阅读说明：本技术 一种mimo信道估计的改进方法和系统 (Method and system for improving MIMO channel estimation ) 是由 蒋芜 吴建兵 于 2021-01-13 设计创作，主要内容包括：本发明提供一种MIMO信道估计的改进方法及系统,所述MIMO信道估计的改进方法包括以下步骤：步骤S1,基于WiFi协议实现MIMO信道估计；步骤S2,逐个子载波进行信道估计和泄漏权值计算；步骤S3,根据步骤S2计算的泄漏权值,将所述泄漏权值与噪声进行比较,以此对信道估计矩阵H的各个元素进行修正；步骤S4,对每个子载波上的信道估计计算MMSE修正均衡矩阵。本发明通过权衡噪声和流间泄漏的影响,并综合考虑到LS信道估计和MMSE估计的优势,即考虑到了流间泄漏的问题,也能够在抑制噪声上达到较好的效果,进而达到提升接收性能的目的。(The invention provides an improvement method and a system for MIMO channel estimation, wherein the improvement method for the MIMO channel estimation comprises the following steps: step S1, realizing MIMO channel estimation based on WiFi protocol; step S2, channel estimation and leakage weight calculation are carried out one by one on the subcarriers; step S3, comparing the leakage weight value with the noise according to the leakage weight value calculated in step S2, thereby estimating the channel matrix H Correcting each element of the table; step S4, an MMSE modified equalization matrix is calculated for the channel estimates on each subcarrier. The invention can achieve better effect on noise suppression by balancing the influence of noise and inter-stream leakage and comprehensively considering the advantages of LS channel estimation and MMSE estimation, namely considering the problem of inter-stream leakage, thereby achieving the purpose of improving the receiving performance.)

1. A method for improving MIMO channel estimation, comprising the steps of:

step S1, realizing MIMO channel estimation based on WiFi protocol;

step S2, channel estimation and leakage weight calculation are carried out one by one on the subcarriers;

step S3, according to the leakage weight calculated in step S2, comparing the leakage weight with noise, so as to correct each element of the channel estimation matrix H;

step S4, an MMSE modified equalization matrix is calculated for the channel estimates on each subcarrier.

2. The method for improving MIMO channel estimation according to claim 1, wherein said step S2 comprises the following sub-steps:

step S201, estimating each channel estimation coefficient to the channel on the sub-carrier kCalculating its response power P^kWherein i is 1,2, … N_t，j＝1,2,…,N_r，N_tNumber of transmitting antennas, N_rIs the number of receive antennas;

step S202, according to the sonChannel estimation coefficient on carrier kBy responding to the power P^kValue calculation normalized power P on the principal diagonal_s；

Step S203, calculating response power P^kNormalized response power after normalization processing

Step S204, calculating the normalized response powerEach element ofLeakage weight of

3. The method of claim 2, wherein in step S201, the channel estimation is improved by formulaCalculating its response power P^kWherein | x | is a modulus of the complex signal x.

4. The method of claim 3, wherein in step S202, the channel estimation is improved by formulaCalculating normalized power P_sWherein, E [ x ]]To average x.

5. Method for improving MIMO channel estimation according to claim 4Method, characterized in that in step S203, the formula is usedCalculating normalized response power

6. The method of claim 5, wherein in step S204, the channel estimation is improved by formulaCalculating leakage weightsWherein the content of the first and second substances,to normalize the response powerThe element of the ith row and the ith column,to normalize the response powerThe element in the jth row and jth column,to normalize the response powerThe element in the jth row and ith column,is composed ofThe calculated weight.

7. The method for improving MIMO channel estimation according to any of claims 1 to 6, wherein the step S3 comprises the following sub-steps:

step S301, counting noise information of the receiver

Step S302, comparing the leakage weightAnd noise informationFor channel estimation matrix H^kChannel estimation coefficient ofMaking adjustments so that i is 1,2, … N_t，j＝1,2,…,N_r，N_tNumber of transmitting antennas, N_rK is the subcarrier for the number of receive antennas.

8. The method for improving MIMO channel estimation according to claim 7, wherein in step S302, the channel estimation is performed according to a formulaFor channel estimation matrix H^kChannel estimation coefficient ofAdjusting, wherein thr is a set threshold value and takes any value greater than 1; p_sIs the normalized power.

