CN101321009B - Self-adapting MIMO system and its signal processing method - Google Patents

Abstract

The invention discloses an adaptive MIMO system and a signal processing method thereof, including a transmitting end, a receiving end, and a feedback path from the receiving end to the transmitting end, the transmitting end includes an antenna, the receiving end includes a signal detection module, and the receiving end also includes a unified The channel decomposition module, the unified channel decomposition module decomposes the channel matrix, decomposes the precoding matrix and feeds it back to the transmitter through the feedback path, and decomposes the weighting coefficient matrix and sends it to the signal detection module. The invention also discloses an adaptive MIMO system, which groups antennas at the transmitting end and includes space-time coding modules corresponding to the number of antenna groups, and the antenna groups are respectively connected to the space-time coding modules. The invention also discloses an adaptive MIMO system which combines the above two systems. The MIMO system and its signal processing method of the present invention can adapt to the performance of high-correlation channels, and at the same time can adapt to different numbers of transmitting and receiving antennas, and improve the capacity and performance of the system.

Description

An Adaptive MIMO System and Its Signal Processing Method

technical field

The present invention relates to the technical field of wireless communication, in particular to an adaptive MIMO (Multiple-Input Multiple-Out-put, multiple-input multiple-output) antenna system and a signal processing method thereof.

Background technique

MIMO technology in mobile communication refers to the technology of using multiple transmitting antennas and multiple receiving antennas for wireless transmission. Any wireless communication system, as long as its transmitting end and receiving end use multiple antennas or antenna arrays, constitutes A wireless MIMO system. In a MIMO system, signals are transmitted and received through multiple antennas at the transmitter and receiver, thereby improving the quality of service (bit error rate or data rate) for each user. . The wireless MIMO system uses space-time processing technology for signal processing. In a multipath environment, a wireless MIMO system can greatly improve spectrum utilization and increase system data transmission rate. According to the number of antennas at both ends of the transceiver, compared with the ordinary SISO (Single-Input Single-Output, single-input single-output) antenna system, the use of MIMO channels can double the wireless channel capacity without increasing the bandwidth and antenna transmission power. Under this condition, the spectrum utilization rate can be doubled. At the same time, it can also improve the reliability of the channel and reduce the bit error rate.

In the general multi-antenna technology, it is typical that the base station is equipped with multiple antennas, because it is generally believed that the base station can bear the extra cost and space more than the mobile terminal.

Over the past few years, wireless services have become increasingly important, while the need for higher network capacity and higher performance continues to grow. Several options such as higher bandwidths, optimized modulation schemes and even code multiplexing systems have limited potential to actually improve spectral efficiency. MIMO systems utilize spatial multiplexing techniques to improve the efficiency of the bandwidth used by employing antenna arrays.

Sending multiple sets of data streams through more than one antenna is called spatial multiplexing, of which there are two types:

The first type is VBLAST (Vertical Bell Laboratories Layered Space-Time), which sends a spatially unencoded data stream without having to consider equalizing the signal at the receiver.

The second type is realized by space-time coding (ie, space-time coding). Compared with VBLAST, space-time coding provides an orthogonal coding method, so it is an independent data stream. The VBLAST method cannot separate data streams, so there will be interference of multiple data streams, which will make the transmission unstable, and forward error coding does not always solve this problem. The detection of space-time coded signals is based on a simple linear process with reasonable results. The advantage of spatial multiplexing is that the increase in capacity is linearly related to the number of transmit antennas.

The traditional research focus on MIMO communication has always been that only the receiver knows the channel information (CSIR). However, if the wireless propagation environment is a slow fading channel (channel parameters change slowly over time), such as indoor environments and local wireless networks, we can pass The uplink feeds back channel information to the sending end, and the sending end uses the fed back channel information to preprocess the data to be sent to improve system performance. Based on this assumption, feedback-precoding scheme design has gradually become the focus of research. Existing feedback-precoding schemes are based on SVD (Singular Value Decomposition). For the sake of simplicity, take the MIMO system of 2 transmissions and 2 receptions as an example, the system of this solution is shown in Figure 1.

