CN106357309A - Method of large scale MIMO linear iterative detection under non-ideal channel - Google Patents

Method of large scale MIMO linear iterative detection under non-ideal channel Download PDF

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CN106357309A
CN106357309A CN201610669854.8A CN201610669854A CN106357309A CN 106357309 A CN106357309 A CN 106357309A CN 201610669854 A CN201610669854 A CN 201610669854A CN 106357309 A CN106357309 A CN 106357309A
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matrix
detection
communication channel
threshold value
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CN106357309B (en
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张川
薛烨
尤肖虎
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Southeast University
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    • 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
    • H04B7/08—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
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03891—Spatial equalizers
    • H04L25/03898—Spatial equalizers codebook-based design
    • H04L25/0391—Spatial equalizers codebook-based design construction details of matrices

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

Abstract

The present invention discloses a method of large-scale MIMO linear iterative detection under non-ideal channel. The method utilizes the incomplete Cholesky decomposition as the preprocessing part of the conjugate gradient iteration, combining the advantages of the two, so that the problem of reduced rate of convergence in the traditional conjugate gradient algorithm under non-ideal channel or in a expanded system.Furthermore, a dynamic threshold based on channel correlation coefficient and system scale is proposed as an important parameter of preprocessing, which makes the solution of the whole system well balanced between complexity and performance.

