanding _PR = E[(gx(t)) ^H (gx(t))] yields

信号传输矩阵，where λ _ij =E[ _hi ^H h _j ] is a scalar, R _ij =E[ _xi (t)x _j (t) ^H ] is a _Ni × _Nj input correlation matrix (i = 1, 2, …, _NT ; j = 1, 2, …, _NT ), and R is a

Signal transmission matrix,

At the receiving end of the system, we hope to maximize the signal-to-noise ratio η _R , but since the noise power σ ² at the receiving end is regarded as a constant, maximizing the received signal power _PR is equivalent to maximizing the signal-to-noise ratio η _R . Therefore, the optimization criterion for this example is as follows:

Here, G is a constant. Under the condition that the transmitted signal power is constant, by adjusting the virtual channel, the above optimization mechanism maximizes the signal power transmitted to the receiving end. Its optimization solution is:

是对应于矩阵R的最大特征值的特征向量，且

w_opt即为欲得到的复加权向量w的最优值。In the formula,

is the eigenvector corresponding to the largest eigenvalue of the matrix R, and

w _opt is the optimal value of the desired complex weight vector w.

则接收误码率(BER)为For QPSK signals, if the received signal-to-noise ratio is known,

The received bit error rate (BER) is

因此，对于QPSK信号，采用优化解

时接收误码率(BER)为Where Q(.) is a function defined as

Therefore, for QPSK signals, the optimal solution is

The received bit error rate (BER) is

Where _PRmax is the maximum value of the received signal power.

提供了一个优化闭合解，但在有的情况下其效果不一定很理想。另一个方案是采用粒子群算法搜索全局最优解。Although

It provides an optimal closed solution, but in some cases its effect may not be ideal. Another solution is to use particle swarm algorithm to search for the global optimal solution.

Here, let the particle group size be _SE , that is, the number of particles is _SE , and each potential system virtual channel vector at the transmitter is taken as the position of a particle. At the kth iteration, the sth particle position, that is, the sth system virtual channel vector is expressed as

是第s个粒子位置中的第m个虚拟信道向量。在第k次迭代时刻，第s个粒子的移动速度表示为

式中，

是虚拟信道向量

的相应移动速度。令

表示在第k次迭代时刻，第s个粒子迄今为止搜索到的个体最优位置，式中，

是第s个粒子个体最优位置中的第m个虚拟信道向量。令

表示在第k次迭代时刻，整个粒子群迄今为止搜索到的全局最优位置，式中，

是全局最优位置中的第m个虚拟信道向量。在这个方法中，采用参考信号将有助于搜索。参考信号在每个数据帧中占用一个时隙，用

表示参考信号向量，式中，x_Rm(t)是对应于输入信号向量x_m(t)的第m个参考信号向量。在优化过程中，首先要检测或估计参考信号，然后将检测或估计结果与实际参考信号进行对比，产生误差，将误差作为反馈信息发送到发射端。在检测参考信号向量x_R(t)时，信号向量估计值会受虚拟信道向量w^(s)(k)的影响,因此，将参考信号向量在w^(s)(k)条件下的估计值表示为

其相应的误差表示为

类似地，在w^(s)(k)条件下的误码率BER表示为

In the formula,

is the mth virtual channel vector in the position of the sth particle. At the kth iteration, the moving speed of the sth particle is expressed as

In the formula,

is the virtual channel vector

The corresponding moving speed.

represents the individual optimal position searched by the sth particle so far at the kth iteration, where

is the mth virtual channel vector in the optimal position of the sth particle.

It represents the global optimal position searched by the entire particle swarm so far at the kth iteration, where:

is the mth virtual channel vector in the global optimal position. In this method, the use of a reference signal will help the search. The reference signal occupies one time slot in each data frame.

represents the reference signal vector, where x _Rm (t) is the mth reference signal vector corresponding to the input signal vector x _m (t). In the optimization process, the reference signal must first be detected or estimated, and then the detection or estimation result is compared with the actual reference signal to generate an error, which is sent to the transmitter as feedback information. When detecting the reference signal vector x _R (t), the signal vector estimation value will be affected by the virtual channel vector w ^(s) (k). Therefore, the estimated value of the reference signal vector under the condition of w ^(s) (k) is expressed as