Referring to FIG. 1 , the SVD system includes a transmitting end, a receiving end, and a feedback path 113 . The transmitting end includes: an information source module 101, a modulation module 102, a precoding module 103, a serial-to-parallel conversion module 104, a VBLAST transmitting module 105, and two transmitting antennas 106; the receiving end includes: two receiving antennas 107, and a preprocessing module 108 , a channel estimation module 109 , a signal detection module 110 , an SVD decomposition module 111 , and a demodulation module 112 .

The transmitter first modulates the 01 sequence of the information source 101 using the modulation module 102. After modulation, the modulated signal is precoded using the precoding matrix V fed back from the receiving terminal through the feedback path 113. The precoding is completed in the precoding module 103. Finally, the serial-to-parallel conversion is performed by the serial-to-parallel conversion module 104 and transmitted from the transmitting antenna 106 in the form of VBLAST in the VBLAST module. Here, the VBLAST transmission is one of the parallel data transmitted by each antenna of the transmitting end at the same time.

为加性高斯白噪声。在接收端的信号检测模块110中我们可以使用“迫零算法”等检测算法。检测完后在解调模块112中对信号进行解调得到01信息序列。The receiving end first estimates the channel through the channel estimation module 109 to obtain the current channel parameters, and inputs it into the SVD decomposition module 111 to perform SVD decomposition on the channel matrix. SVD decomposition produces 3 matrices: U, S, V (H=U×S×V ^* ), V is fed back to the precoding module 103 at the transmitting end as the precoding matrix, U is used as the preprocessing matrix at the receiving end, and sent to the preprocessing module 108. S is sent to the signal detection module 110 as a virtual channel matrix in signal detection. Among them, S is a diagonal matrix whose diagonal elements are the singular values of the channel matrix λ ₁ , λ ₂ ,..., λ _K , so the SVD decomposition will decompose the MIMO channel into multiple parallel virtual sub-channels, refer to the figure 2 shows the virtual channel model of the SVD scheme. Among them, χ ₁ , χ ₂ , ..., χ _K are the transmitting signals of each virtual sub-channel, y ₁ , y ₂ , ..., y _K are the receiving signals of each virtual sub-channel,

is additive white Gaussian noise. In the signal detection module 110 at the receiving end, we can use detection algorithms such as "zero-forcing algorithm". After the detection, the signal is demodulated in the demodulation module 112 to obtain the 01 information sequence.

The SVD scheme is simple to implement, but has the following disadvantages:

1. Cannot adapt to channels with different correlations

The actual MIMO channel is very complicated, and the parameters of the channel are affected by many factors. At the receiving end, if the envelopes of the signals are "similar" when receiving the same signal between two antennas, we say that the correlation coefficient of the pair of antennas is large. The signal with a large correlation coefficient is actually that the signal reaches the receiving end without sufficient scattering process, and the gain of the MIMO system is largely obtained by the scattering of the signal, so the performance of the MIMO system in a channel with a large correlation coefficient tends to get worse.

The traditional SVD scheme is very sensitive to the correlation of the channel, and the performance of the SVD scheme will become poor or even unusable in a channel with a high correlation.

2. There are restrictions on the number of antennas

The SVD scheme combined with the traditionally used VBLAST scheme requires that the number of transmitting antennas (Nt) cannot be greater than the number of receiving antennas (Nr). In practical applications, the number of antennas at the base station is often greater than the number of antennas at the mobile terminal (such as a mobile phone), so the limitation of the number of antennas in the SVD scheme affects the practical application of MIMO to a certain extent.

