CN110635836A - A MMSE-PCA Channel Estimation Method for Millimeter-Wave Massive MIMO System Based on Beam Selection - Google Patents

A MMSE-PCA Channel Estimation Method for Millimeter-Wave Massive MIMO System Based on Beam Selection Download PDF

Info

Publication number
CN110635836A
CN110635836A CN201910865261.2A CN201910865261A CN110635836A CN 110635836 A CN110635836 A CN 110635836A CN 201910865261 A CN201910865261 A CN 201910865261A CN 110635836 A CN110635836 A CN 110635836A
Authority
CN
China
Prior art keywords
channel
matrix
mmse
pca
base station
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201910865261.2A
Other languages
Chinese (zh)
Inventor
廖勇
李皓雯
赵磊
王繁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University
Original Assignee
Chongqing University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing University filed Critical Chongqing University
Priority to CN201910865261.2A priority Critical patent/CN110635836A/en
Publication of CN110635836A publication Critical patent/CN110635836A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • 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
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • 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
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H04B7/046—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking physical layer constraints into account
    • H04B7/0465—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking physical layer constraints into account taking power constraints at power amplifier or emission constraints, e.g. constant modulus, into account
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/0848—Joint weighting
    • H04B7/0854—Joint weighting using error minimizing algorithms, e.g. minimum mean squared error [MMSE], "cross-correlation" or matrix inversion
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/02—Arrangements for detecting or preventing errors in the information received by diversity reception
    • H04L1/06—Arrangements for detecting or preventing errors in the information received by diversity reception using space diversity
    • H04L1/0618—Space-time coding
    • H04L1/0675—Space-time coding characterised by the signaling
    • H04L1/0681—Space-time coding characterised by the signaling adapting space time parameters, i.e. modifying the space time matrix
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/025—Channel estimation channel estimation algorithms using least-mean-square [LMS] method
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Power Engineering (AREA)
  • Radio Transmission System (AREA)

Abstract

本发明提出一种基于波束选择的毫米波大规模MIMO系统MMSE‑PCA(最小均方误差‑主成分分析)信道估计方法。该方法首先在基站侧以MMSE预编码为基准的幅度最大化标准选择波束信号。其次在接收端使用PCA信道压缩方法，将信道进行压缩降维，最后使用经典的最小二乘法(LS)进行信道估计。MMSE预编码可以减小系统中信道噪声和各个用户之间的干扰，减少基站使用射频链路数目，因此可降低系统的实现成本和能量损耗。同时，PCA利用原始高维数据的相关性，将高维的数据压缩到低维。该方法在降低系统复杂度基础上，达到有效优化系统传输性能和提高能量效率的目的。

The present invention proposes a MMSE-PCA (Minimum Mean Square Error-Principal Component Analysis) channel estimation method for a millimeter-wave massive MIMO system based on beam selection. The method first selects beam signals at the base station side based on the amplitude maximization criterion based on MMSE precoding. Secondly, the PCA channel compression method is used at the receiving end to compress the channel for dimension reduction, and finally the classical least squares (LS) method is used for channel estimation. MMSE precoding can reduce channel noise and interference between users in the system, reduce the number of radio frequency links used by the base station, and thus reduce the implementation cost and energy consumption of the system. At the same time, PCA utilizes the correlation of the original high-dimensional data to compress high-dimensional data into low-dimensional data. The method achieves the purpose of effectively optimizing the system transmission performance and improving the energy efficiency on the basis of reducing the system complexity.