9. The method for improving MIMO channel estimation according to claim 8, wherein in step S4, the formula is shownAn MMSE modified equalization matrix is calculated for the channel estimates on each subcarrier, wherein,for estimating a matrix H for a channel^kThe channel estimation correction matrix after step S3 (·)^*Denotes the conjugate transpose, I is the identity matrix.

10. An improved MIMO channel estimation system, characterized in that the improved method of MIMO channel estimation according to any of claims 1 to 9 is used, and comprises:

the channel estimation module is used for realizing MIMO channel estimation based on a WiFi protocol;

the noise and leakage weight calculation module is used for performing channel estimation and leakage weight calculation on the subcarriers and respectively processing each subcarrier;

a corrected channel estimation matrix module, which compares the leakage weight with the noise according to the noise and the leakage weight calculated by the leakage weight calculation module, so as to correct each element of the channel estimation matrix H;

and the module for calculating a modified equalization matrix calculates an MMSE modified equalization matrix for channel estimation on each subcarrier.

Technical Field

The invention relates to MIMO channel estimation based on an OFDM wireless system, in particular to an improved method of MIMO channel estimation based on a training sequence under an MIMO scene under an 802.11n/ac/ax standard, and an improved system adopting the improved method of the MIMO channel estimation.

Background

MIMO refers to a multiple antenna technology. MIMO technology can be defined simply as: in a wireless communication system, multiple antennas are used at both the transmitting and receiving ends of a link. The MIMO system is characterized in that under the condition of not increasing frequency spectrum resources and antenna transmission power, the MIMO channel can be used for providing space multiplexing gain to improve the capacity of the channel, and the space diversity gain provided by the MIMO channel can be used for improving the reliability of the channel and reducing the error rate.

WLANs of the IEEE802.11 standard offer high-rate, high-quality broadband service applications, where the core technologies are OFDM technology and MIMO technology. 802.11n supports 4 antenna stream OFDM-MIMO transmission and reception, while 802.11ac and 802.11ax support to 8 antenna stream MIMO-OFDM transmission and reception. The MIMO technology generates a plurality of independent parallel channels in space and simultaneously transmits a plurality of data streams, thereby effectively increasing the transmission efficiency of the system.

In the MIMO-OFDM transmission system, transmission signals are transmitted from a plurality of transmission antennas simultaneously and arrive at each reception antenna almost synchronously, because signals obtained from each reception antenna are the superposition of a plurality of transmission signals, the channel response between each pair of transmission and reception antennas is also affected by signals between other transmission and reception antennas, the complexity of channel estimation is higher than SISO, and a channel estimation algorithm is required to estimate the channel characteristics of a plurality of parallel channels between each transmission antenna and the same reception antenna. On the premise of mutual influence among multi-stream channels, if channel estimation is not ideal, the performance of the system is rapidly reduced, so accurate channel estimation is the key point for ensuring the transmission quality of the MIMO-OFDM system and playing the superiority thereof.

Disclosure of Invention

The technical problem to be solved by the present invention is to provide an improved method for MIMO channel estimation, which further achieves the purpose of improving the receiving performance by balancing the influence of noise and inter-stream leakage and comprehensively considering the advantages of LS channel estimation and MMSE channel estimation.

In view of the above, the present invention provides an improved method for MIMO channel estimation, comprising the steps of:

step S1, realizing MIMO channel estimation based on WiFi protocol;

step S2, channel estimation and leakage weight calculation are carried out one by one on the subcarriers;

step S3, according to the leakage weight calculated in step S2, comparing the leakage weight with noise, so as to correct each element of the channel estimation matrix H;

step S4, an MMSE modified equalization matrix is calculated for the channel estimates on each subcarrier.

A further refinement of the invention is that said step S2 comprises the following sub-steps:

step S201, estimating each channel estimation coefficient to the channel on the sub-carrier kCalculating its response power P^kWherein i is 1,2, … N_t，j＝1,2,…,N_r，N_tNumber of transmitting antennas, N_rIs the number of receive antennas;

step S202, estimating the coefficient according to the channel on the subcarrier kBy responding to the power P^kValue calculation normalized power P on the principal diagonal_s；

Step S203, calculating response power P^kNormalized response power after normalization processing

Step S204, calculating the normalized response powerEach element ofLeakage weight ofThe main diagonal is unchanged.

The invention is further improved in that in the step S201, the formula is usedCalculating its response power P^kWherein | x | is a modulus of the complex signal x.

The invention is further improved in that in the step S202, the formula is usedCalculating normalized power P_sWherein, E [ x ]]To average x.

