CN112653497A - Signal transceiving method for reducing MIMO multichannel phase noise influence - Google Patents

Signal transceiving method for reducing MIMO multichannel phase noise influence Download PDF

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CN112653497A
CN112653497A CN202011520005.9A CN202011520005A CN112653497A CN 112653497 A CN112653497 A CN 112653497A CN 202011520005 A CN202011520005 A CN 202011520005A CN 112653497 A CN112653497 A CN 112653497A
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张昌明
许乙付
罗喜伶
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Hangzhou Innovation Research Institute of Beihang University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
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    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
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Abstract

The invention discloses a signal receiving and transmitting method for reducing the influence of MIMO multi-channel phase noise. The signal transceiving process of the invention is mainly optimized aiming at the differentiation part of the phase noise of each channel. In the MIMO system with multi-channel phase noise, the signal receiving and transmitting process is optimized by combined suppression with Gaussian white noise according to the statistical characteristic of the phase noise. The invention can improve the system performance and further improve the communication capacity. The technical scheme of the invention can obviously reduce the influence of phase noise and simultaneously realize better suppression of white noise.

Description

Signal transceiving method for reducing MIMO multichannel phase noise influence
Technical Field
The invention belongs to the field of wireless communication and relates to a Multiple-Input Multiple-Output (MIMO) system signal transceiving technology, in particular to a signal transceiving method capable of reducing the influence of MIMO multichannel phase noise.
Background
With the development of fifth/sixth generation mobile communication technology (5G/6G), the demand for transmission capacity is continuously increasing. Under the condition of limited frequency spectrum resources, the MIMO technology realizes parallel transmission by constructing a plurality of channels between transmitting and receiving by using a plurality of antennas at the transmitting end and the receiving end, and can improve the communication capacity by times. The MIMO technology has gained wide attention and application in many scenarios in the field of wireless communication, such as wireless access networks, wireless bearer networks, and the like.
In order to achieve the best improvement of communication capacity, the MIMO system theoretically requires that the distances between the antennas at the transmitting end and the receiving end should both satisfy the rayleigh distance requirement shown in formula (1), where c is 3 × 108m/s is the speed of light, l is the communication distance between the transmitter and receiver, fcIs the carrier frequency. In practical situations, the ideal antenna spacing is difficult to satisfy, and especially in an environment with a long transmission distance, the ideal antenna spacing is too large, which poses a great challenge to both the installation environment and the cost. For example, 10Km is transmitted in a 15GHz band, and the ideal antenna spacing reaches 10m, so that efficient deployment and debugging cannot be performed.
Figure BDA0002849195560000011
When the antenna spacing is smaller than the rayleigh distance, it is generally necessary to perform precoding on each channel signal at the transmitting end and perform corresponding equalization and signal demodulation at the receiving end, which can significantly improve the communication capacity of MIMO, thereby improving adverse effects caused by difficulty in achieving an ideal antenna spacing.
Considering that the number of transmitting and receiving antennas is N, the number of signal paths is also N, and in the case of no precoding, the received signal model is as shown in equation (2). Wherein, r, x, N are all N × 1 vectors, which respectively represent the received signal, the transmitted signal, and the noise (the time index is omitted) at a certain time, and H is an N × N matrix, which represents the channel response. By adopting equalization methods such as Zero Forcing (ZF) or Minimum Mean Square Error (MMSE) and the like at a receiving end, a transmission signal x can be directly obtained from r, but when the antenna spacing is smaller than the rayleigh distance, the condition number of H is large, noise can be obviously amplified after ZF or MMSE equalization, the performance of Mean Square Error (MSE) corresponding to the demodulation process is poor, and thus the communication capacity of the MIMO system is restricted.
r＝Hx+n (2)
Signal transceiving process based on precoding is realized by carrying out communication at a sending endThe signal is preprocessed to a certain extent, so that the receiving end can balance and demodulate the sending signal without obviously amplifying the noise. In the prior art, the implementation scheme based on precoding is directly completed by decomposing a channel response matrix H, that is, H is decomposed into a structure shown in formula (3). Wherein Q and P are both unitary matrices of NxN, i.e. QHQ＝QQHIs equal to I and PHP＝PPHWhere the superscript H represents the conjugate transpose of the matrix, I is the N × N identity matrix. When Singular Value Decomposition (SVD) is performed on H, R is a diagonal matrix; when Geometric Mean Decomposition (GMD) is performed on H, R is an upper triangular matrix with the same diagonal elements.