The corresponding error is expressed as

Similarly, the bit error rate BER under the condition of w ^(s) (k) is expressed as

Therefore, the specific steps of using the particle swarm algorithm to search for the global optimal system virtual channel vector are as follows:

Step 1. Set constants at the system transmitter according to the actual communication environment: c ₁ , c ₂ , r ₁ , r ₂ , ε ₁ , ε ₂ , A, B, _GT , v _min , where c ₁ and c ₂ are learning factors, which enable particles to have the ability to self-summarize and learn from excellent individuals in the group, so as to approach their own historical optimal points and the historical optimal points within the group; r ₁ and r ₂ are random numbers between [0, 1]; ε ₁ and ε ₂ are smaller constants set according to the actual communication environment; A is the initial inertia weight; B is the update coefficient of the inertia weight; _GT is the virtual channel gain constraint constant; v _min and v _max are the minimum and maximum speeds of particle movement, respectively, which limit the speed range of particles.

和

采用得到的每一个w^(s)(0)，分时隙发送一个参考信号序列x_R(t)，一共S_E个不同时隙，每个时隙采用一个不同的位置向量w^(s)(0)(s＝1,2,…,S_E)。Step 2: Set k = 0 at the system transmitter, and randomly initialize the position and moving speed of each particle to obtain

and

Using each obtained w ^(s) (0), a reference signal sequence x _R (t) is sent in time slots, for a total of S _E different time slots, and each time slot uses a different position vector w ^(s) (0) (s = 1, 2, ..., S _E ).

然后，用不同的位置向量w^(s)(0)计算误差：Step 3: Detect the reference signal at the receiving end of the system and obtain the estimated values of S _E reference signal vectors, that is,

Then, the error is calculated using different position vectors w ^(s) (0):

然后，作为反馈信号，发送每一个

或

到系统发射端(s＝1,2,…,S_E)。Or calculate

Then, as a feedback signal, send each

or

To the system transmitting end (s＝1,2,…, _SE ).

Step 4: Set the optimal individual position at the transmitting end of the system: p ^(s) (0)=w ^(s) (0)(s=1,2,…,S _E ), find the minimum feedback signal value among all feedback signals, and assume that the particle position corresponding to the minimum feedback signal value is w ^(g) (0), then the optimal global position is b(0)=w ^(g) (0).

Step 5: Update the inertia weight at the system transmitter: α = B(k+1) + A. For each particle, calculate its velocity and position vector as follows:

v ^(s) (k+1)=αv ^(s) (k)+c ₁ r ₁ [p ^(s) (k)-w ^(s) (k)]+c ₂ r ₂ [b(k)- w ^(s) (k)]

w ^(s) (k+1)＝w ^(s) (k)+v ^(s) (k+1)

Where s = 1, 2, …, S _E . The value range of each element in the vector v ^(s) (k+1) is [v _min , v _max ]. In addition, the transmission power is limited:

Then, reference signals are sent to the system receiving end in different time slots, for a total of S _E time slots, and each time slot uses a different position vector w ^(s) (k+1) (s=1, 2, …, S _E ).

然后，用不同的位置向量w^(s)(k+1)计算误差：Step 6: Detect the reference signal at the receiving end of the system and obtain the vector estimation value of S _E reference signals, that is,

Then, the error is calculated using different position vectors w ^(s) (k+1):

或者计算

然后，作为反馈信号，发送每一个

或

到系统发射端(s＝1,2,…,S_E)。

Or calculate

Then, as a feedback signal, send each

or

To the system transmitting end (s＝1,2,…, _SE ).

或者

则p^(s)(k+1)＝w^(s)(k+1)；如果

或者

则P^(s)(k+1)＝P^(s)(k)。Step 7: Update the best individual position based on the feedback signal at the system transmitter.

or

Then p ^(s) (k+1) = w ^(s) (k+1); if

or

Then P ^(s) (k+1) = P ^(s) (k).