Description

Millimeter wave large-scale MIMO system MMSE-PCA channel estimation method based on beam selection
The technical field is as follows:
the invention relates to the field of wireless communication, in particular to a method for estimating MMSE-PCA (minimum mean square error-principal component analysis) channels of a millimeter wave large-scale MIMO (multiple input multiple output) system based on beam selection.
Background art:
with the continuous development of modern electronic information technology, the mobile communication technology in China achieves high-efficiency achievements, and the 4G mobile network is vigorously promoted in China, so that the life experience and the production mode of people are greatly improved. Currently, the fifth generation mobile communication technology (5G) for 2020 is still in the beginning stage. Millimeter wave massive Multiple Input Multiple Output (MIMO) is a key technology for future 5G wireless communication, because it has wider bandwidth and higher spectral efficiency, and can significantly improve data rate. However, with the rapid increase of the number of mobile communication users and the wireless data transmission rate, the existing spectrum resources become crowded and cannot meet the requirement of 5G communication index, so the application research of the millimeter wave spectrum resources which are not completely developed in the future 5G communication is focused by domestic and foreign researchers. On one hand, the bandwidth of the millimeter wave can reach 10GHz, and abundant bandwidth resources can be provided for a communication system, and on the other hand, the size of the antenna corresponding to the millimeter wave is greatly reduced due to the fact that the size of the antenna in the wireless communication system is in direct proportion to the wavelength of the signal, so that the millimeter wave antenna is suitable for being deployed with a large number of antennas at a sending end and a receiving end, and accordingly higher antenna array gain is obtained. Therefore, the perfect combination of millimeter wave and massive MIMO technology will become a research hotspot in the current communication field.
However, it is not a simple task to implement millimeter wave massive MIMO in practical applications. One key challenge is that each antenna in a conventional MIMO system typically requires a dedicated Radio Frequency (RF) chain (including digital-to-analog converters, upconverters, etc.). The baseband part generally adopts a digital pre-coding technology to pre-process the transmitted signals, and the pre-processed signals can greatly reduce the interference in the system so as to greatly improve the system performance. However, in the all-digital precoding scheme, each antenna corresponds to one RF link, and as the number of antennas and the number of users of the base station increase, the number of RF links required by the system increases, which increases the system implementation cost and causes huge energy loss. This results in hardware cost and power consumption of the millimeter wave massive MIMO system being burdensome because the number of antennas becomes huge (e.g., 256 antennas) and the power consumption of the RF chains is high (e.g., about 250mW per RF chain at millimeter wave frequencies).
To reduce the number of RF chains required, millimeter-wave massive MIMO systems have recently been proposed that employ lensed line (ULA) antenna arrays, an electromagnetic lens with energy focusing capability and an antenna array matched to elements located at the focal plane of the lens. By using a ULA antenna array, spatial channels can be converted to beam spatial channels by focusing signals from different directions on different antennas. Since scattering at millimeter wave frequencies is not abundant, the number of active paths in millimeter wave communications is very limited, occupying only a small number of beams. Therefore, the millimeter wave beam space channel is sparse, and a small number of main beams can be selected according to the sparsity. In a millimeter wave large-scale MIMO system, a base station end is configured with a large number of antenna array elements, and signals are concentrated in a block area space by utilizing a beam forming technology, so that a millimeter wave large-scale MIMO path has certain sparse characteristics. With this feature, channels are processed using compressed sensing which has been studied more extensively in recent years. Firstly, a measurement matrix of a millimeter wave system is obtained by using a hybrid precoder according to the research of a related compressed sensing theory, and then the channel estimation problem of the millimeter wave system can be researched as a typical sparse signal recovery problem.
In summary, it is a challenge in the channel estimation research of the millimeter wave massive MIMO system at present to solve the problem of high energy consumption and to achieve the purpose of effectively improving the system performance and energy efficiency on the basis of reducing the system complexity.
The invention content is as follows:
the invention aims to at least solve the technical problems in the prior art, and particularly provides a method for estimating MMSE-PCA (minimum mean square error-principal component analysis) channels of a millimeter wave massive MIMO (multiple input multiple output) system based on beam selection.
In order to achieve the above object, the present invention provides a method for estimating MMSE-PCA of a millimeter wave massive MIMO system based on beam selection, which is characterized by comprising:
s1, selecting beam signals by adopting an amplitude maximization (MM) standard with minimum mean square error precoding as a reference at the base station side, and introducing a minimum mean square error linear precoding technology to weaken the influence of noise and interference among users;
s2, adopting a Time Division Duplex (TDD) large-scale MIMO system, and obtaining Channel State Information (CSI) by Least Square (LS) channel estimation in an uplink according to channel reciprocity in the TDD system;