Description

Based on mimo linear iteraction detection method extensive under non-ideal communication channel
Technical field
The present invention relates to wireless communication technology field, more particularly, to a kind of linear based on mimo extensive under non-ideal communication channel Iteration detection method.
Background technology
The fast development of wireless communication technology and the rapid popularization of smart mobile phone, bringing people needs to wireless data transmission The explosive increase asked.For improving message transmission rate further, build extensive mimo by increasing antenna for base station number (multiple-input multiple-output, multiple-input and multiple-output) system, is a kind of easily mode efficiently and relatively. Extensive mimo system energy depth excavate space dimension degree of freedom so that base station can be served using same running time-frequency resource multiple User.2010, AT＆T Labs scientist thomas l.marzetta proposed the concept of extensive mimo.Extensive mimo Radio communication, configures tens of even more than hundreds of antennas in base station coverage area, supports to join in base station highest compared with lte Putting 8 antenna number increases a more than magnitude.And the antenna number of transmitting terminal or receiving terminal configuration is more, transmission channel can provide Higher degree of freedom, enables better performance on throughput and circuit are stable.Because multi-user system can be with simultaneous transmission Service several users, and select reception particular user scheduling aspect more flexible, so this gain is in multi-user system In more considerable.
However, the drastically expansion of antenna scale, the complexity of system is made to greatly increase.Signal detection, as communication system In a requisite part, be also faced with the problem that under extensive mimo system complexity increases.Therefore, find one kind to exist Between reliability and complexity, compromise detection method is very important.It is easy to hardware is realized in recent main flow document The linearity test of low complex degree receive much concern.Wherein with broken zero detection (zero forcing) and minimum mean-squared error algorithm (mmse) most representative.But in high-dimensional system, it is faced with the solution of m dimensional linear systems, and (m is user sky Line number) traditional matrix inversion technique, the such as accurately inversion technique such as qr decomposition method, Gaussian elimination method and cholesky decomposition method, Its complexity is o (m3) order of magnitude.In scale mimo system, when m becomes larger, its complexity will be increased dramatically, The a large amount of computing resource of system can be expended or increase time delay.At this moment, one kind is efficiently based on extensive multiple-input and multiple-output The matrix inversion technique of mimo linearity test just becomes extremely important.
On the other hand, in extensive mimo system, base station is configured with a large amount of antennas, and the spatial resolution of mimo transmission shows Write and improve, wireless transmission channel has new characteristic, need deeply systematically to inquire into the letter being applied to extensive mimo system Road model.And the introducing of the dependency of user side and base station end can destroy some advantageous properties that original m ties up matrix, make original Some detection methods face inefficacy.
Content of the invention
Goal of the invention: the present invention is directed to the problem that prior art exists, provides a kind of being based under non-ideal communication channel on a large scale Mimo linear iteraction detection method, mainly employs the solution decomposed with conjugate gradient (cg) algorithm with reference to incomplete cholesky Framework, through the pretreatment of incomplete decomposing, the conditional number of matrix to be asked reduces so as to big greatly in the convergence rate of iteration Hurry up.Because pertaining only to additive operation and multiplying through optimizing this algorithm, it is especially suitable for realization within hardware, greatly reduces Hardware complexity.
Technical scheme: of the present invention included based on mimo linear iteraction detection method extensive under non-ideal communication channel:
Channel corresponding matrix h construction mmse detection matrix a according to non-ideal communication channel；
Threshold value η is constructed according to detection matrix a；
Preconditioning matrix m is obtained according to threshold value η and detection matrix a；
The receipt signal matrix received end matched filter being exported using preconditioning matrix mCarry out preconditioned conjugate Iterative detection, obtains transmission signal Matrix Estimation value
Further, the described channel according to non-ideal communication channel corresponding matrix h construction mmse detection matrix a, specifically includes:
Channel corresponding matrix h according to non-ideal communication channel according to below equation construct mmse detection matrix a:
α=hhh+δ-1ι
In formula, δ is the average signal-to-noise ratio of transmitting terminal, and ι is unit matrix.
Further, described according to detection matrix a construct threshold value η, specifically include:
Threshold value η is constructed according to detection matrix a using below equation:
η=ε (1-k/n) aii
In formula, ε is adjustable constant, and k is user side antenna number, and n is base station end antenna number, aiiFor detecting i-th of matrix a Diagonal entry.
Further, described according to threshold value η and detection matrix a obtain preconditioning matrix m, specifically include:
Preconditioning matrix m is defined as m=l-1l-t；Wherein, if element value is less than threshold value η in detection matrix a, under In triangular matrix l, correspondence position element sets to 0, if this element is more than threshold value η, in triangular matrix l, the i-th row jth column element isaijFor detecting the i-th row jth column element of matrix a.