The invention is further improved in that in the step S203, the formula is usedCalculating normalized response power

The invention is further improved in that in the step S204, the formula is usedCalculating leakage weightsWherein the content of the first and second substances,to normalize the response powerThe element of the ith row and the ith column,to normalize the response powerThe element in the jth row and jth column,to normalize the response powerThe element in the jth row and ith column,is composed ofThe calculated weight.

A further refinement of the invention is that said step S3 comprises the following sub-steps:

step S301, counting noise information of the receiver

Step S302, comparing the leakage weightAnd noise informationFor channel estimation matrix H^kChannel estimation coefficient ofMaking adjustments so that i is 1,2, … N_t，j＝1,2,…,N_r，N_tNumber of transmitting antennas, N_rK is the subcarrier for the number of receive antennas.

The invention is further improved in that in the step S302, the formula is usedFor channel estimation matrix H^kChannel estimation coefficient ofAdjusting, wherein thr is a set threshold value and takes any value greater than 1; p_sIs the normalized power.

In a further improvement of the present invention, in the step S4, the formula is used An MMSE modified equalization matrix is calculated for the channel estimates on each subcarrier, wherein,for estimating a matrix H for a channel^kThe channel estimation correction matrix after step S3 (·)^*Denotes the conjugate transpose, I is the identity matrix.

The present invention also provides an improved MIMO channel estimation system, which adopts the improved MIMO channel estimation method described above, and includes:

the channel estimation module is used for realizing MIMO channel estimation based on a WiFi protocol;

the noise and leakage weight calculation module is used for performing channel estimation and leakage weight calculation on the subcarriers and respectively processing each subcarrier;

a corrected channel estimation matrix module, which compares the leakage weight with the noise according to the noise and the leakage weight calculated by the leakage weight calculation module, so as to correct each element of the channel estimation matrix H;

a calculation correction equalization matrix module which calculates an MMSE correction equalization matrix for channel estimation on each subcarrier; MMSE refers to the minimum mean square error.

Compared with the prior art, the invention has the beneficial effects that: by balancing the influence of noise and inter-stream leakage and comprehensively considering the advantages of LS channel estimation and MMSE estimation, namely considering the problem of inter-stream leakage, the method can achieve a good effect of suppressing noise, further achieve the purpose of improving the receiving performance, and provide a good basis for improving the testing efficiency and reducing the complexity of a tester and a system.

Drawings

FIG. 1 is a schematic workflow diagram of one embodiment of the present invention;

FIG. 2 is a diagram of a MIMO system model;

fig. 3 is a schematic view of a scenario in which a device under test is connected to a tester by a radio frequency line (Cable line).

Detailed Description

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

The MIMO System model is shown in FIG. 2, with N_tRoot transmitting antenna, N_rThe root receives the antenna. Under flat fading channel, h_j,iIndicating the channel fading coefficients between the ith transmitting antenna to the jth receiving antenna. x is the number of_iIs the data transmitted by the ith transmit antenna. y is_jIs the data received by the jth receiving antenna. Comprises the following steps:

the form of the transmission signal matrix is as follows:the received signal matrix form is: the noise signal matrix form is:the channel matrix is:

writing the channel transmission relationship into a matrix form is as follows: y ═ HX + N, where Y is N_rA received signal vector of X1 dimension, X being N_tA transmitted signal vector of x 1 dimension, N being N_r×N_tAdditive gaussian noise.

The linear signal detection method treats the desired signal stream from the target antenna as useful information while treating other transmitted signals as interference. Therefore, in detecting the desired signal from the target transmitting antenna, the interference signals of other transmitting antennas are minimized or eliminated. To detect the desired signal from each antenna, the channel is inverted using a weighting matrix W:

based on Zero Forcing (ZF) technique and Minimum Mean Square Error (MMSE) technique of training sequence conventional channel estimation algorithm, since the ideal situation of the training sequence at the transmitting end and the receiving end is known and the channel can be assumed to be unchanged in one data frame, the channel response H of the training sequence part is estimated, and then the weighting matrix W is estimated from the channel response H.

The LS technique cancels interference using the following weighting matrix: w_LS＝(H^*H)^-1H^*Wherein (·)^*Indicating the complex conjugate transpose. Then

MMSE can maximize the detected SINR, let the weighting matrix be: MMSE receivers require statistical information on the noise.