s3, a Saleh-Vallenzuela channel model is adopted to reflect the channel sparse characteristic, and a Principal Component Analysis (PCA) channel compression method is used at a receiving end to map CSI from a high dimension to a low dimension for reducing the characteristic dimension;
s4, the receiving end compresses the channel to reduce dimension, and then adopts LS to estimate the channel.
The method for estimating MMSE-PCA of a millimeter wave massive MIMO system based on beam selection is characterized in that S1 includes:
the base station side adopts MM standard with MMSE precoding as reference to select beam signals, MMSE linear precoding technology is introduced on the basis of Zero Forcing (ZF) algorithm, and MMSE precoding matrix expression is as follows:
Figure BDA0002201077640000021
wherein beta represents a power control factor, | · |. non-woven phosphor22 norm is obtained, and E (-) is expected; in order to achieve simple calculation, the optimization problem of the MMSE precoding algorithm can be regarded as the problem of solving the minimum mean square error of a received signal and a transmitted signal under a certain power constraint condition; on the basis, an objective function is established:
Figure BDA0002201077640000031
wherein P represents the maximum transmit power of the signal; according to the MMSE criterion, the obtained precoding matrix is as follows:
Figure BDA0002201077640000032
wherein σ2Work as noiseThe rate, power control factor β is:
Figure BDA0002201077640000033
wherein Tr (H) represents the trace of the matrix (H)-1Representing the inverse of the matrix, HHRepresenting the conjugate transpose of the matrix.
The MMSE-PCA channel estimation method for the beam selection MMSE is characterized in that the S2 comprises the following steps:
the antenna matrix U expression is:
Figure BDA0002201077640000034
in the formula:
Figure BDA0002201077640000035
n denotes an attitude, and a system model received signal of massive MIMO based on a 3D beam space can be expressed as:
in the formula
Figure BDA0002201077640000037
For the received signal vector of the beam space, after converting the channel vector and the channel vector into the channel vector of the beam space, the conversion mode is as follows:
Figure BDA0002201077640000038
Figure BDA0002201077640000039
comprises hkCan be used to estimate the entire CSI; then the channel matrix of the beam spaceCan be defined as:
Figure BDA00022010776400000311
Figure BDA00022010776400000312
representing a downlink beam space channel matrix;
system model as shown in fig. 1, the uplink obtains CSI through LS channel estimation, in which each user needs to send orthogonal pilot sequence ψ to the base station at time QmAssuming that the Q time is divided into M blocks each consisting of K times, the uplink signal vector received at the base station of the M-th block according to channel reciprocity in the TDD system
Figure BDA0002201077640000041
Can be expressed as:
Figure BDA0002201077640000042
by adaptively selecting a network, the base station uses an analog combiner W with dimension K NmTo be combined outAnd obtaining a sampling signal R with dimension K multiplied by K in baseband sampling through a radio frequency chainmWherein R ismThe expression is as follows:
Figure BDA0002201077640000044
finally, the signals with reduced dimensionality and the orthogonal pilot frequency matrix are combinedMultiplying to obtain beam space channel
Figure BDA0002201077640000046
Is detected by the detection matrix Zm，
Figure BDA0002201077640000047
WhereinAnd the effective noise matrix is represented, and in a TDD system, the CSI obtained according to uplink estimation can be used as the CSI of a downlink channel due to channel reciprocity.
The method for estimating MMSE-PCA of a millimeter wave massive MIMO system based on beam selection is characterized in that S3 includes:
because the number of effective paths in millimeter wave communication is limited, H has the characteristic of a sparse structure, and a millimeter wave Saleh-Vallenzuela channel model is shown in FIG. 2;
then the reduced dimensionality signal of the massive MIMO system based on beam space can be expressed as:
Figure BDA0002201077640000049
in the formula,
Figure BDA00022010776400000410
b denotes the set of selected beams, PrA reduced dimension precoding matrix; in order to achieve near-optimal performance, the base station needs to obtain a 3D wave-imperial spatial channel with a limited number of radio frequency chains, and in order to guarantee spatial multiplexing gain for K users, the minimum number of required radio frequency chains should be NRFK, so consider the number of radio frequency chains to be NRFK, and without loss of generality;
after signals are precoded through MM-MMSE to form beams and are sent out, a receiving end receives the precoded signals, and then dimension reduction is carried out on CSI through PCA;
in the CSI algorithm based on low-complexity PCA, firstly, eigenvalue decomposition is carried out on a covariance matrix to obtain CHIs represented as:
Figure BDA0002201077640000051
wherein,
Figure BDA0002201077640000052
is a diagonal matrix, whose diagonal elements are eigenvalues of a covariance matrix,
Figure BDA0002201077640000053
is a covariance matrix CHThe feature vector of (2);
then, the eigenvalues are arranged from big to small, and eigenvectors corresponding to the first eigenvalues with eigenvalue contribution rates exceeding a threshold gamma are selected to form a compression matrix
Figure BDA0002201077640000054
Second using the compression matrix
Figure BDA0002201077640000055
A high-dimensional downlink channel information matrix HrCompressed into a low dimensional space, represented as:
Figure BDA0002201077640000056
whereinRepresenting the channel matrix after dimensionality reduction;
the feedback quantity compression ratio based on the PCA algorithm is as follows:
rPCA＝l(Nr+Nt)/(Nr×Nt)
non-codebook feedback is adopted, the feedback quantity is defined by a compression ratio, and the smaller the compression ratio is, the lower the required feedback overhead is;
finally, the user will
Figure BDA0002201077640000058
And compressing the matrix
Figure BDA0002201077640000059
Feeding back the feedback information to a base station end, and recovering an original channel by using the same compression matrix after the base station end receives the feedback information, wherein the original channel is represented as the reference channel;