Further, the receipt signal matrix that described employing preconditioning matrix m exports to received end matched filter Carry out preconditioned conjugate iterative detection, specifically include:
A () is initialized: s0=0,p0=z0, wherein, l is lower three angular moments of m Battle array；
B () arranges iterationses j=1；
C () calculates according to below equation:
α j = ( r j , r j ) / ( a p j , p j ) s j + 1 = s j + α j p j r j + 1 = r j + α j ap j z j + 1 = ( ll t ) - 1 r j + 1 β j = ( r j + 1 , z j + 1 ) / ( r j , z j ) p j + 1 = z j + 1 + β j p j
D () is by j=j+1, and be back to (c), till iterating to preset times m, then smEstimate for transmission signal matrix Evaluation
Although wherein zj+1=(llt)-1rj+1It is related to a linear system solution, but because it can be write as cam system Form:qj+1For intermediate variable, therefore this walks computation complexity and still controls in o (n2).
Beneficial effect: compared with prior art, its remarkable advantage is the present invention: 1, the present invention passes through pretreatment, reduces The conditional number rising because matrix propertieses deteriorate, can preferably solve to expand in matrix size or channel relevancy strengthens In the case of, the defect that the convergence rate of iterative algorithm slows down；2nd, the present invention is in view of channel correlation coefficient and antenna number On the basis of a kind of adaptive threshold is proposed, to carry out incomplete decomposing pretreatment, because the preprocess method adopting is so that every The complexity of the solution linear equation in single-step iteration controls in o (n2), it is a kind of method taking into account complexity and reliability.
Brief description
Fig. 1 is the schematic flow sheet of the present invention；
Fig. 2 is the present invention and traditional ber curve comparison diagram under different state of signal-to-noise for the conjugate gradient algorithms；
Fig. 3 is the bit error rate with traditional conjugate gradient algorithms in the case of fixing number of users different base station antenna number for the present invention Curve comparison diagram；
Fig. 4 is the complexity contrast that the present invention and traditional conjugate gradient algorithms and cholskey decompose accurate inversion technique Figure.
Specific embodiment
In extensive mimo system, (antenna for base station number n is much larger than number of users k) typically n ＞ ＞ k.S is allowed to represent k × 1 The signal vector of rank, s contains the transmission symbol producing from k user.Represent channel response matrix, therefore base station end Received signal vector can be expressed as
Y=hs+n
Wherein n is the additive white Gaussian noise vector of n × 1 dimension, and its element is obeyed
The multiuser signal detection task of base station is exactly to estimate transmission signal symbol from the plus noise signal vector y receiving s.H can be obtained by time domain or pilot tone.Theoretical using least mean-square error (mmse) linearity test, to receipt signal to Amount is expressed as
( h h h + δ - 1 i ) s = a s = h h y = y &overbar;
WhereinIt is the output receiving vectorial y in receiving terminal matched filter.As can be seen that estimating to transmission signal vector EvaluationIt is represented by:In order to solveIteration detection method using the present embodiment.
As shown in figure 1, the present embodiment based on mimo linear iteraction detection method extensive under non-ideal communication channel include with Lower step:
S1, the channel corresponding matrix h construction mmse detection matrix a according to non-ideal communication channel.
Wherein, mmse detection matrix a is: α=hhh+δ-1ι, in formula, δ is the average signal-to-noise ratio of transmitting terminal, and ι is unit square Battle array.
S2, according to detection matrix a construct threshold value η.
Wherein, η=ε (1-k/n) aii, in formula, ε is adjustable constant, and k is user side antenna number, and n is base station end antenna number, aiiFor detecting i-th diagonal entry of matrix a.
S3, according to threshold value η and detection matrix a obtain preconditioning matrix m.
Specifically, this step specifically includes: preconditioning matrix m is defined as m=l-1l-t；Wherein, if detection matrix a Middle element value is less than threshold value η, then in lower triangular matrix l, correspondence position element sets to 0, if this element is more than threshold value η, triangular matrix In l, the i-th row jth column element isaijFor detecting the i-th row jth column element of matrix a.
S4, the receipt signal matrix received end matched filter being exported using preconditioning matrix mCarry out pretreatment Conjugation iterative detection, obtains transmission signal Matrix Estimation value
Specifically, this step includes；
A () is initialized: s0=0,p0=z0, wherein, l is lower three angular moments of m Battle array；
B () arranges iterationses j=1；
C () calculates according to below equation:
α j = ( r j , r j ) / ( a p j , p j ) s j + 1 = s j + α j p j r j + 1 = r j + α j ap j z j + 1 = ( ll t ) - 1 r j + 1 β j = ( r j + 1 , z j + 1 ) / ( r j , z j ) p j + 1 = z j + 1 + β j p j
D () is by j=j+1, and be back to (c), till iterating to preset times m, then smEstimate for transmission signal matrix Evaluation
The method passes through pretreatment, reduces the conditional number rising because matrix propertieses deteriorate, sees Fig. 3, can be preferable Solution in the case of matrix size expands or channel relevancy is enhanced, the defect that the convergence rate of iterative algorithm slows down, See Fig. 2.
In addition, this method proposes a kind of adaptive threshold on the basis of in view of channel correlation coefficient and antenna number, come Carry out incomplete decomposing pretreatment, due to the preprocess method that adopts so that solution linear equation in every single-step iteration Complexity controls in o (n2).Table 1 lists the algorithm complex of its each step, and its complexity due to the setting of threshold value and sets to 0 place So that each step has s element to be not involved in actual operation, therefore algorithm complex is not significantly increased reason, sees Fig. 4.
Table 1