H＝QRPH (3)
Based on the channel matrix decomposition shown in the formula (3), P is selected as a precoding matrix, QHAnd (3) equalizing the matrix for the receiving end, wherein the obtained signal form is shown as a formula (4). Since P is unitary matrix, the precoding process does not change the transmission signal power, and since Q is unitary matrix, when the noise of each channel is independently and simultaneously distributed, the equalization process of the receiving end does not change the power and the distribution characteristic of the noise, i.e. n' and n have the same distribution characteristic. For SVD, all paths of the obtained signal y are decoupled, and each component is directly and respectively demodulated; for GMD decomposition, Serial Interference Cancellation (SIC) or sphere decoding may be used to demodulate the channel signal components in sequence.
y＝QHHPx+QHn＝Rx+n′ (4)
The maximum capacity can be theoretically obtained by performing power water injection on each path at the transmitting end while SVD precoding is adopted, but the power of each path is usually fixed in an actual system, so that the signal power is not conveniently and flexibly adjusted from one path to another path. In terms of algorithm implementation, a demodulation process corresponding to SVD decomposition is simpler than that under GMD decomposition, but the difference of diagonal elements of R under SVD decomposition is larger, and the MSE difference of each corresponding channel is more obvious, so that different channels need to be configured with different coding and modulation modes, and the implementation difficulty of the system is increased; at the same time, under SVD decomposition, it may happen that the MSE of some channels is too high to be reasonably utilized (corresponding to modulation orders exceeding the commonly used modulation modes, e.g. above 4096/8192 QAM), while the MSE of other channels is too low to be used for transmitting useful information, which results in that the available communication capacity is not high. Because the diagonal elements of R are the same under GMD decomposition, the MSE of each channel is theoretically the same, and the method has stronger application value in various scenes compared with SVD decomposition.
The existing corresponding signal transceiving method under SVD and GMD matrix decomposition can ensure better performance under the condition of only considering Additive White Gaussian Noise (AWGN), wherein in a traditional low-frequency communication system, the White Gaussian Noise is a main part of Noise, and the existing method has stronger applicability. However, as low-band frequency resources are increasingly scarce, communication frequency points gradually step toward high-frequency bands such as K band, Ka band, E band, even D band, etc. of microwave and millimeter waves, in these high-frequency systems, due to the non-ideal characteristic of local oscillation, phase noise inevitably occurs, which becomes another random noise affecting system performance besides white gaussian noise. At the transmitting end and the receiving end of the MIMO system, each channel has phase noise, and comprises a public part and a differentiation part, the public phase noise can be compensated by adopting a phase-locked loop and other modes, and the differentiation part can not be compensated by a simple phase correction algorithm, so that the performance of the MIMO communication system is seriously influenced. The existing MIMO signal transceiving method is not optimized for phase noise, particularly for a phase noise differential part which is difficult to compensate, and has no anti-phase noise capability, so that the MSE performance of a signal demodulation process is still poor, and the improvement capability of the communication capacity is very limited.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a signal transceiving method for reducing the influence of MIMO multi-channel phase noise. The technical scheme of the invention is as follows:
the invention provides a signal receiving and transmitting method for reducing the influence of MIMO multi-channel phase noise, which comprises the following steps:
1) according to the channel response matrix HAutocorrelation matrix among phase noises of channels of receiving end and transmitting end
Figure BDA0002849195560000031
And
Figure BDA0002849195560000032
gaussian white noise power
Figure BDA0002849195560000033
Calculating to obtain an autocorrelation matrix C of the whole noisezz；
Wherein the content of the first and second substances,
Figure BDA0002849195560000034
representing an expectation, the corresponding elements of the matrix are multiplied, I is an identity matrix, the superscript H represents a conjugate transpose of the matrix, Λ (·) represents that a diagonal matrix is constructed by taking the corresponding vector as a diagonal element, and z is a total noise component;
2) by pairs
Figure BDA0002849195560000035
Performing Cholesky decomposition to obtain a matrix L;
3) obtaining a precoding matrix P and a demodulation matrix B by executing SVD or GMD decomposition on L;
4) calculating to obtain an equilibrium matrix W;
5) and executing signal transceiving of the MIMO system according to the obtained precoding matrix P, the equalization matrix W and the demodulation matrix B.
In a specific embodiment of the present invention, the MIMO system targeted by the method has the same number of transmitting and receiving antennas, which are both N, and the number of signal paths is N.
In a specific embodiment of the present invention, the MIMO system targeted by the method has different numbers of transmit and receive antennas, and the corresponding number of effective signal paths is the smaller of the number of transmit antennas and the number of receive antennas.