In order to solve the problem that the SVD scheme cannot be applied when the number of receiving antennas is smaller than the number of transmitting antennas, that is, Nr<Nt, when performing signal detection at the receiving end of the VBLAST system, the existing technology is to perform space-time coding on all transmitting antennas, using 4 Take transmit 2 receive MIMO as an example, the system is shown in Figure 3

Referring to Fig. 3, it is a 4-transmit and 2-receive VBLAST system using space-time coding. The system includes a transmitting end and a receiving end, and the transmitting end includes: an information source module 301, a modulation module 302, a precoding module 303, a serial-to-parallel conversion module 304, a space-time encoding module 314, a VBLAST module 305, and 4 transmitting antennas 306; The end includes: two receiving antennas 307, a linear combination module 308, a channel estimation module 309, a space-time decoding module 310, a decision module 311, and a demodulation module 312. The system utilizes the space-time encoding module 314 at the transmitting end to perform space-time encoding on the signal, and correspondingly performs space-time decoding on the signal through the space-time decoding module 310 at the receiving end 31 .

The original purpose of space-time coding is to improve the performance of the receiving end by repeatedly transmitting different forms of the same data (such as transmitting its negative value, conjugate value, or different linear combinations of multiple signals) at the sending end. It has the effect of virtually increasing the number of antennas at the receiving end.

For example, in a MIMO system with 4 transmissions and 2 receptions, if the space-time code is not used, the receiving end will be a 4-element equation system with only 2 equations (noise is not regarded as an unknown), which cannot be solved. After using the space-time code, the sending end repeatedly transmits different forms of the same data 4 times, and the number of equations in the receiving end's equation system becomes 8, which can be solved.

However, the above-mentioned method of using space-time coding for all transmitting antennas also has the following problems:

First, similar to traditional SVD schemes, this technical scheme performs poorly under highly correlated channel conditions.

Second, the scheme has a lower capacity.

For example, the space-time coding of 4 transmitting antennas, 4 transmitting antennas transmit 4 different data in 4 time slots, and the code rate (the average number of different data transmitted in each time slot) is 1, which is only equivalent to the SISO system The sending rate of the system is greatly reduced.

It can be seen that the performance of signal processing using SVD decomposition is very poor in channels with high correlation, or even unusable, and it cannot adapt to different numbers of antennas; while using space-time coding can adapt to different numbers of antennas, but also in The performance is poor under high correlation channel conditions and the system capacity is low.

Contents of the invention

The technical problem to be solved by the present invention is to provide an adaptive MIMO system to optimize the system so that it can adapt to the performance of high-correlation channels.

In order to solve the above technical problems, the present invention provides an adaptive MIMO system, including a transmitting end, a receiving end, and a feedback path from the receiving end to the transmitting end, the transmitting end includes an antenna, and the receiving end includes Signal detection module, the receiving end also includes a unified channel decomposition module, the unified channel decomposition module decomposes the channel matrix, decomposes the precoding matrix and feeds it back to the transmitting end through the feedback path, decomposes the weighting coefficient matrix and sends it to the signal detection module.

Further, the antennas are divided into multiple groups; the system further includes a space-time coding module, which is respectively connected to the grouped antennas to perform space-time coding on the signal groups.

Further, the two antennas are grouped into one group.

Further, the signal detection module is a SIC-MMSE detection module.

Another technical problem to be solved by the present invention is to provide an adaptive MIMO system that can adapt to different numbers of transmitting and receiving antennas.

In order to solve the above technical problems, the present invention provides an adaptive MIMO system, which includes a transmitting end and a receiving end. A time coding module, the antenna groups are respectively connected to the space time coding module.

Further, the receiving end includes a signal detection module and a matrix decomposition module. The matrix decomposition module decomposes the channel matrix, decomposes the precoding matrix and feeds it back to the transmitting end, and decomposes the weighting coefficient matrix and sends it to the signal detection module.

Further, the matrix decomposition module is a unified channel decomposition module.

Another technical problem to be solved by the present invention is to provide an adaptive MIMO system, which can adapt to the performance of high-correlation channels, adapt to different numbers of transmitting and receiving antennas, and improve the capacity and performance of the system.

In order to solve the above technical problems, the present invention provides an adaptive MIMO system, including a transmitting end, a receiving end, and a feedback path from the receiving end to the transmitting end, the transmitting end includes an antenna, and the receiving end includes A signal detection module and a matrix decomposition module, the antenna grouping also includes a space-time coding module corresponding to the number of antenna groups, and the antenna groups are respectively connected to the space-time coding module; the matrix decomposition module decomposes the channel matrix, The decomposed precoding matrix is fed back to the transmitting end, and the decomposed weight coefficient matrix is sent to the signal detection module.