Figure BDA00022010776400000510
wherein
Figure BDA00022010776400000511
Representing the recovery value of the channel.
The MMSE-PCA channel estimation method based on beam selection is characterized in that the S4 comprises the following steps:
the receiving end estimates the CSI by an LS algorithm;
let the channel matrix be H and the received signal matrix beThe transmit signal matrix is X, and its estimate can be expressed as:
to obtain a specific expression, the partial derivatives of the above formula are solved, and the partial derivatives are made to be 0, so that:
to solve this, the channel estimate is obtained as:
Figure BDA0002201077640000063
if X is a non-singular matrix, the Mueller-Penrose inverse of X may be used.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
A3D beam space system model adopting Time Division Duplex (TDD), a combined beam forming technology and a millimeter wave massive MIMO system MMSE-PCA (minimum mean square error-principal component analysis) channel estimation algorithm based on beam selection are provided. The method can finally achieve the purpose of improving the problem that multi-user interference is more serious and the like which are not beneficial to channel estimation in a large-scale MIMO system, improve the information transmission efficiency of the channel on the basis of reducing the complexity of the system, and optimize the performance of the system.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a millimeter wave Saleh-Vallenzuela channel model;
FIG. 2 is a 3D beam space massive MIMO system model;
fig. 3 is an overall flow chart of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
According to the invention, through the method of MMSE-PCA channel estimation of the millimeter wave large-scale MIMO system based on beam selection, the rate and the performance of the millimeter wave beam selection scheme can be effectively improved, and the beam signals are selected by using the amplitude maximization standard taking MMSE precoding as the reference at the base station side, so that the multi-user interference is reduced. And then, a PCA channel compression method is used at a receiving end, the channel is compressed and dimension reduced, the complexity of channel estimation is reduced, and finally, the channel estimation is carried out by using a classical least square method.
The invention is explained in detail with reference to fig. 3, which mainly comprises the following steps:
step 1: starting;
step 2: selecting a beam signal by using an amplitude maximization criterion with MMSE precoding as a reference;
the method adopts amplitude maximization standard based on MMSE precoding to select beam signals, and introduces MMSE linear precoding technology on the basis of ZF algorithm, thereby more effectively reducing plane noise and interference between users. The MMSE precoding matrix expression is:
Figure BDA0002201077640000071
wherein beta represents a power control factor, | · |. non-woven phosphor2Representing 2 norm, and E (·) representing expectation. For simple calculation, the optimization problem of the MMSE precoding algorithm can be regarded as a problem of solving the minimum mean square error of a received signal and a transmitted signal under a certain power constraint condition. On the basis, an objective function is established:
Figure BDA0002201077640000072
where P represents the maximum transmit power of the signal. According to the MMSE criterion, the obtained precoding matrix is as follows:
Figure BDA0002201077640000073
wherein σ2Is the noise power. The power control factor β is:
Figure BDA0002201077640000081
and step 3: the receiving end uses a PCA channel compression method to compress and reduce the dimension of the channel;
and the compressed channel estimation algorithm of PCA is adopted, so that the dimensionality of a channel matrix is reduced, and the calculation complexity of channel estimation is reduced. In the CSI feedback algorithm based on low-complexity PCA, firstly, eigenvalue decomposition is carried out on a covariance matrix to obtain CHIs represented as:
Figure BDA0002201077640000082
wherein,
Figure BDA0002201077640000083
is a diagonal matrix, whose diagonal elements are eigenvalues of a covariance matrix,
Figure BDA0002201077640000084
is a covariance matrix CHThe feature vector of (2).
Then, the eigenvalues are arranged from big to small, and eigenvectors corresponding to the first eigenvalues with eigenvalue contribution rates exceeding a threshold gamma are selected to form a compression matrix
Figure BDA0002201077640000085
Second using the compression matrix
Figure BDA0002201077640000086
A high-dimensional downlink channel information matrix HrCompressed into a low dimensional space, represented as:
wherein
Figure BDA0002201077640000088
Representing the reduced channel matrix.
The feedback quantity compression ratio based on the PCA algorithm is as follows:
rPCA＝l(Nr+Nt)/(Nr×Nt)
the project adopts non-codebook feedback, the feedback quantity is defined by the compression ratio, and the smaller the compression ratio is, the lower the required feedback overhead is.
Finally, the user will
Figure BDA0002201077640000089
And compressing the matrix
Figure BDA00022010776400000810
And feeding back the feedback information to the base station end, and recovering the original channel by using the same compression matrix after the base station end receives the feedback information, wherein the compression matrix is expressed as follows:
Figure BDA00022010776400000811
wherein
Figure BDA00022010776400000812
Representing the recovery value of the channel.
And 4, step 4: estimating channel state information by using an LS algorithm;
let the channel matrix be H and the received signal matrix be
Figure BDA00022010776400000813
The transmit signal matrix is X, and its estimate can be expressed as:
Figure BDA00022010776400000814
to obtain the LS channel estimation result, the above formula is biased to 0
Figure BDA0002201077640000091
To solve this, the channel estimate is obtained as:
Figure BDA0002201077640000092
if X is a non-singular matrix, the Mueller-Penrose inverse of X may be used.
And 5: and (6) ending.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (5)