In a specific embodiment of the present invention, the step 5) is specifically: in the process of receiving and transmitting signals of the MIMO system, a precoding matrix P obtained in the step 3) is adopted at a transmitting end to carry out a precoding process, an equalization matrix W obtained in the step 4) is adopted at a receiving end to carry out an equalization process, and a demodulation matrix B obtained in the step 3) is adopted to carry out a demodulation process.
Compared with the prior art, the invention has the beneficial effects that:
the signal transceiving process of the invention is mainly optimized aiming at the differentiation part of the phase noise of each channel. In the MIMO system with multi-channel phase noise, the signal receiving and transmitting process and Gaussian white noise are subjected to combined suppression optimization according to the statistical characteristic of the phase noise, so that the system performance can be improved, and the communication capacity can be further improved. The technical scheme of the invention can obviously reduce the influence of phase noise and simultaneously realize better suppression of white noise.
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FIG. 1 is a schematic diagram of a MIMO communication system under the influence of multi-channel phase noise;
FIG. 2 is a schematic diagram of a simulation system;
FIG. 3 is a graph comparing MSE performance under SVD decomposition;
FIG. 4 is a graph comparing MSE performance under GMD decomposition.
Detailed Description
Fig. 1 is a schematic diagram of a MIMO communication system under the influence of multi-channel phase noise. At a transmitting end, introducing multi-channel phase noise into a radio frequency part after precoding P; at the receiving end, multi-channel phase noise is introduced prior to digital baseband processing including equalization and demodulation. If each path of the sending end or the receiving end can share a local oscillator, the phase noise of each path is the same, the phase noise can be compensated by the phase-locked loop and other existing phase correction modules, and the influence of the phase noise on the system performance is small. However, the driving force of each path is insufficient under the action of a common local oscillator, and the common local oscillator is not practical when the radio frequency channels are separated. In practical situations, the local oscillation signals of each channel can only be excited and generated under a common reference source, the phase noise from the reference source is a common part, and a differential part also occurs in the process of generating the local oscillation signals of each channel from the reference source. The signal transceiving process of the invention is mainly optimized aiming at the differentiation part of the phase noise of each channel.
The received signal model corresponding to FIG. 1 is
Figure BDA0002849195560000051
Here, use is made of
Figure BDA0002849195560000052
And
Figure BDA0002849195560000053
to represent the multi-channel phase noise of the receiving end and the transmitting end, the superscript T represents taking transposition, the Λ (-) represents constructing diagonal matrix by taking corresponding vector as diagonal element,
Figure BDA0002849195560000054
and is
Figure BDA0002849195560000055
The precoding matrix P is still unitary, and the total noise component z ═ j Λ (θ)r)HPx+jHΛ(θt) Px + n. In addition, the approximate relationship (a)1) Is obtained by exponential first-order Taylor expansion, and the approximate relation (a)2) Is obtained by ignoring the second order small quantity. In general, the phase noise, and in particular the differential part, is small, e.g. less than 1 °, (a)1) And (a)2) The method has high accuracy and negligible approximation error.
As can be seen from equation (5), the multi-channel phase noise is an exponential multiplicative noise, which can be converted into an additive noise having a certain correlation with the useful signal, and the additive noise and the gaussian white noise are regarded as a whole, so that how to design a novel signal transceiving method can be explored, and the influence of the multi-channel phase noise can be reduced. For convenience of subsequent description, the correlation matrix between the signals is expressed by equations (6) to (10). Wherein the content of the first and second substances,
Figure BDA0002849195560000056
representing and expecting; an indication of multiplication of corresponding elements of the matrix;
Figure BDA0002849195560000057
and
Figure BDA0002849195560000058
respectively representing the autocorrelation matrixes among the phase noises of all channels of the receiving and sending ends; the white Gaussian noise of each receiving channel is independent and has the power of
Figure BDA0002849195560000059
CxxAn autocorrelation matrix for the transmitted signal; cxrIs a cross-correlation matrix between the transmitted signal and the received signal; crxIs a cross-correlation matrix between the received signal and the transmitted signal; czzAn autocorrelation matrix that is the overall noise; crrIs a received signal autocorrelation matrix.