Further, the matrix decomposition module is a unified channel decomposition module.

Another technical problem to be solved by the present invention is to provide a receiving end that can adapt to the performance of high-correlation channels and improve the capacity and performance of the system.

In order to solve the above technical problems, the present invention provides a receiving end for receiving signals from the MIMO system transmitting end, including a signal detection module, and also includes a unified channel decomposition module, the unified channel decomposition module decomposes the channel matrix, The decomposed precoding matrix is fed back to the transmitting end, and the decomposed weight coefficient matrix is sent to the signal detection module.

Further, the signal detection module is a SIC-MMSE detection module.

Another technical problem to be solved by the present invention is to provide a transmitting end that can adapt to different numbers of transmitting and receiving antennas.

In order to solve the above-mentioned technical problems, the present invention provides a transmitting end, which is used to transmit signals of the MIMO system, including antennas and space-time coding modules, the antennas are divided into multiple groups, and the number of space-time coding modules is the same as The number of antenna groups corresponds, and the space-time coding modules are respectively connected to the antenna groups, and the signals are space-time coded and sent out through grouped antennas.

Further, the two antennas are grouped into one group.

Another technical problem to be solved by the present invention is to provide a signal processing method for an adaptive MIMO system, so that it can adapt to the performance of high-correlation channels, and at the same time can adapt to different numbers of transmitting and receiving antennas, and improve the capacity of the system. performance.

In order to solve the above-mentioned technical problems, the present invention provides a signal processing method for an adaptive MIMO system, which is used to process signals from a transmitting end to a receiving end, and is characterized in that it includes the following steps:

(1) grouping the antennas of the transmitting end;

(2) The transmitting end generates a signal, and performs space-time coding on the signal group;

(3) The space-time coded signal is sent through the grouped antennas;

(4) The receiving end receives the grouped signal, performs matrix decomposition, and then feeds it back to the transmitting end.

Further, the receiving end includes a channel estimation module, a signal detection module and a matrix decomposition module, the channel estimation module is connected with the matrix decomposition module, and in step (4), the channel estimation module processes after receiving the grouped signal , the obtained channel parameters are sent to the matrix decomposition module, and the matrix decomposition module performs matrix decomposition on the channel matrix, decomposes the precoding matrix and feeds it back to the transmitting end, and decomposes the weighting coefficient matrix and sends it to the signal detection module.

Further, the matrix decomposition module in step (4) is a unified channel decomposition module.

The present invention uses a UCD decomposition feedback scheme, which can greatly improve the bit error rate performance of the system while ensuring the maximum system capacity, so that the system can still work normally under different channel environments (such as high correlation channels). In addition, when the number of receiving antennas is less than the number of sending antennas, combining space-time coding, antenna grouping and UCD decomposition feedback technology not only enables the system to adapt to such a number of antennas and work normally, but also improves the system capacity compared with existing solutions and bit error rate performance.

Description of drawings

Fig. 1 is a schematic diagram of a 2-transmit 2-receive MIMO system based on SVD decomposition;

Fig. 2 is the virtual channel model of SVD scheme;

Fig. 3 is a 4-transmit and 2-receive VBLAST system using space-time coding;

Fig. 4 is a schematic diagram of the present invention based on UCD decomposition of 2 transmission 2 reception adaptive MIMO system;

5 is a schematic diagram of the present invention using space-time coding and antenna grouping at the transmitting end to realize a 4-transmit and 2-receive MIMO system;

FIG. 6 is a schematic diagram of the present invention performing space-time coding on two transmit antennas simultaneously;

FIG. 7 is a schematic diagram of a 4-transmission and 2-reception adaptive MIMO system based on UCD decomposition in the present invention.