The ith row vector of the weighting matrix in the above equation is derived from the following optimization equation:using the MMSE weighting matrix, the following relationship can be obtained:

on the premise that the channel response in a frame is not changed, the receiving can be balanced to obtain the estimated value of the transmission signal, and the data receiving is finally completed by means of a decoding algorithm.

In the process of MIMO transmission, noise interference and inter-stream signal leakage exist, and if the streams are approximately orthogonal, for example, the MIMO multi-stream isolation is good, a channel estimation matrix H is approximate to a unit matrix, and a small amount of inter-stream leakage and noise appear as a very small value on a non-main diagonal of the channel estimation matrix H, when the MIMO multi-stream isolation is applied to a test instrument and a device to be tested is connected to the test instrument by a radio frequency line.

The noise may cause deterioration of demodulation performance, and correct inter-collection leakage may improve reception performance. The LS estimation method fully considers the inter-stream leakage problem, but has a weak noise suppression capability, and the MMSE method is better in noise suppression, but suppresses the inter-stream leakage.

Therefore, the present example aims to provide an improved method for MIMO channel estimation, which achieves the purpose of improving the receiving performance by balancing the influence of noise and inter-stream leakage and comprehensively considering the advantages of LS channel estimation and MMSE estimation.

As shown in fig. 1, this example provides an improved method for MIMO channel estimation, which includes the following steps:

step S1, realizing MIMO channel estimation based on WiFi protocol;

step S2, channel estimation and leakage weight calculation are carried out one by one on the subcarriers;

step S3, according to the leakage weight calculated in step S2, comparing the leakage weight with noise, so as to correct each element of the channel estimation matrix H;

step S4, an MMSE modified equalization matrix is calculated for the channel estimates on each subcarrier.

The embodiment is applied to a test instrument, and under the application environment that the device to be tested is connected with the test instrument by a radio frequency line, as shown in fig. 3, the isolation between MIMO multiple streams is good.

Suppose the number of transmitting antennas of the MIMO system is N_tHaving N of_tRoot transmitting antenna, N_rThe root receives the antenna. Under flat fading channel, h_j,iIndicating the channel fading coefficients between the ith transmitting antenna to the jth receiving antenna. x is the number of_iIs the data transmitted by the ith transmit antenna. y is_jIs the data received by the jth receiving antenna. Comprises the following steps:

the form of the transmission signal matrix is as follows:the received signal matrix form is: the noise signal matrix form is:the channel matrix is:

writing the channel transmission relationship into a matrix form is as follows: y ═ HX + N, where Y is N_rA received signal vector of X1 dimension, X being N_tA transmitted signal vector of x 1 dimension, N being N_r×N_tAdditive gaussian noise. In which the device under test is connected to the tester by radio-frequency linesIn the scene, N_r＝N_t。

In this example, step S1 is used to implement MIMO channel estimation based on the WiFi protocol, or implement MIMO channel estimation based on the training sequence. This can be achieved by a training sequence; in N_r＝N_tIn the MIMO system of (1), x_iRepresenting the ith stream transmission signal according to when there is N_LTFA training sequence of N_LTFAnd N_rThe relationship is such that,if the original frequency domain value of the training sequence on the subcarrier k is R_k，R_kIs protocol-specific and known, X_i,iLTFRepresents the frequency-domain value on the ith training sequence subcarrier k of the ith streaming signal, then X_i,iLTF＝PHT_i,iLTF*R_k. Designed according to a protocol, PHT_i,iLTFThere are the following properties that are present,the PHT matrix defined by the protocol described in this example is an HT-LTF mapping matrix under (including) 4 × 4, i.e.:4 x 4 or more is

Estimating a channel estimate h_j,iFirst, a training sequence part, Y, of a j-th stream received signal is selected_j,iLTFThe frequency domain value of the ith training sequence subcarrier k representing the j stream received signal has PHT_i,iLTFIs a protocolDetermined value, n, on row i, column iLTF of PHT_jIs a noisy representation of column j.

Adding up training sequences on the received signal, multiplying the training sequences by PHT_j,iLTFThen, the process of the present invention,

simplifying and merging noise parts, then n_j,iAccumulating values Q for training sequences_j,iInternal noise, PHT_j,iLTFThe value on the jth row and iLTF column of the PHT determined for the protocol.

By PHT_i,iLTFAttribute is thenNoise and leakage are both in Q_j,i-n_j,iIn the above, if the ideal state is true, the channel estimate H has values only on the main diagonal.