或者

则最佳全局位置为b(k+1)＝w^(g)(k+1)；如果

或者

则最佳全局位置为b(k+1)＝b(k)。Step 8: Update the best global position based on the feedback signal at the transmitting end of the system. Find the minimum feedback signal value among all feedback signals. Let the particle position corresponding to the minimum feedback signal value be w ^(g) (k+1). If

or

Then the best global position is b(k+1)=w ^(g) (k+1); if

or

Then the optimal global position is b(k+1)=b(k).

Step 9. If e _R,b(k+1) <ε ₁ or BER _b(k+1) <ε ₂ , the operation stops and data transmission begins; if e _R,b(k+1) ≥ε ₁ or BER _b(k+1) ≥ε ₂ , k→k+1, return to step 5.

直接计算BER。In the above steps 3 and 6, when using QPSK signal, if the received signal-to-noise ratio has been obtained, it can be used

Calculate BER directly.

Claims (8)

1. A space-time channel optimized MIMO wireless transmission system transmitter, comprising a multi-channel signal transmitter, each of which comprises a modulation filter amplifier module and a transmitting antenna, characterized in that it also comprises a plurality of virtual channel vector modules, a feedback information receiving end and a space-time optimization module, each virtual channel vector module corresponds to at least one signal input end, each signal input end corresponds to only one virtual channel vector module, the output end of each virtual channel vector module is connected to only one signal transmitter in a one-to-one correspondence, the feedback information receiving end is connected to the space-time optimization module, the space-time optimization module is connected to each virtual channel vector module, and the space-time optimization module is connected to each signal input end; The virtual channel vector module is used to perform a complex weighting operation on the baseband signal input from each signal input terminal connected thereto according to the set complex weighting value, and combine all the complex weighted baseband signals and transmit them to the corresponding signal transmitting terminal; The feedback information receiving end is used to receive feedback information sent by the system receiving end and transmit it to the space-time optimization module; The space-time optimization module is used to calculate each complex weighted value in each virtual channel vector module using a space-time optimization algorithm according to the received feedback information, and set it; The space-time optimization module uses the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module according to the received feedback information, including: The space-time optimization module uses the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module according to the received feedback information. The calculation formula is:

Wherein, w _opt is the optimal value of the complex weight vector w to be obtained, and the complex weight vector w is also called the system virtual channel vector, which is expressed as:

Here, the vector _wm represents the mth virtual channel vector, including _Nm virtual channels _wmn , which is expressed as:

w _mn represents the virtual channel corresponding to each baseband input signal x _mn (t), specifically:

_NT is the number of transmitting antennas, and also the number of input signal vectors. _Nm refers to the number of signal input terminals corresponding to the mth virtual channel vector module, n=1, 2, ..., _Nm . _xmn (t) refers to the baseband input signal input to the nth signal input terminal in the mth virtual channel vector module.

is the eigenvector corresponding to the largest eigenvalue of the matrix R, and

R is a

Signal transmission matrix refers to:

Where, R _ij =E[ _xi (t) _xj (t) ^H ] is a _Ni × _Nj input correlation matrix, i=1,2,…, _NT , j=1,2,…, _NT ; The vector x _m (t) refers to the input signal vector of the mth virtual channel vector module, which includes N _m baseband input signals x _mn (t) (n=0, 1, ..., N _m ), where x _mn (t) is a complex signal, expressed as:

All input signal vectors constitute the system transmission signal vector

λ _ij =E[ _hi ^H h _j ] is a scalar, and the spatial wireless channel matrix of the system is expressed as

H can be simplified as

in

h _lm represents the spatial wireless channel between the lth receiving antenna and the mth transmitting antenna, l = 1, 2, …, _LR , _LR is the number of receiving antennas; The method of using the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module is to use the particle swarm algorithm to search for the global optimal system virtual channel vector, where: Assume that the number of transmitting antennas is _NT , which is also the number of input signal vectors, the number of receiving antennas is _LR , and w is the system virtual channel vector, which can be expressed as:

Here, the vector _wm represents the mth virtual channel vector, including _Nm virtual channels _wmn , which is expressed as:

w _mn represents the virtual channel corresponding to each baseband input signal x _mn (t), specifically:

N _m refers to the number of signal input terminals corresponding to the mth virtual channel vector module, n = 1, 2, ..., N _m , x _mn (t) refers to the baseband input signal input to the nth signal input terminal in the mth virtual channel vector module, and the vector x _m (t) refers to the input signal vector of the mth virtual channel vector module, which includes N _m baseband input signals x _mn (t) (n = 0, 1, ..., N _m ), and x _mn (t) is a complex signal, which can be expressed as

Assume the number of particles is S _E , and take each system virtual channel vector of the transmitting end of the space-time channel optimization MIMO wireless transmission system as the position of a particle; At the kth iteration, the position of the sth particle, that is, the sth system virtual channel vector is expressed as

in,

is the mth virtual channel vector in the sth particle position; At the kth iteration, the moving speed of the sth particle is expressed as:

in,

is the virtual channel vector

The corresponding moving speed; make

represents the individual optimal position searched by the sth particle so far at the kth iteration, where

is the mth virtual channel vector in the optimal position of the sth particle individual; make

represents the global optimal position searched by the entire particle swarm so far at the kth iteration, where

is the mth virtual channel vector in the global optimal position; Let the reference signal occupy one time slot in each data frame,

represents the reference signal vector, where x _Rm (t) is the mth reference signal vector corresponding to the input signal vector x _m (t), and the estimated value of the reference signal vector under the action of w ^(s) (k) is expressed as

The corresponding error is expressed as

Similarly, the bit error rate BER under the action of w ^(s) (k) is expressed as

Therefore, the specific steps of using the particle swarm algorithm to search for the global optimal system virtual channel vector are as follows: Step 1. At the transmitting end of the space-time channel optimization MIMO wireless transmission system, set constants according to the actual communication environment: c ₁ , c ₂ , r ₁ , r ₂ , ε ₁ , ε ₂ , A, B, _GT , v _min , v _max , where c ₁ and c ₂ are learning factors, which enable particles to have the ability to self-summarize and learn from excellent individuals in the group, so as to approach their own historical optimal points and the historical optimal points within the group; r ₁ and r ₂ are random numbers between [0, 1]; ε ₁ and ε ₂ are smaller constants set according to the actual communication environment; A is the initial inertia weight; B is the update coefficient of the inertia weight; _GT is the virtual channel gain constraint constant; v _min and v _max are the minimum and maximum speeds of particle movement, respectively, which limit the speed range of particles; Step 2: At the transmitting end of the space-time channel optimization MIMO wireless transmission system, set k = 0, randomly initialize the position and moving speed of each particle, and obtain

and

Using each obtained w ^(s) (0), a reference signal sequence x _R (t) is sent in time slots, for a total of S _E different time slots, each time slot using a different position vector w ^(s) (0) (s = 1, 2, ..., S _E ); Step 3: Detect the reference signal at the receiving end of the system and obtain the vector estimation value of S _E reference signals, that is,

The error is then calculated using different position vectors w ^(s) (0):

Or calculate

As a feedback signal, send each

or

To the transmitter of the space-time channel optimized MIMO wireless transmission system; Step 4: Set the best individual position at the transmitting end of the space-time channel optimized MIMO wireless transmission system: p ^(s) (0) = w ^(s) (0) (s = 1, 2, ..., S _E ), find the minimum feedback signal value among all feedback signals, and assume that the particle position corresponding to the minimum feedback signal value is w ^(g) (0), then the best global position is b (0) = w ^(g) (0); Step 5: Update the inertia weight at the transmitter of the space-time channel optimized MIMO wireless transmission system: α = B(k+1) + A. For each particle, calculate its velocity and position vector as follows: v ^(s) (k+1)=αv ^(s) (k)+c ₁ r ₁ [p ^(s) (k)-w ^(s) (k)]+c ₂ r ₂ [b(k)- w ^(s) (k)] w ^(s) (k+1)＝w ^(s) (k)+v ^(s) (k+1) Where s = 1, 2, ..., _SE , the value of each element in the vector v ^(s) (k+1) is in the range [v _min , v _max ]. In addition, the transmit power is limited as follows:

Then, the reference signal is sent to the system receiving end in different time slots, a total of S _E time slots, and each time slot uses a different position vector w ^(s) (k+1) (s = 1, 2, ..., S _E ); Step 6: Detect the reference signal at the receiving end of the system and obtain the vector estimation value of S _E reference signals, that is,