Detailed ways

From the perspective of the SVD scheme, the high correlation of the channel leads to the reduction of the singular value of the channel, which also leads to the reduction of the signal gain of the virtual sub-channel. When the channel parameters are constant, the total gain of each sub-channel is constant, so for different MIMO schemes, the increase of channel correlation can be considered as the decrease of channel gain, while for the MIMO architecture with multiple sub-channels, the system error The code rate performance is determined by the worst sub-channel, so optimizing the worst channel (that is, making each sub-channel gain the same) under the premise of ensuring the system capacity can maximize the system performance.

The present invention uses a scheme called "Uniform Channel Decomposition" (Uniform Channel Decomposition, UCD) to carry out precoding at the sending end and signal detection at the receiving end, so as to ensure the maximum system capacity at the same time in MMSE (minimize the mean square value of the error) Under the detection criterion, the gains of each virtual sub-channel are made the same.

The invention also makes the system adaptable to different numbers of antennas by grouping the antennas of the transmitting end and performing space-time coding on the signal grouping.

In addition, the present invention combines UCD decomposition, space-time coding, and antenna grouping to provide an adaptive MIMO system architecture, which can adapt to the requirements of different antenna numbers while improving system performance.

Preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

Preferred embodiment one:

Referring to FIG. 4 , it is a schematic diagram of a 2-transmission-2-reception adaptive MIMO system based on UCD decomposition in the present invention. The system includes a transmitting end, a receiving end and a feedback path 413. The transmitting end includes: an information source module 401, a modulation module 402, a precoding module 403, a serial-to-parallel conversion module 404, a VBLAST module 405, and two transmitting antennas 406; the receiving end It includes: two receiving antennas 407, a linear combiner 408, a channel estimation module 409, a MIC-MMSE detection module 410, a UCD decomposition module 411, and a demodulation module 412.

For the signal detection module, this embodiment adopts the MIC-MMSE detection module, of course, other types of signal detection modules can also be used in practical applications. This is also applicable to the third preferred embodiment.

The UCD decomposition module 410 decomposes matrix F and matrix W, matrix F is a precoding matrix, and is fed back to the precoding module 403 of the transmitting end through the feedback path 413, and participates in precoding processing of signals; matrix W is SIC (continuous interference cancellation, that is, for The signals that need to be detected are layered, and the results of each layer of detection are substituted into the next layer to eliminate interlayer interference)-MMSE The weight coefficient matrix required for detection is sent to the SIC-MMSE detection module 409 through the UCD decomposition module 410, and the participating signal detection processing.

For the specific working process of the UCD decomposition module, refer to the third preferred embodiment.

As mentioned earlier, for MIMO architectures with multiple subchannels, the BER performance of the system is determined by the worst subchannel. The UCD scheme optimizes the worst sub-channel to the greatest extent, thereby optimizing the system performance and making it usable under high-correlation channel conditions.

Preferred embodiment two:

In the case that there are fewer receiving antennas than transmitting antennas, we propose a scheme of grouping the transmitting antennas to solve the problem of low code rate in the existing space-time coding scheme. Taking MIMO with 4 transmissions and 2 receptions as an example, the block diagram of this solution is shown in Figure 5.

Referring to FIG. 5 , it is a schematic diagram of a 4-transmit, 2-receive MIMO system implemented by using space-time coding and transmitting-end antenna grouping according to the present invention. The transmitting end of the system includes: an information source module 501, a modulation module 502, a serial-to-parallel conversion module 503, a space-time encoding module 504, and a transmitting antenna 505; the receiving end includes: a receiving antenna 506, a linear combiner module 507, and a SIC-MMSE detection module 508, a demodulation module 509. In this embodiment, there are two receiving antennas 506 and four transmitting antennas 505. The number of receiving antennas 506 is less than the number of transmitting antennas 505, and every two transmitting antennas are divided into groups. There are two space-time coding modules 504 in this embodiment, corresponding to the number of groups of antennas.

This embodiment only gives examples of the number of antenna groups and the number of space-time coding modules. Of course, antennas can also be grouped in other numbers, and the number of space-time coding modules corresponds to the number of groups of antennas. This is also applicable to the third preferred embodiment.

