CN105978609A - Massive MIMO linear detection hardware architecture and method under correlated channels - Google Patents

Abstract

The invention discloses a massive MIMO linear detection hardware architecture and a method under correlated channels. The massive MIMO linear detection hardware architecture comprises a lower triangular pulse multiplication module, a noise addition module, a diagonal matrix inversion module, a vector multiplier, an iteration module, and a detection module. The iteration module comprises a principal diagonal addition module and a coefficient adjustment module. A channel response matrix sequentially passes through the lower triangular pulse multiplication module and the noise addition module, and then enters the diagonal matrix inversion module and the vector multiplier. The vector multiplier removes principal diagonal elements from the matrix output by the noise addition module, and multiplies the matrix by a matrix solved in the diagonal matrix inversion module. The massive MIMO linear detection hardware architecture and the method are applicable to a matrix inversion algorithm under wider channel conditions, and have the advantages of low computational complexity, high accuracy and good flexibility. Moreover, the channel adaptability and throughput of the linear detection architecture are improved greatly.

Description

Extensive MIMO linearity test hardware architecture and method under a kind of correlated channels

Technical field

The present invention is applicable to the popular extensive MIMO of communication technology instantly.The design provides one and is applicable to correlated channels In the case of, the linearity test framework that the low complex degree under multiple-input and multiple-output background is inverted.

Background technology

MIMO (Multiple-Input Multiple-Output) technology refers to use many respectively at transmitting terminal and receiving terminal Individual transmitting antenna and reception antenna, make signal pass through transmitting terminal and multiple antenna transmission of receiving terminal and reception.In short supply at frequency spectrum The most not start with from frequency, antenna is not launched power simultaneously and brings bigger consumption, this technology make use of Space resources, increases exponentially system channel capacity, improves communication quality, has the most superior performance, is considered of future generation The core technology of mobile communication.It is included into forth generation mobile communication standard.

Traditional small-scale MIMO dual-mode antenna number is all in smaller magnitude, to the improvement of communication quality and fail to understand Aobvious, cut the optkmal characteristics the most well representing MIMO.The base station (BS) of extensive MIMO on this basis is equipped with in a large number Antenna (N), a relatively low number of users or moving station number (M) are provided simultaneously.Substantially, for base station be equipped with one big Aerial array (N → ∞), under such vast scale antenna, more can bring more channel capacity gain.Have been demonstrated that having Under the propagation conditions of profit, all incoherent noises and fast-fading.

MIMO technology is that his high power capacity of achievements is high-precision as his theory advantage of the future communication technologies attracted most attention at present The basis of exactness, and implement to concrete implementation, it is only and utilizes this technology closely bound up with us.Although it is extensive MIMO has superior performance, but the index that the huge amplification of antenna magnitude brings computation complexity rises.How to combine The concrete condition of channel, design is efficient to be sent and detection framework, has become the important step put theory into practice, and this is also Determine the 5th requisite part of third-generation mobile communication standard eventually.

The existing plurality of articles of conception for precoding and detection part is mentioned at present, and its main computation complexity exists In inverting of M × M rank matrix, wherein M is user antenna number.Accurate matrix inversion technique, such as Cholesky decomposition method Complexity is O (M³) order of magnitude.So when the high number of M, it is complicated that such inversion approach brings huge calculating Degree and hardware consumption.Differentiation simultaneously for different frames also needs to rely on an important factor, it is simply that channel.The most Research be all based on Gauss ideal communication channel.But in our real life, it is impossible to ensure the desirability of channel, then Under correlated channels, the performance of detection module is more of practical significance with the investigation in many ways of hardware cost.The present invention sets up exactly The hardware architecture inquiring into low-cost high-efficiency in different channels situation under MIMO background.

Summary of the invention

Goal of the invention: in order to overcome MIMO high efficiency for low complex degree in the case of correlated channels in prior art linear The disappearance of detection, the present invention provides extensive MIMO linearity test hardware architecture and method under a kind of correlated channels, be suitable for more The extensively matrix inversion algorithm under channel condition, computation complexity is little, and accuracy is high, motility is good, and substantially increases line Property the detection channel adaptation ability of framework and throughput.

Technical scheme: for achieving the above object, the technical solution used in the present invention is:

Extensive MIMO linearity test hardware architecture under a kind of correlated channels, including lower triangle pulsation multiplier module, adds and makes an uproar Module, diagonal matrix are inverted module, vector multiplier, iteration module and detection module, and described iteration module includes main diagonal angle addition Module, coefficient adjustment module, wherein:

Lower triangle pulsation multiplier module is for obtaining Wei Sha spy matrix G=H according to channel response matrix H^HH, and by this Wei Spy Sha matrix G=H^HH flows to add module of making an uproar；Matrix Q and diagonal addition module for inputting according to vector multiplier are defeated The result gone outObtainWhereinFor the iteration result once obtained before iteration module, and will obtain It is input to coefficient adjustment module.