Then, the error is calculated using different position vectors w ^(s) (k+1):

Or calculate

Then use it as a feedback signal to send each

or

To the transmitter of the space-time channel optimized MIMO wireless transmission system; Step 7: At the transmitting end of the space-time channel optimization MIMO wireless transmission system, the best individual position is updated according to the feedback signal. If

or

Then p ^(s) (k+1) = w ^(s) (k+1); otherwise, P ^(s) (k+1) = P ^(s) (k); Step 8: At the transmitting end of the space-time channel optimized MIMO wireless transmission system, the best global position is updated according to the feedback signal, and the minimum feedback signal value is found among all feedback signals. The particle position corresponding to the minimum feedback signal value is set to be w ^(g) (k+1). If

or

Then the best global position is b(k+1)=w ^(g) (k+1); otherwise, the best global position is b(k+1)=b(k),

is the corresponding error of the reference signal vector under the action of W ^(g) (k+1),

is the BER representation of the bit error rate under the condition of W ^(g) (k+1), e _R,b(k) is the corresponding error of the reference signal vector under the condition of b(k), BER _b(k) is the BER representation of the bit error rate under the condition of b(k); Step 9. If e _R,b(k+1) ＜ε ₁ or BER _b(k+1) ＜ε ₂ , the operation stops and starts to send data formally; otherwise, k→k+1, return to step 5, e _R,b(k+1) is the corresponding error of the reference signal vector under the condition of b( _{k+1), and BER b(k+1)} is the BER representation of the bit error rate under the condition of b(k+1). 2. The space-time channel optimized MIMO wireless transmission system transmitter as described in claim 1 is characterized in that the virtual channel vector module includes complex weighted modules and an adder corresponding to the number of signal input terminals, the input terminals of each complex weighted module are respectively connected one-to-one with a signal input terminal, the output terminals of each complex weighted module are respectively connected one-to-one with an input terminal of the adder, the output terminal of each adder is connected one-to-one with a signal transmitting terminal as the output terminal of the virtual channel vector module, and the space-time optimization module is respectively connected to each complex weighted module. 3. The space-time channel optimized MIMO wireless transmission system transmitter as described in claim 1 is characterized in that the baseband signals input by each of the signal input terminals are different. 4. The space-time channel optimized MIMO wireless transmission system transmitter as described in claim 1 is characterized in that the number of signal input terminals corresponding to each virtual channel vector module is different. 5. The space-time channel optimized MIMO wireless transmission system transmitter as claimed in claim 1, characterized in that the feedback information includes channel identification and system status information. 6. The space-time channel optimized MIMO wireless transmission system transmitter as described in claim 5 is characterized in that the channel identification and system status information include signal-to-noise ratio, bit error rate, error value and channel estimation value. 7. A processing method for a space-time channel optimized MIMO wireless transmission system transmitter, applied to the space-time channel optimized MIMO wireless transmission system transmitter as claimed in claim 1 or 2 or 3 or 4 or 5 or 6, characterized in that it comprises the following steps: A. The signal input end receives an input baseband signal and transmits the baseband signal to its corresponding virtual channel vector module; B. Each virtual channel vector module performs a complex weighting operation on the baseband signal input from each signal input terminal connected thereto according to the set complex weighting value, and combines all the complex weighted baseband signals and transmits them to the corresponding signal transmitting terminal for transmission; C. The feedback information receiving end receives the feedback information sent by the system receiving end in real time and transmits it to the space-time optimization module. The space-time optimization module calculates each complex weight value in each virtual channel vector module using the space-time optimization algorithm according to the received feedback information, sets it, and returns to step B. In step C, the space-time optimization module uses the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module according to the received feedback information: The space-time optimization module uses the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module according to the received feedback information. The calculation formula is:

Wherein, w _opt is the optimal value of the complex weight vector w to be obtained, and the complex weight vector w is also called the system virtual channel vector, which is expressed as:

Here, the vector _wm represents the mth virtual channel vector, including _Nm virtual channels _wmn , which is expressed as:

w _mn represents the virtual channel corresponding to each baseband input signal x _mn (t), specifically:

_NT is the number of transmitting antennas, and also the number of input signal vectors. _Nm refers to the number of signal input terminals corresponding to the mth virtual channel vector module, n=1, 2, ..., _Nm . _xmn (t) refers to the baseband input signal input to the nth signal input terminal in the mth virtual channel vector module.

is the eigenvector corresponding to the largest eigenvalue of the matrix R, and

R is a

Signal transmission matrix refers to:

Where, R _ij =E[ _xi (t) _xj (t) ^H ] is a _Ni × _Nj input correlation matrix, i=1,2,…, _NT , j=1,2,…, _NT ; The vector x _m (t) refers to the input signal vector of the mth virtual channel vector module, which includes N _m baseband input signals x _mn (t) (n=0, 1, ..., N _m ), where x _mn (t) is a complex signal, expressed as:

All input signal vectors constitute the system transmission signal vector

λ _ij =E[ _hi ^H h _j ] is a scalar, and the spatial wireless channel matrix of the system is expressed as

H can be simplified as

in

h _lm represents the spatial wireless channel between the lth receiving antenna and the mth transmitting antenna, l = 1, 2, …, _LR , _LR is the number of receiving antennas; The method of using the space-time optimization algorithm to calculate the complex weighted values in each virtual channel vector module is to use the particle swarm algorithm to search for the global optimal system virtual channel vector, where: Assume that the number of transmitting antennas is _NT , which is also the number of input signal vectors, the number of receiving antennas is _LR , and w is the system virtual channel vector, which can be expressed as:

Here, the vector _wm represents the mth virtual channel vector, including _Nm virtual channels _wmn , which is expressed as:

w _mn represents the virtual channel corresponding to each baseband input signal x _mn (t), specifically:

N _m refers to the number of signal input terminals corresponding to the mth virtual channel vector module, n = 1, 2, ..., N _m , x _mn (t) refers to the baseband input signal input to the nth signal input terminal in the mth virtual channel vector module, and the vector x _m (t) refers to the input signal vector of the mth virtual channel vector module, which includes N _m baseband input signals x _mn (t) (n = 0, 1, ..., N _m ), and x _mn (t) is a complex signal, which can be expressed as

Assume the number of particles is S _E , and take each system virtual channel vector of the transmitting end of the space-time channel optimization MIMO wireless transmission system as the position of a particle; At the kth iteration, the position of the sth particle, that is, the sth system virtual channel vector is expressed as

in,

is the mth virtual channel vector in the sth particle position; At the kth iteration, the moving speed of the sth particle is expressed as:

in,

is the virtual channel vector

The corresponding moving speed; make

represents the individual optimal position searched by the sth particle so far at the kth iteration, where

is the mth virtual channel vector in the optimal position of the sth particle individual; make

represents the global optimal position searched by the entire particle swarm so far at the kth iteration, where

is the mth virtual channel vector in the global optimal position; Let the reference signal occupy one time slot in each data frame,

represents the reference signal vector, where x _Rm (t) is the mth reference signal vector corresponding to the input signal vector x _m (t), and the estimated value of the reference signal vector under the action of w ^(s) (k) is expressed as

The corresponding error is expressed as

Similarly, the bit error rate BER under the action of w ^(s) (k) is expressed as

Therefore, the specific steps of using the particle swarm algorithm to search for the global optimal system virtual channel vector are as follows: Step 1. At the transmitting end of the space-time channel optimization MIMO wireless transmission system, set constants according to the actual communication environment: c ₁ , c ₂ , r ₁ , r ₂ , ε ₁ , ε ₂ , A, B, _GT , v _min , v _max , where c ₁ and c ₂ are learning factors, which enable particles to have the ability to self-summarize and learn from excellent individuals in the group, so as to approach their own historical optimal points and the historical optimal points within the group; r ₁ and r ₂ are random numbers between [0, 1]; ε ₁ and ε ₂ are smaller constants set according to the actual communication environment; A is the initial inertia weight; B is the update coefficient of the inertia weight; _GT is the virtual channel gain constraint constant; v _min and v _max are the minimum and maximum speeds of particle movement, respectively, which limit the speed range of particles; Step 2: At the transmitting end of the space-time channel optimization MIMO wireless transmission system, set k = 0, randomly initialize the position and moving speed of each particle, and obtain

and

Using each obtained w ^(s) (0), a reference signal sequence x _R (t) is sent in time slots, for a total of S _E different time slots, each time slot using a different position vector w ^(s) (0) (s = 1, 2, ..., S _E ); Step 3: Detect the reference signal at the receiving end of the system and obtain the vector estimation value of S _E reference signals, that is,