Assuming that there are K groups in total, each group of transmitting antennas transmits two different data in two time slots. Referring to FIG. 6 , it is a schematic diagram of simultaneous space-time coding of two transmitting antennas in the present invention. The data of the transmitting end before space-time encoding at the current moment are c ₁ c ₂ respectively, and the data forming the first and second antenna groups after space-time encoding are $\left(\begin{array}{cc}{c}_1& {-c}_2^{*}\\ c_2& c_1^{*}\end{array}\right),$ Where c ^* represents the conjugate of c, and the first column and the second column of data are sent at time t and t+T respectively. The sending end sends K different data in each time slot on average, and the efficiency is improved by K times compared with the existing scheme 2. This scheme requires that the number of receiving antennas is not less than K.

Preferred embodiment three:

In order to make the MIMO system adapt to different antenna numbers and different correlation channel environments, we propose a new adaptive MIMO system.

Referring to FIG. 7 , it is a schematic diagram of a 4-transmission-2-reception adaptive MIMO system based on UCD decomposition in the present invention. The system includes a transmit end and a receive end and a feedback path 713 . The transmitting end includes: an information source module 701, a modulation module 702, a precoding module 703, a serial-to-parallel conversion module 704, a space-time coding module 705, and a transmitting antenna 706; the receiving end includes: a receiving antenna 707, a linear combiner 708, and a channel estimation module 709 , a SIC-MMSE detection module 710 , a UCD decomposition module 711 , and a demodulation module 712 .

It can be seen from the figure that there are four transmitting antennas 706 in this embodiment, which are divided into two groups, each group has two antennas, and the corresponding space-time coding modules 705 are also two. The receiving end 71 has two receiving antennas. After serial-to-parallel conversion by the serial-to-parallel conversion module 704 at the transmitting end, the signals are divided into two groups and input to two space-time coding modules 705, and then sent out through two sets of transmitting antennas 706 after space-time coding. A schematic diagram of space-time coding is shown in FIG. 6 above. After receiving the signal, the receiving end first obtains the channel parameters by the channel estimation module 709, sends them to the UCD decomposition module 711, and then decomposes the matrix F and matrix W by the UCD decomposition module 711, and the matrix F is fed back to the precoding of the transmitting end through the feedback path 713 In module 703, the matrix W is sent to the SIC-MMSE detection module 710 through the UCD decomposition module 711 to participate in signal detection processing.

The receiver detection process is as follows:

Assuming that the current channel is block fading (the channel parameters remain unchanged in a short period of time), the channel matrix is $h=\left(\begin{array}{cccc}{h}_{11}& h_{\mathrm{twenty\; one}}& g_{11}& g_{\mathrm{twenty\; one}}\\ h_{12}& h_{\mathrm{twenty\; two}}& g_{12}& g_{\mathrm{twenty\; two}}\end{array}\right)$ (The number of rows of the channel matrix is equal to the number of receiving antennas, and the number of columns is equal to the number of transmitting antennas). The data before space-time coding of the first and second antenna groups at the transmitting end at the current moment are c ₁ c ₂ and s ₁ s ₂ respectively.

The characters r ₁₁ and r ₁₂ received by the first receiving antenna 707 at the receiving end in two consecutive time slots can be written as:

r ₁₁ =h ₁₁ c ₁ +h ₂₁ c ₂ +g ₁₁ s ₁ +g ₂₁ s ₂ +η ₁₁ (1)

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

make $r_1={\left(\begin{array}{cc}{r}_{11}& r_{12}^{*}\end{array}\right)}^T,$ c=(c ₁ c ₂ ) ^T , s=(s ₁ s ₂ ) ^T , ${\mathrm{\&eta;}}_1={\left(\begin{array}{cc}{\mathrm{\&eta;}}_{11}& {\mathrm{\&eta;}}_{12}^{*}\end{array}\right)}^T,$ So formulas 1 and 2 can be written as:

r ₁ =H ₁ c+G ₁ s+η ₁ (3)

in

${\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\left(\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}\\ {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}^{<span\; class="notranslate">\; *</span>}& {<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}^{<span\; class="notranslate">\; *</span>}\end{array}\right)<span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\left(\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}& {\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}\\ {\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; one</span>}}^{<span\; class="notranslate">\; *</span>}& {<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}^{<span\; class="notranslate">\; *</span>}\end{array}\right)<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

_η1 is additive white Gaussian noise with zero mean.