Add module of making an uproar for accepting the noise variance σ of input², and by noise variance σ²It is added to lower triangle pulsation multiplier module Matrix A to be inverted, A=G+ σ is tried to achieve on the diagonal of the Wei Sha spy matrix G of input²I_M=E+X, wherein matrix X is square to be inverted The elements in a main diagonal diagonal matrix of battle array A, E is the matrix deducting the elements in a main diagonal diagonal matrix in matrix A to be inverted, and H is channel Response matrix, σ²For noise variance, I_MFor unit battle array, (.)^HFor conjugate transposition operation；And the elements in a main diagonal diagonal matrix X is sent out Give diagonal matrix to invert module, the matrix E deducting the elements in a main diagonal diagonal matrix in matrix A to be inverted is sent to vector multiplication Device.

Diagonal matrix module of inverting, for inverting the elements in a main diagonal diagonal matrix X, obtains the elements in a main diagonal diagonal matrix Inverse matrix X^-1, and by inverse matrix X of the elements in a main diagonal diagonal matrix^-1It is input to the diagonal angle in vector multiplier and iteration module Battle array addition module.

Vector multiplier is for according to deducting the matrix E of the elements in a main diagonal diagonal matrix and main diagonal angle in matrix A to be inverted Inverse matrix X of line element diagonal matrix^-1It is multiplied and negates, obtaining matrix Q=-X^-1(A-X), wherein E=A-X, and will obtain The lower triangle pulsation multiplier module that matrix Q is input in iteration module.

Coefficient adjustment module is for according to the pulsation multiplier module input of lower triangleIt is iterated coefficient adjustment, ArriveAnd willIt is pushed to main diagonal angle addition module.

Main diagonal angle addition module is for inverse matrix X according to the elements in a main diagonal diagonal matrix^-1WithObtain Complete an iteration, wherein:

K is iterations；

Then will obtainIt is pushed to lower triangle pulsation multiplier module and carries out next iteration, be pushed to detection simultaneously Module detects.

The wave filter output that detection module will receivePulse what multiplier module obtained with lower triangleIt is multiplied and is passed The estimation of defeated signal vector And then obtain result of linear detection based on extensive MIMO.

Preferred: described iterations k is 2-6 time.

It is preferred: described lower triangle pulsation multiplier module includes (1+M) M/2 adder and (1+M) M/2 multiplier, Wherein M represents the quantity of user.

Preferred: described vector multiplier M multiplier.

Preferred: described main diagonal angle addition module includes M adder.

Preferred: described detection module includes M multiplier, M adder.

Further: the diagonal matrix addition module in described iteration module is provided with lower triangle pulsation multiplier module and deposits Device, the data of the diagonal matrix addition module output in iteration module first leave in depositor, and lower triangle pulsation multiplier module exists The data that the diagonal matrix addition module in iteration module exports are called by depositor.

A kind of linearity test method of extensive MIMO linearity test hardware architecture under correlated channels, comprises the following steps:

Step 1, obtains Wei Sha spy matrix G=H by lower for channel response matrix H input triangle pulsation multiplier module^HH；By Wei Spy Sha matrix G and noise variance σ²It is input to add module of making an uproar, adds module of making an uproar by noise σ²It is added on the diagonal of Wei Sha spy matrix G Try to achieve matrix A to be inverted=G+ σ²I_M=E+X, wherein matrix X is the elements in a main diagonal diagonal matrix of matrix A to be inverted, and E is for treating Deducting the matrix of the elements in a main diagonal diagonal matrix in finding the inverse matrix A, H is channel response matrix, σ²For noise variance, I_MFor unit Battle array, (.)^HFor conjugate transposition operation.

Step 2, the elements in a main diagonal diagonal matrix X is inverted by diagonal matrix module of inverting, and obtains the elements in a main diagonal diagonal matrix Inverse matrix X^-1, and by inverse matrix X of the elements in a main diagonal diagonal matrix^-1Be input in vector multiplier and iteration module is right Angle battle array addition module.

Step 3, obtains inverse matrix X by step 2^-1The elements in a main diagonal pair is deducted with in the matrix A to be inverted of step 1 acquisition The matrix E of angle battle array is input in vector multiplier be multiplied and negate, and obtains matrix Q=-X^-1(A-X), wherein E=A-X, and The lower triangle pulsation multiplier module that matrix Q is input in iteration module will be obtained.

Step 4, the described lower triangle pulsation matrix Q that inputs according to vector multiplier of multiplier module and diagonal addition module The result of outputObtainAnd will obtainIt is input to coefficient adjustment module；Coefficient adjustment module pairCarry out coefficient adjustment, obtainAnd willIt is pushed to main diagonal angle addition module, wherein a_k-1For adjusting Integral coefficient；Main diagonal angle addition module is according to inverse matrix X of the elements in a main diagonal diagonal matrix^-1WithObtainIts In:

$A_k^{-1}=\left\{\begin{array}{cc}{X}^{-1}+a_{k-1}{\mathrm{QA}}_{k-1}^{-1},& k>1\\ X^{-1},& k=1\end{array}\right.;$

Then will obtainBeing pushed to lower triangle pulsation multiplier module respectively and carry out next iteration, k is iteration time Number, is pushed to detection module simultaneously and detects；When iteration, control iterations by clock signal.