Figure BDA00028491955600000510
Figure BDA00028491955600000511
Figure BDA0002849195560000061
Figure BDA0002849195560000062
Figure BDA0002849195560000063
The invention designs a signal transceiving method capable of reducing the influence of multi-channel phase noise, namely, a precoding matrix P, an equalization matrix W and a demodulation matrix B shown in figure 1 are calculated based on formulas (6) to (10) under a corresponding signal model. For convenience of illustration, the demodulation matrix B is still an upper triangular matrix (note: the diagonal matrix is a special upper triangular matrix, and is collectively expressed as an upper three-solution matrix), but compared with R in the equations (3) and (4), diagonal elements are normalized, and normalization is achieved by adjusting the equalization process, so the equalization matrix W is not limited to a unitary matrix.
As in the signaling process shown in fig. 1, the demodulation error can be recorded without considering the decision error
e＝Bx-Wr (11)
The invention researches how to design a precoding matrix P, an equalization matrix W and a demodulation matrix B related to a signal receiving and transmitting process from the aspect of analyzing the autocorrelation characteristic of a demodulation error. By derivation, a demodulation error autocorrelation matrix C can be obtainedeeIs composed of
Figure BDA0002849195560000064
The optimal signal transceiving process is to ensure that the MSE is as small as possible, namely CeeThe diagonal elements of (a) are as small as possible. Formula (12) is derived fromeeIn two parts, it can be found that the diagonal elements of the first half must be greater than 0, while the diagonal elements of the second part are greater than or equal to 0, while the equalization matrix W is only associated with the second part, which can be made a zero matrix by optimally configuring it. Thus, the equalization matrix that minimizes the MSE satisfies
Figure BDA0002849195560000065
Accordingly, the demodulation error autocorrelation matrix is
Figure BDA0002849195560000066
The problem of achieving performance optimization is degenerated into how to design P and B. As shown in formula (9), CzzIs a known amount and can be directly usedTo pair
Figure BDA0002849195560000067
Performing Cholesky decomposition to obtain L, i.e
Figure BDA0002849195560000068
Then, SVD or GMD decomposition is performed on L to obtain the result shown in equation (15). Wherein the content of the first and second substances,
Figure BDA0002849195560000069
and
Figure BDA00028491955600000610
is a unitary matrix of the first phase,
Figure BDA0002849195560000071
for the upper triangular matrix after diagonal normalization, U is for implementation
Figure BDA0002849195560000072
A diagonal normalized diagonal matrix.
Figure BDA00028491955600000711
Further, substituting into formula (14) and formula (15), CeeCan be derived as the result shown in equation (16), wherein,
Figure BDA0002849195560000073
as shown in equation (16), may be set
Figure BDA0002849195560000074
Then
Figure BDA0002849195560000075
Due to B and
Figure BDA0002849195560000076
all are diagonal normalized upper triangular matrix, then
Figure BDA0002849195560000077
Also a diagonal normalized upper triangular matrix. Implementation CeeMinimizing diagonal elements requires
Figure BDA0002849195560000078
Is an identity matrix, i.e. has
Figure BDA0002849195560000079
MIMO signal receiving and transmitting are carried out according to the precoding matrix P, the equalization matrix W and the demodulation matrix B obtained in the above way, and C can be obtainedee＝U-2The off-diagonal element is 0, and the diagonal element corresponds to the mean square error MSE of the signal demodulation of each channel.
When SVD is performed on L, the demodulation matrix B is a diagonal matrix (actually the unit matrix I after normalization), CeeThe diagonal elements are different, namely the MSE of each channel is different; when GMD is performed on L, the demodulation matrix B is the upper three solution matrix (diagonal element normalization), CeeThe diagonal elements are the same, i.e., the MSE for each channel is the same. This is similar to the effect of the corresponding signal transceiving method under the conventional SVD and GMD decomposition based on the channel matrix H.
In order to compare the performance of the solution of the present invention with the prior art solution, an example of Matlab simulation is given here. The communication frequency point is set to 15GHz, as shown in fig. 2, the number of the receiving and transmitting antennas is N-4, the antennas are arranged in a square shape, the side length of the square is 3m, and the transmission distance is 10 Km. Thus, the antenna pitch is 0.3 rayleigh distance, and the channel response after power normalization is shown in equation (17). In addition, the standard deviation of the phase noise of each channel at the transmitting end and the receiving end in the simulation system is set to be 0.5 degrees.
Figure BDA00028491955600000710
Fig. 3 shows a comparison of MSE performance between the present invention and the prior art based on SVD decomposition under different Signal-to-Noise Ratio (SNR) conditions. Here, the MSE and average MSE performance of the worst of the four paths are given, respectively. It can be seen from the figure that the solution of the present invention is superior to the prior art solutions, both for the worst-path MSE and for the average MSE. When the SNR is lower, the influence of white noise is more obvious than phase noise, the MSE of the worst channel of the invention can be about 10dB lower than that of the prior art, and the average MSE is more than 2dB lower; when the SNR is higher, the influence of phase noise is more significant than that of white noise, the MSE difference of the worst path is above 2dB, and the average MSE difference is close to 1 dB. Therefore, the technical scheme of the invention can obviously reduce the influence of phase noise and simultaneously realize better suppression of white noise.