1. A millimeter wave massive MIMO system MMSE-PCA channel estimation method based on beam selection is characterized by comprising the following steps:
s1, selecting beam signals by adopting an amplitude maximization (MM) standard with minimum mean square error precoding as a reference at the base station side, and introducing a minimum mean square error linear precoding technology to weaken the influence of noise and interference among users;
s2, adopting a Time Division Duplex (TDD) large-scale MIMO system, and obtaining Channel State Information (CSI) by Least Square (LS) channel estimation in an uplink according to channel reciprocity in the TDD system;
s3, a Saleh-Vallenzuela channel model is adopted to reflect the channel sparse characteristic, and a Principal Component Analysis (PCA) channel compression method is used at a receiving end to map CSI from a high dimension to a low dimension for reducing the characteristic dimension;
s4, the receiving end compresses the channel to reduce dimension, and then adopts LS to estimate the channel.
2. The method for MMSE-PCA channel estimation in a MMSE massive MIMO system based on beam selection as claimed in claim 1, wherein the S1 comprises:
the base station side adopts MM standard with MMSE precoding as reference to select beam signals, MMSE linear precoding technology is introduced on the basis of Zero Forcing (ZF) algorithm, and MMSE precoding matrix expression is as follows:
wherein beta represents a power control factor, | · |. non-woven phosphor22 norm is obtained, and E (-) is expected; in order to achieve simple calculation, the optimization problem of the MMSE precoding algorithm can be regarded as the problem of solving the minimum mean square error of a received signal and a transmitted signal under a certain power constraint condition; on the basis, an objective function is established:
Figure FDA0002201077630000012
wherein P represents the maximum transmit power of the signal; according to the MMSE criterion, the obtained precoding matrix is as follows:
wherein σ2For noise power, the power control factor β is:
Figure FDA0002201077630000014
wherein Tr (H) represents the trace of the matrix (H)-1Representing the inverse of the matrix, HHRepresenting the conjugate transpose of the matrix.
3. The MMSE-PCA channel estimation method for the beam-selection MMSE massive MIMO system of claim 1, wherein the S2 comprises:
the antenna matrix U expression is:
Figure FDA0002201077630000021
in the formula:
Figure FDA0002201077630000022
representing the attitude, the system model received signal of massive MIMO based on 3D beam space can be represented as:
Figure FDA0002201077630000023
in the formula
Figure FDA0002201077630000024
For the received signal vector of the beam space, after converting the channel vector and the channel vector into the channel vector of the beam space, the conversion mode is as follows:
Figure FDA0002201077630000025
Figure FDA0002201077630000026
comprises hkCan be used to estimate the entire CSI; then the beamSpatial channel matrix
Figure FDA0002201077630000027
Can be defined as:
Figure FDA0002201077630000028
Figure FDA0002201077630000029
representing a downlink beam space channel matrix;
the uplink obtains the CSI by LS channel estimation, in which each user needs to send orthogonal pilot sequence psi to the base station at time QmAssuming that the Q time is divided into M blocks each consisting of K times, the uplink signal vector received at the base station of the M-th block according to channel reciprocity in the TDD system
Figure FDA00022010776300000210
Can be expressed as:
Figure FDA00022010776300000211
by adaptively selecting a network, the base station uses an analog combiner W with dimension K NmTo be combined out
Figure FDA00022010776300000212
And obtaining a sampling signal R with dimension K multiplied by K in baseband sampling through a radio frequency chainmWherein R ismThe expression is as follows:
Figure FDA00022010776300000213
finally, the signals with reduced dimensionality and the orthogonal pilot frequency matrix are combined
Figure FDA00022010776300000214
Multiplying to obtain beam space channel
Figure FDA00022010776300000215
Is detected by the detection matrix Zm，
Figure FDA0002201077630000031
Wherein
Figure FDA0002201077630000032
And the effective noise matrix is represented, and in a TDD system, the CSI obtained according to uplink estimation can be used as the CSI of a downlink channel due to channel reciprocity.
4. The method for MMSE-PCA channel estimation in a MMSE massive MIMO system based on beam selection as claimed in claim 1, wherein the S3 comprises:
because the number of effective paths in millimeter wave communication is limited, H has the characteristic of a sparse structure;
then the reduced dimensionality signal of the massive MIMO system based on beam space can be expressed as:
in the formula,b denotes the set of selected beams, PrA reduced dimension precoding matrix; in order to achieve near-optimal performance, the base station needs to obtain a 3D wave-imperial spatial channel with a limited number of radio frequency chains, and in order to guarantee spatial multiplexing gain for K users, the minimum number of required radio frequency chains should be NRFK, so consider the number of radio frequency chains to be NRFK, and without loss of generality;
after signals are precoded through MM-MMSE to form beams and are sent out, a receiving end receives the precoded signals, and then dimension reduction is carried out on CSI through PCA;
in the CSI algorithm based on low-complexity PCA, firstly, eigenvalue decomposition is carried out on a covariance matrix to obtain CHIs represented as:
Figure FDA0002201077630000035
wherein,
Figure FDA0002201077630000036
is a diagonal matrix, whose diagonal elements are eigenvalues of a covariance matrix,
Figure FDA0002201077630000037
is a covariance matrix CHThe feature vector of (2);
then, the eigenvalues are arranged from big to small, and eigenvectors corresponding to the first eigenvalues with eigenvalue contribution rates exceeding a threshold gamma are selected to form a compression matrix
Figure FDA0002201077630000038
Second using the compression matrix
Figure FDA0002201077630000039
A high-dimensional downlink channel information matrix HrCompressed into a low dimensional space, represented as:
Figure FDA00022010776300000310
whereinRepresenting the channel matrix after dimensionality reduction;
the feedback quantity compression ratio based on the PCA algorithm is as follows:
rPCA＝l(Nr+Nt)/(Nr×Nt)
non-codebook feedback is adopted, the feedback quantity is defined by a compression ratio, and the smaller the compression ratio is, the lower the required feedback overhead is;
finally, the user will
Figure FDA0002201077630000041
And compressing the matrix
Figure FDA0002201077630000042
Feeding back the feedback information to a base station end, and recovering an original channel by using the same compression matrix after the base station end receives the feedback information, wherein the original channel is represented as the reference channel;
Figure FDA0002201077630000043
wherein
Figure FDA0002201077630000044
Representing the recovery value of the channel.
5. The MMSE-PCA channel estimation method for the MMSE-MMSE system based on the beam selection as claimed in claim 1, wherein the S4 comprises:
the receiving end estimates the CSI by an LS algorithm;
let the channel matrix be H and the received signal matrix be
Figure FDA0002201077630000045
The transmit signal matrix is X, and its estimate can be expressed as:
Figure FDA0002201077630000046
to obtain a specific expression, the partial derivatives of the above formula are solved, and the partial derivatives are made to be 0, so that:
Figure FDA0002201077630000047
to solve this, the channel estimate is obtained as:
Figure FDA0002201077630000048
if X is a non-singular matrix, the Mueller-Penrose inverse of X may be used.
CN201910865261.2A 2019-09-12 2019-09-12 A MMSE-PCA Channel Estimation Method for Millimeter-Wave Massive MIMO System Based on Beam Selection Pending CN110635836A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910865261.2A CN110635836A (en) 2019-09-12 2019-09-12 A MMSE-PCA Channel Estimation Method for Millimeter-Wave Massive MIMO System Based on Beam Selection