The error is then calculated using different position vectors w ^(s) (0):

Or calculate

As a feedback signal, send each

or

To the transmitter of the space-time channel optimized MIMO wireless transmission system; Step 4: Set the best individual position at the transmitting end of the space-time channel optimized MIMO wireless transmission system: p ^(s) (0) = w ^(s) (0) (s = 1, 2, ..., S _E ), find the minimum feedback signal value among all feedback signals, and assume that the particle position corresponding to the minimum feedback signal value is w ^(g) (0), then the best global position is b (0) = w ^(g) (0); Step 5: Update the inertia weight at the transmitter of the space-time channel optimized MIMO wireless transmission system: α = B(k+1) + A. For each particle, calculate its velocity and position vector as follows: v ^(s) (k+1)=αv ^(s) (k)+c ₁ r ₁ [p ^(s) (k)-w ^(s) (k)]+c ₂ r ₂ [b(k)- w ^(s) (k)] w ^(s) (k+1)＝w ^(s) (k)+v ^(s) (k+1) Where s = 1, 2, ..., _SE , the value of each element in the vector v ^(s) (k+1) is in the range [v _min , v _max ]. In addition, the transmit power is limited as follows:

Then, the reference signal is sent to the system receiving end in different time slots, a total of S _E time slots, and each time slot uses a different position vector w ^(s) (k+1) (s = 1, 2, ..., S _E ); Step 6: Detect the reference signal at the receiving end of the system and obtain the vector estimation value of S _E reference signals, that is,

Then, the error is calculated using different position vectors w ^(s) (k+1):

Or calculate

Then use it as a feedback signal to send each

or

To the transmitter of the space-time channel optimized MIMO wireless transmission system; Step 7: At the transmitting end of the space-time channel optimization MIMO wireless transmission system, the best individual position is updated according to the feedback signal. If

or

Then p ^(s) (k+1) = w ^(s) (k+1); otherwise, P ^(s) (k+1) = P ^(s) (k); Step 8: At the transmitting end of the space-time channel optimized MIMO wireless transmission system, the best global position is updated according to the feedback signal, and the minimum feedback signal value is found among all feedback signals. The particle position corresponding to the minimum feedback signal value is set to be w ^(g) (k+1). If

or

Then the best global position is b(k+1)=w ^(g) (k+1); otherwise, the best global position is b(k+1)=b(k),

is the corresponding error of the reference signal vector under the action of W ^(g) (k+1),

is the BER representation of the bit error rate under the condition of W ^(g) (k+1), e _R,b(k) is the corresponding error of the reference signal vector under the condition of b(k), BER _b(k) is the BER representation of the bit error rate under the condition of b(k); Step 9. If e _R,b(k+1) ＜ε ₁ or BER _b(k+1) ＜ε ₂ , the operation stops and starts to send data formally; otherwise, k→k+1, return to step 5, e _R,b(k+1) is the corresponding error of the reference signal vector under the condition of b( _{k+1), and BER b(k+1)} is the BER representation of the bit error rate under the condition of b(k+1). 8. The processing method for the transmitting end of the space-time channel optimization MIMO wireless transmission system according to claim 7, characterized in that in step B, the signal output by the mth virtual channel vector module is

Wherein, vector _wm represents the mth virtual channel vector, including _Nm virtual channels _wmn , which is expressed as:

w _mn represents the virtual channel corresponding to each baseband input signal x _mn (t), specifically:

x _mn (t) is a complex signal, _NT is the number of transmitting antennas, which is also the number of input signal vectors, _Nm refers to the number of signal input terminals corresponding to the mth virtual channel vector module, n = 1, 2, ..., _Nm , _xmn (t) refers to the baseband input signal input to the nth signal input terminal in the mth virtual channel vector module, and the vector _xm (t) refers to the input signal vector of the mth virtual channel vector module, which is expressed as:

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