Fig. 4 shows performance results corresponding to fig. 3 based on GMD decomposition. In the prior art, if no phase noise exists, the MSE of each path under GMD decomposition is strictly the same theoretically, however, as shown in fig. 4, when the phase noise is considered, the MSE of the worst path and the average MSE have a large difference, which indicates that the MSE of each path can not be guaranteed to be the same in the prior art. By adopting the technical scheme of the invention, the worst path is strictly the same as the average MSE, and the paths can still adopt the same coding modulation mode. Similarly, the inventive solution of fig. 4 is superior to the prior art solution, especially when the SNR is large, the influence of phase noise is more prominent than white noise, and the MSE of the present invention has a gain of more than 5dB than the worst path of the prior art solution.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (4)

1. A signal receiving and transmitting method for reducing the influence of MIMO multi-channel phase noise is characterized by comprising the following steps:
1) obtaining a channel response matrix H and an autocorrelation matrix among phase noises of each channel of a receiving end and a transmitting end through parameter estimation
Figure FDA0002849195550000011
And
Figure FDA0002849195550000012
gaussian white noise power
Figure FDA0002849195550000013
Calculating to obtain the autocorrelation matrix C of the integral noise according to the following formulazz；
Figure FDA0002849195550000014
Wherein the content of the first and second substances,
Figure FDA0002849195550000015
representing an expectation, the corresponding elements of the matrix are multiplied, I is an identity matrix, the superscript H represents a conjugate transpose of the matrix, Λ (·) represents that a diagonal matrix is constructed by taking the corresponding vector as a diagonal element, and z is a total noise component;
2) by pairs
Figure FDA0002849195550000016
Performing Cholesky decomposition to obtain a key matrix L, wherein the formula is as follows:
Figure FDA0002849195550000017
3) obtaining a precoding matrix P and a demodulation matrix B by performing SVD or GMD decomposition on the key L, wherein the formula is as follows:
Figure FDA0002849195550000018
wherein the content of the first and second substances,
Figure FDA0002849195550000019
and
Figure FDA00028491955500000110
is a unitary matrix of the first phase,
Figure FDA00028491955500000111
for the upper triangular matrix after diagonal normalization, U is for implementation
Figure FDA00028491955500000112
A diagonal normalized diagonal matrix; is provided with
Figure FDA00028491955500000113
4) Calculating to obtain an equilibrium matrix W according to the following formula;
Figure FDA00028491955500000114
wherein the content of the first and second substances,
Figure FDA00028491955500000115
is a cross-correlation matrix between the transmitted signal and the received signal;
Figure FDA00028491955500000116
an autocorrelation matrix for the received signal;
5) and executing signal transceiving of the MIMO system according to the obtained precoding matrix P, the equalization matrix W and the demodulation matrix B.
2. The method of claim 1, wherein the MIMO system has the same number of transmit and receive antennas, both N, and the number of signal paths is N.
3. The method of claim 1, wherein the number of transmit/receive antennas of the MIMO system is different, and the corresponding number of effective signal paths is the smaller of the number of transmit antennas and the number of receive antennas.
4. The method for transceiving signals with reduced MIMO multi-channel phase noise impact according to claim 1, wherein the step 5) is specifically: in the process of receiving and transmitting signals of the MIMO system, a precoding matrix P obtained in the step 3) is adopted at a transmitting end to carry out a precoding process, an equalization matrix W obtained in the step 4) is adopted at a receiving end to carry out an equalization process, and a demodulation matrix B obtained in the step 3) is adopted to carry out a demodulation process.
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CN113315561A (en) * 2021-05-25 2021-08-27 之江实验室 Co-reference multi-channel phase noise suppression method in MIMO system
CN113315561B (en) * 2021-05-25 2022-04-08 之江实验室 Co-reference multi-channel phase noise suppression method in MIMO system
WO2022247379A1 (en) * 2021-05-25 2022-12-01 之江实验室 Co-reference multichannel phase noise suppression method in mimo system
US11716134B2 (en) 2021-05-25 2023-08-01 Zhejiang Lab Phase noise suppression method for a multiple-input multiple-output (MIMO) system with a plurality of co-reference channels

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