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910865261.2A CN110635836A (en) 2019-09-12 2019-09-12 A MMSE-PCA Channel Estimation Method for Millimeter-Wave Massive MIMO System Based on Beam Selection

Publications (1)

Publication Number Publication Date
CN110635836A true CN110635836A (en) 2019-12-31

Family

ID=68971142

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910865261.2A Pending CN110635836A (en) 2019-09-12 2019-09-12 A MMSE-PCA Channel Estimation Method for Millimeter-Wave Massive MIMO System Based on Beam Selection

Country Status (1)

Country Link
CN (1) CN110635836A (en)

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111277313A (en) * 2020-01-20 2020-06-12 东南大学 Bipartite graph-based large-scale MIMO beam selection and transmission method for cellular internet of vehicles
CN111431567A (en) * 2020-03-30 2020-07-17 内蒙古大学 A mmWave Massive Beam Spatial MIMO System
CN111654456A (en) * 2020-06-09 2020-09-11 江南大学 Method and device for millimeter-wave massive MIMO angular domain channel estimation based on dimensionality reduction decomposition
CN111786708A (en) * 2020-07-02 2020-10-16 电子科技大学 Joint channel information acquisition method for massive MIMO system
CN111917447A (en) * 2020-08-12 2020-11-10 电子科技大学 A low frequency assisted hybrid precoding design method based on beam selection
CN112564754A (en) * 2020-12-01 2021-03-26 哈尔滨工业大学 Wave beam selection method based on self-adaptive cross entropy under millimeter wave Massive MIMO system
CN113179231A (en) * 2021-04-15 2021-07-27 内蒙古大学 Beam space channel estimation method in millimeter wave large-scale MIMO system
CN113824477A (en) * 2021-10-09 2021-12-21 北京邮电大学 Discrete lens antenna array assisted multi-user large-scale MIMO optimization method
CN113839695A (en) * 2021-09-16 2021-12-24 东南大学 FDD Massive MIMO and Rate Optimal Statistical Precoding Method and Device
CN115314086A (en) * 2022-06-23 2022-11-08 厦门大学 Precoding method, device, medium and equipment of communication perception integrated system
CN115460616A (en) * 2022-06-17 2022-12-09 东南大学 A Genetic Algorithm Based User Grouping Method for Millimeter Wave Massive MIMO System
CN115694571A (en) * 2022-10-31 2023-02-03 西安科技大学 A signal detection method and device based on deep learning in a massive MIMO system
CN116016052A (en) * 2023-01-04 2023-04-25 西南交通大学 A Channel Estimation Method for Millimeter Wave Massive MIMO Systems
CN116367318A (en) * 2022-12-30 2023-06-30 国电南瑞南京控制系统有限公司 Massive MIMO Terminal Scheduling Method Based on Combined Multi-Arm Gambler
CN116636155A (en) * 2020-12-16 2023-08-22 大陆汽车科技有限公司 Transceiver method between receiver (Rx) and transmitter (Tx) in overloaded communication channel