Step S2 described in this example is used to implement noise estimation and leakage weight calculation, and process each subcarrier separately;is h_j,iAnd (3) estimating a coefficient on a subcarrier k, wherein for an ideal MIMO test scene, when j is not equal to i, the channel response only has noise and inter-stream leakage, and when j is equal to i, the channel response only has the current stream channel response and the noise. By H^kRepresents the channel estimation matrix on subcarrier k, then

Step S2 in this example preferably includes the following sub-steps:

step S201, estimating each channel estimation coefficient to the channel on the sub-carrier kCalculating its response power P^kWherein i is 1,2, … N_t，j＝1,2,…,N_r，N_tNumber of transmitting antennas, N_rIs the number of receive antennas;

step S202, estimating the coefficient according to the channel on the subcarrier kBy responding to the power P^kValue calculation normalized power P on the principal diagonal_s；

Step S203, calculating response power P^kNormalized response power after normalization processing

Step S204, calculating the normalized response powerEach element ofLeakage weight ofThe main diagonal is unchanged.

In step S201 of the present example, the formula is usedCalculating its response power P^kWherein | x | is a modulus of the complex signal x.

In step S202 in this example, the formula is shownCalculating normalized power P_sWherein, E [ x ]]To average x.

In step S203 described in this example, the formula is usedCalculating normalized response power

In step S204 described in this example, the formula is usedCalculating leakage weightsWherein the content of the first and second substances,to normalize the response powerThe element of the ith row and the ith column,to normalize the response powerThe element in the jth row and jth column,to normalize the response powerThe element in the jth row and ith column,is composed ofThe calculated weight.

In this example, step S3 compares the calculated leakage weight with the relative noise according to step S2, and corrects each element of the channel estimation matrix H to achieve the purpose of suppressing noise and preserving leakage.

Step S3 in this example includes the following substeps:

step S301, counting noise information of the receiver

Step S302, comparing the leakage weightAnd noise informationFor channel estimation matrix H^kChannel estimation coefficient ofMaking adjustments so that i is 1,2, … N_t，j＝1,2,…,N_r，N_tNumber of transmitting antennas, N_rK is the subcarrier for the number of receive antennas.

In step S302 of the present example, the formula is usedFor channel estimation matrix H^kChannel estimation coefficient ofAdjusting, wherein thr is a set threshold value and takes any value greater than 1; p_sIs the normalized power.

When in useAnd (3) when the normalized power-to-noise ratio is smaller than the set threshold thr, the leakage part is considered to be negligible, otherwise, the leakage part needs to be reserved. For channel estimation matrix H^kCorrected in step S3 and recorded asAnd furthermore, the retention of the leakage term and the suppression of the noise term can be considered, and the requirement of the embodiment for improving the receiving performance is met.

In step S4 in this example, the formula is usedAn MMSE modified equalization matrix is calculated for the channel estimates on each subcarrier, wherein,for estimating a matrix H for a channel^kThe channel estimation correction matrix after step S3 (·)^*Denotes the conjugate transpose, I is the identity matrix.

And then repeating the steps S1 to S4, and traversing all the subcarriers to obtain an MMSE modified equalization matrix on each subcarrier. When analyzing the data domain, analyzing the frequency domain data of each Symbol by using an MMSE (minimum mean square error) correction equalization matrix, and finally, correctly receiving the data. The advantages of the LS method and the MMSE method are comprehensively considered by the MMSE correction equalization matrix, and the performance evaluated in the scene that the test instrument is used for completing the connection from the equipment to be tested to the test instrument through the radio frequency line is obviously improved.

This example also provides an improved MIMO channel estimation system, which employs the improved MIMO channel estimation method described above and includes:

the channel estimation module is used for realizing MIMO channel estimation based on a WiFi protocol;

the noise and leakage weight calculation module is used for performing channel estimation and leakage weight calculation on the subcarriers and respectively processing each subcarrier;

a corrected channel estimation matrix module, which compares the leakage weight with the noise according to the noise and the leakage weight calculated by the leakage weight calculation module, so as to correct each element of the channel estimation matrix H;

and the module for calculating a modified equalization matrix calculates an MMSE modified equalization matrix for channel estimation on each subcarrier.

In summary, in this embodiment, by balancing the influence of noise and inter-stream leakage, and comprehensively considering the advantages of LS channel estimation and MMSE estimation, that is, considering the problem of inter-stream leakage, a better effect on suppressing noise can be achieved, so as to achieve the purpose of improving the receiving performance, and provide a good basis for improving the test efficiency and reducing the complexity of the tester and the system.

The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.