Similarly, the received signals r ₂₁ and r ₂₂ at the second receiving antenna 707 at the receiving end can be obtained (let $r_2={\left(\begin{array}{cc}{r}_{\mathrm{twenty\; one}}& r_{\mathrm{twenty\; two}}^{*}\end{array}\right)}^T$ ) can be written as:

r ₂ =H ₂ c+G ₂ s+η ₂ (5)

in

${\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\left(\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}\\ {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}^{<span\; class="notranslate">\; *</span>}& {<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; *</span>}\end{array}\right)<span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\left(\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}& {\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}\\ {\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}^{<span\; class="notranslate">\; *</span>}& {<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; *</span>}\end{array}\right)<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

We define the global acceptance signal vector r as:

$\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ {\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right]<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; h</span>}\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; =</span>\left(\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}& {\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}& {\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right)\left(\begin{array}{c}\mathrm{<span\; class="notranslate">\; c</span>}\\ \mathrm{<span\; class="notranslate">\; the\; s</span>}\end{array}\right)<span\; class="notranslate">\; +</span>\left(\begin{array}{c}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ {\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right)<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

In formula 7, c and s are the original modulation data before space-time coding, and H is a matrix of 4 rows and 4 columns, which can be regarded as a virtual transmission channel. The original data can be obtained by performing SIC-MMSE detection on formula 7.

The present invention uses UCD decomposition feedback to precode the transmitted information. When the number of receiving antennas is less than the number of transmitting antennas, the transmitted data is processed by antenna grouping and space-time coding, which can adapt to different antenna numbers. Combining UCD decomposition, antenna grouping and space-time coding to reprocess the transmitted data, it can adapt to different antenna numbers while improving system performance.

Of course, the preferred embodiments of the present invention are not limitations to the technical solutions of the present invention, and equivalent replacements or corresponding improvements to the technical features of the present invention still fall within the protection scope of the present invention.

Claims (4)

1. An adaptive MIMO system, comprising a transmitting end, a receiving end, and a feedback path from the receiving end to the transmitting end, the transmitting end comprises an antenna, and the receiving end comprises a signal detection module, wherein, The signal detection module is a SIC-MMSE detection module, and the receiving end also includes a unified channel decomposition module. The unified channel decomposition module decomposes the channel matrix, and the decomposed precoding matrix is fed back to the transmitting channel via the feedback path. end, decompose the weighting coefficient matrix needed for SIC-MMSE detection and send it to the SIC-MMSE detection module, and the antennas are divided into multiple groups; the system also includes a space-time coding module, and the space-time coding module is connected with the The grouped antennas are connected, and the signal grouping is space-time coded. 2. The adaptive MIMO system according to claim 1, wherein two antennas are divided into one group. 3. A signal processing method of an adaptive MIMO system, for processing the signal from the transmitting end to the receiving end, it is characterized in that, comprising the following steps: (1) grouping the antennas of the transmitting end; (2) The transmitting end generates a signal, and performs space-time coding on the signal group; (3) The space-time coded signal is sent through the grouped antennas; (4) The receiving end receives the grouped signal, and the unified channel decomposition module at the receiving end decomposes the channel matrix, decomposes the precoding matrix and feeds it back to the transmitting end through the feedback path, and decomposes the weighting required for SIC-MMSE detection The coefficient matrix is sent to the SIC-MMSE detection module at the receiving end. 4. the method for claim 5 is characterized in that, described receiver also comprises channel estimation module, and described channel estimation module is connected with unified channel decomposition module, and in step (4), channel estimation module receives the After processing the grouped signals, the obtained channel parameters are sent to the unified channel decomposition module.

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