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102177671A (en) * 2008-10-10 2011-09-07 高通股份有限公司 Method and device for channel feedback through multiple description coding in wireless communication system
CN103139117A (en) * 2007-03-16 2013-06-05 飞思卡尔半导体公司 Generalized reference signaling scheme for MU-MIMO using arbitrarily precoded reference signals
CN104603853A (en) * 2012-05-04 2015-05-06 李尔登公司 Systems and methods for handling the Doppler effect in a distributed input-distributed output wireless system
CN107465436A (en) * 2017-07-04 2017-12-12 西安电子科技大学 The low-complexity base stations system of selection of the extensive mimo system of millimeter wave frequency band
CN107483091A (en) * 2017-07-06 2017-12-15 重庆邮电大学 A Channel Information Feedback Algorithm for FDD Massive MIMO-OFDM System
CN108198558A (en) * 2017-12-28 2018-06-22 电子科技大学 A kind of audio recognition method based on CSI data
CN108650003A (en) * 2018-04-17 2018-10-12 中国人民解放军陆军工程大学 Hybrid transmission method with joint Doppler compensation in massive MIMO high-speed mobile scenarios
WO2019041470A1 (en) * 2017-08-31 2019-03-07 东南大学 Large-scale mimo robust precoding transmission method
CN109617585A (en) * 2019-01-18 2019-04-12 杭州电子科技大学 Hybrid precoding method based on partial connection in mmWave massive MIMO
CN109743268A (en) * 2018-12-06 2019-05-10 东南大学 Millimeter wave channel estimation and compression method based on deep neural network
US20190229791A1 (en) * 2018-01-19 2019-07-25 Lenovo (Singapore) Pte. Ltd. Channel compression matrix parameters
WO2019157230A1 (en) * 2018-02-08 2019-08-15 Cohere Technologies, Inc. Aspects of channel estimation for orthogonal time frequency space modulation for wireless communications
CN110212957A (en) * 2019-05-27 2019-09-06 广西大学 A kind of MU-MIMO system user scheduling method based on letter leakage noise ratio

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103139117A (en) * 2007-03-16 2013-06-05 飞思卡尔半导体公司 Generalized reference signaling scheme for MU-MIMO using arbitrarily precoded reference signals
CN102177670A (en) * 2008-10-10 2011-09-07 高通股份有限公司 Method and device for channel feedback in wireless communication system
CN102177671A (en) * 2008-10-10 2011-09-07 高通股份有限公司 Method and device for channel feedback through multiple description coding in wireless communication system
CN104603853A (en) * 2012-05-04 2015-05-06 李尔登公司 Systems and methods for handling the Doppler effect in a distributed input-distributed output wireless system
CN107465436A (en) * 2017-07-04 2017-12-12 西安电子科技大学 The low-complexity base stations system of selection of the extensive mimo system of millimeter wave frequency band
CN107483091A (en) * 2017-07-06 2017-12-15 重庆邮电大学 A Channel Information Feedback Algorithm for FDD Massive MIMO-OFDM System
WO2019041470A1 (en) * 2017-08-31 2019-03-07 东南大学 Large-scale mimo robust precoding transmission method
CN108198558A (en) * 2017-12-28 2018-06-22 电子科技大学 A kind of audio recognition method based on CSI data
US20190229791A1 (en) * 2018-01-19 2019-07-25 Lenovo (Singapore) Pte. Ltd. Channel compression matrix parameters
WO2019157230A1 (en) * 2018-02-08 2019-08-15 Cohere Technologies, Inc. Aspects of channel estimation for orthogonal time frequency space modulation for wireless communications
CN108650003A (en) * 2018-04-17 2018-10-12 中国人民解放军陆军工程大学 Hybrid transmission method with joint Doppler compensation in massive MIMO high-speed mobile scenarios
CN109743268A (en) * 2018-12-06 2019-05-10 东南大学 Millimeter wave channel estimation and compression method based on deep neural network
CN109617585A (en) * 2019-01-18 2019-04-12 杭州电子科技大学 Hybrid precoding method based on partial connection in mmWave massive MIMO
CN110212957A (en) * 2019-05-27 2019-09-06 广西大学 A kind of MU-MIMO system user scheduling method based on letter leakage noise ratio

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
KENTARO NISHIMORI: "Multi-beam Massive MIMO Using Analog Beamforming and DBF Based Blind Algorithm", 《2015 INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION (ISAP)》 *
廖勇等: "高速移动环境下基于动态CSI 的MIMO 系统", 《电子学报》 *
张凯: "移动通信系统中的大规模MIMO技术研究", 《中国博士学位论文全文数据库-信息科技辑》 *