Claims (5)

1. a kind of based on mimo linear iteraction detection method extensive under non-ideal communication channel it is characterised in that the method includes:
Channel corresponding matrix h construction mmse detection matrix a according to non-ideal communication channel；
Threshold value η is constructed according to detection matrix a；
Preconditioning matrix m is obtained according to threshold value η and detection matrix a；
The receipt signal matrix received end matched filter being exported using preconditioning matrix mCarry out preconditioned conjugate iteration Detection, obtains transmission signal Matrix Estimation value
2. according to claim 1 based on mimo linear iteraction detection method extensive under non-ideal communication channel, its feature exists In: the described channel according to non-ideal communication channel corresponding matrix h construction mmse detection matrix a, specifically include:
Channel corresponding matrix h according to non-ideal communication channel according to below equation construct mmse detection matrix a:
α=ηηη+δ-1ι
In formula, δ is the average signal-to-noise ratio of transmitting terminal, and ι is unit matrix.
3. according to claim 1 based on mimo linear iteraction detection method extensive under non-ideal communication channel, its feature exists In: described according to detection matrix a construct threshold value η, specifically include:
Threshold value η is constructed according to detection matrix a using below equation:
η=ε (1-k/n) aii
In formula, ε is adjustable constant, and k is user side antenna number, and n is base station end antenna number, aiiI-th for detecting matrix a diagonal Line element.
4. according to claim 1 based on mimo linear iteraction detection method extensive under non-ideal communication channel, its feature exists In: described according to threshold value η and detection matrix a obtain preconditioning matrix m, specifically include:
Preconditioning matrix m is defined as m=l-1l-t；Wherein, if element value is less than threshold value η in detection matrix a, lower three angular moments In battle array l, correspondence position element sets to 0, if this element is more than threshold value η, in triangular matrix l, the i-th row jth column element isaijFor detecting the i-th row jth column element of matrix a.
5. according to claim 1 based on mimo linear iteraction detection method extensive under non-ideal communication channel, its feature exists In: the receipt signal matrix that described employing preconditioning matrix m exports to received end matched filterCarry out preconditioned conjugate Iterative detection, specifically includes:
A () is initialized: s0=0,p0=z0, wherein, l is the lower triangular matrix of m；
B () arranges iterationses j=1；
C () calculates according to below equation:
α j = ( r j , r j ) / ( a p j , p j ) s j + 1 = s j + α j p j r j + 1 = r j + α j a p j z j + 1 = ( ll t ) - 1 r j + 1 β j = ( r j + 1 , z j + 1 ) / ( r j , z j ) p j + 1 = z j + 1 + β j p j
D () is by j=j+1, and be back to (c), till iterating to preset times m, then smFor transmission signal Matrix Estimation value
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107086971A (en) * 2017-03-24 2017-08-22 东南大学 A kind of soft detection methods of extensive MIMO suitable for a variety of antenna configurations
CN107222246A (en) * 2017-05-27 2017-09-29 东南大学 The efficient extensive MIMO detection method and system of a kind of approximated MMSE-based performance
CN107231177A (en) * 2017-05-19 2017-10-03 东南大学 Efficient CR detection methods and framework based on extensive MIMO
CN110336594A (en) * 2019-06-17 2019-10-15 浙江大学 A kind of deep learning signal detecting method based on conjugate gradient decent
CN113328771A (en) * 2021-06-03 2021-08-31 重庆邮电大学 Large-scale MIMO signal detection method based on conjugate gradient algorithm

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CN105515627A (en) * 2015-12-07 2016-04-20 东南大学 Large-scale MIMO (Multiple-Input Multiple-Output) detecting method and device

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CN105515627A (en) * 2015-12-07 2016-04-20 东南大学 Large-scale MIMO (Multiple-Input Multiple-Output) detecting method and device

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107086971A (en) * 2017-03-24 2017-08-22 东南大学 A kind of soft detection methods of extensive MIMO suitable for a variety of antenna configurations
CN107231177A (en) * 2017-05-19 2017-10-03 东南大学 Efficient CR detection methods and framework based on extensive MIMO
CN107231177B (en) * 2017-05-19 2020-05-05 东南大学 Efficient CR detection method and architecture based on large-scale MIMO
CN107222246A (en) * 2017-05-27 2017-09-29 东南大学 The efficient extensive MIMO detection method and system of a kind of approximated MMSE-based performance
CN107222246B (en) * 2017-05-27 2020-06-16 东南大学 Efficient large-scale MIMO detection method and system with approximate MMSE performance
CN110336594A (en) * 2019-06-17 2019-10-15 浙江大学 A kind of deep learning signal detecting method based on conjugate gradient decent
CN110336594B (en) * 2019-06-17 2020-11-24 浙江大学 Deep learning signal detection method based on conjugate gradient descent method
CN113328771A (en) * 2021-06-03 2021-08-31 重庆邮电大学 Large-scale MIMO signal detection method based on conjugate gradient algorithm
CN113328771B (en) * 2021-06-03 2022-09-23 重庆邮电大学 Large-scale MIMO signal detection method based on conjugate gradient algorithm

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