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111277313A (en) * 2020-01-20 2020-06-12 东南大学 Bipartite graph-based large-scale MIMO beam selection and transmission method for cellular internet of vehicles
CN111277313B (en) * 2020-01-20 2022-07-29 东南大学 Bipartite graph-based large-scale MIMO beam selection and transmission method for cellular internet of vehicles
CN111431567A (en) * 2020-03-30 2020-07-17 内蒙古大学 A mmWave Massive Beam Spatial MIMO System
CN111654456A (en) * 2020-06-09 2020-09-11 江南大学 Method and device for millimeter-wave massive MIMO angular domain channel estimation based on dimensionality reduction decomposition
CN111654456B (en) * 2020-06-09 2021-10-19 江南大学 Millimeter wave large-scale MIMO angular domain channel estimation method and device based on dimension reduction decomposition
CN111786708B (en) * 2020-07-02 2022-06-07 电子科技大学 Joint channel information acquisition method of large-scale MIMO system
CN111786708A (en) * 2020-07-02 2020-10-16 电子科技大学 Joint channel information acquisition method for massive MIMO system
CN111917447A (en) * 2020-08-12 2020-11-10 电子科技大学 A low frequency assisted hybrid precoding design method based on beam selection
CN111917447B (en) * 2020-08-12 2021-12-10 电子科技大学 A low frequency assisted hybrid precoding design method based on beam selection
CN112564754A (en) * 2020-12-01 2021-03-26 哈尔滨工业大学 Wave beam selection method based on self-adaptive cross entropy under millimeter wave Massive MIMO system
CN112564754B (en) * 2020-12-01 2021-09-28 哈尔滨工业大学 Wave beam selection method based on self-adaptive cross entropy under millimeter wave Massive MIMO system
CN116636155A (en) * 2020-12-16 2023-08-22 大陆汽车科技有限公司 Transceiver method between receiver (Rx) and transmitter (Tx) in overloaded communication channel
CN113179231A (en) * 2021-04-15 2021-07-27 内蒙古大学 Beam space channel estimation method in millimeter wave large-scale MIMO system
CN113839695B (en) * 2021-09-16 2022-06-21 东南大学 FDD Massive MIMO and Rate Optimal Statistical Precoding Method and Device
CN113839695A (en) * 2021-09-16 2021-12-24 东南大学 FDD Massive MIMO and Rate Optimal Statistical Precoding Method and Device
CN113824477B (en) * 2021-10-09 2023-04-28 北京邮电大学 Multi-user large-scale MIMO optimization method assisted by discrete lens antenna array
CN113824477A (en) * 2021-10-09 2021-12-21 北京邮电大学 Discrete lens antenna array assisted multi-user large-scale MIMO optimization method
CN115460616A (en) * 2022-06-17 2022-12-09 东南大学 A Genetic Algorithm Based User Grouping Method for Millimeter Wave Massive MIMO System
CN115314086A (en) * 2022-06-23 2022-11-08 厦门大学 Precoding method, device, medium and equipment of communication perception integrated system
CN115314086B (en) * 2022-06-23 2023-11-03 厦门大学 Precoding method, device, medium and equipment of communication perception integrated system
CN115694571A (en) * 2022-10-31 2023-02-03 西安科技大学 A signal detection method and device based on deep learning in a massive MIMO system
CN116367318A (en) * 2022-12-30 2023-06-30 国电南瑞南京控制系统有限公司 Massive MIMO Terminal Scheduling Method Based on Combined Multi-Arm Gambler
CN116016052A (en) * 2023-01-04 2023-04-25 西南交通大学 A Channel Estimation Method for Millimeter Wave Massive MIMO Systems
CN116016052B (en) * 2023-01-04 2024-05-07 西南交通大学 A channel estimation method for millimeter wave massive MIMO system

Similar Documents

Publication Publication Date Title
CN110635836A (en) A MMSE-PCA Channel Estimation Method for Millimeter-Wave Massive MIMO System Based on Beam Selection
CN110808765B (en) Power distribution method for optimizing spectrum efficiency of large-scale MIMO system based on incomplete channel information
CN107046434B (en) Large-scale MIMO system analog-digital mixed precoding method
CN108880774B (en) Frequency division duplex multi-user large-scale multi-antenna system and its downlink pilot signal length design method
CN106603130B (en) A Digital-Analog Hybrid Precoding Method in Massive MIMO System
CN110048752B (en) Mixed precoding method in millimeter wave large-scale MIMO
CN101378280B (en) Multi-input multi-output system based on antenna selection and signal processing method thereof
US8412242B2 (en) Method and apparatus for sending and receiving channel state information in multiple-input multiple-output network wireless communication systems
CN109617585A (en) Hybrid precoding method based on partial connection in mmWave massive MIMO
JP5666581B2 (en) Precoding method for transmitter of MU-MIMO communication system
CN101867462A (en) A multi-base station cooperative linear precoding method based on the minimum total bit error rate
CN101159462A (en) A limited feedback precoding interference suppression method in multi-antenna multi-cell system
CN102983935A (en) Precoding and predecoding method and transmitter and mobile terminal based on interference aligning
CN103780356B (en) A kind of method for designing of the two-stage precoding of cognitive MIMO communication system
CN105049100B (en) Double-layer precoding method for multi-cell MIMO system
CN106982087B (en) A communication method for a multiple-input multiple-output system
CN114389658A (en) Uplink power optimization method of zero-forcing reception cellular large-scale MIMO (multiple input multiple output) system
CN105743559B (en) A kind of Massive MIMO mixed-beam is formed and Space Time Coding multiuser downstream transmission method
CN107171702A (en) Extensive mimo channel feedback method based on PCA evolution
CN103873197B (en) The 3D MIMO Limited Feedback overhead reduction methods that spatial coherence is combined with sub-clustering
my Al-Nimrat et al. An efficient channel estimation scheme for mmwave massive MIMO systems
CN109361434B (en) Millimeter-wave MIMO hybrid precoding method for base station cooperative transmission
KR102230659B1 (en) Distributed array massive mimo system and method for operating distributed array massive mimo system
CN108683441B (en) Multi-User Beamforming Method in Hybrid Precoding
CN107579762B (en) Multi-cell cooperative precoding method based on quantification and channel information statistics

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20191231