Step 5, the wave filter output that detection module will receivePulse what multiplier module obtained with lower triangleIt is multiplied Obtain the estimation of transmission signal vectors And then obtain result of linear detection based on extensive MIMO.

Preferred: the model of coefficient adjustment in described step 4:

$A_k^{-1}=\underset{m=1}{\overset{k-1}{\Σ}}{(I-X^{-1}A)}^{m-1}{X}^{-1}+0.5\×{(I-X^{-1}A)}^{k-1}{X}^{-1}.$

Preferred: the iterations in described step 4 is 2-6 time

Beneficial effect: extensive MIMO linearity test hardware architecture and method under a kind of correlated channels that the present invention provides, Compared to existing technology, have the advantages that

Emphasis of the present invention considers the correlated performance of channel, and this framework is applicable to the linearity test of correlated channels, with physics The actual Du Genggao that is consistent.And the hardware complexity of the present invention is relatively low, greatly reduces computation complexity；Meanwhile, iterative computation Can obtain the accuracy of arbitrary accuracy, the change of iterations is flexible, and the occasion different for performance requirement provides preferably Motility.And the adjustment of now degree of accuracy is only the most relevant with iterations, i.e. only has certain relation with handling capacity size, has no effect on Hardware architecture.And the coefficient adjustment of the present invention pertains only to data and moves to left and move to right, thus realize multiplication, preferably evade volume Outer multiplier, substantially increases the channel adaptation ability of linearity test framework while not increasing hardware resource consumption.This Invention also substantially increases throughput.

Accompanying drawing explanation

Fig. 1: based on MIMO linearity test hardware architecture schematic diagram extensive under correlated channels；

Fig. 2: during channel coefficients ζ=0.3, uses coefficient adjustment Linear of the present invention detection and simple leading diagonal Nuo Yiman Linearity test decomposes the ber curve comparison diagram of linearity test of accurately inverting with Cholesky；

Fig. 3: during channel coefficients ζ=0.6, uses coefficient adjustment Linear of the present invention detection and simple leading diagonal Nuo Yiman Linearity test decomposes the ber curve comparison diagram of linearity test of accurately inverting with Cholesky；

Fig. 4: use the sequential chart that coefficient adjustment type Neumann's series of the present invention is inverted.

Detailed description of the invention

Below in conjunction with the accompanying drawings and specific embodiment, it is further elucidated with the present invention, it should be understood that these examples are merely to illustrate this Invention rather than limit the scope of the present invention, after having read the present invention, various to the present invention of those skilled in the art The amendment of the equivalent form of value all falls within the application claims limited range.

Extensive MIMO linearity test hardware architecture under a kind of correlated channels, including lower triangle pulsation multiplier module, adds and makes an uproar Module, diagonal matrix are inverted module, vector multiplier, iteration module and detection module, and described iteration module includes main diagonal angle addition Module, coefficient adjustment module, wherein:

Lower triangle pulsation multiplier module is for obtaining Wei Sha spy matrix G=H according to channel response matrix H^HH, and by this Wei Spy Sha matrix G=H^HH flows to add module of making an uproar；Matrix Q and diagonal addition module for inputting according to vector multiplier are defeated The result gone outObtainWhereinFor the iteration result once obtained before iteration module, and will obtain It is input to coefficient adjustment module.

Add module of making an uproar for accepting the noise variance σ of input², and by noise variance σ²It is added to lower triangle pulsation multiplier module Matrix A to be inverted, A=G+ σ is tried to achieve on the diagonal of the Wei Sha spy matrix G of input²I_M=E+X, wherein matrix X is square to be inverted The elements in a main diagonal diagonal matrix of battle array A, E is the matrix deducting the elements in a main diagonal diagonal matrix in matrix A to be inverted, and H is channel Response matrix, σ²For noise variance, I_MFor unit battle array, (.)^HFor conjugate transposition operation；And the elements in a main diagonal diagonal matrix X is sent out Give diagonal matrix to invert module, the matrix E deducting the elements in a main diagonal diagonal matrix in matrix A to be inverted is sent to vector multiplication Device.

Diagonal matrix module of inverting, for inverting the elements in a main diagonal diagonal matrix X, obtains the elements in a main diagonal diagonal matrix Inverse matrix X^-1, and by inverse matrix X of the elements in a main diagonal diagonal matrix^-1It is input to the diagonal angle in vector multiplier and iteration module Battle array addition module.

Vector multiplier is for according to deducting the matrix E of the elements in a main diagonal diagonal matrix and main diagonal angle in matrix A to be inverted Inverse matrix X of line element diagonal matrix^-1It is multiplied and negates, obtaining matrix Q=-X^-1(A-X), wherein E=A-X, and will obtain The lower triangle pulsation multiplier module that matrix Q is input in iteration module.

Coefficient adjustment module is for according to the pulsation multiplier module input of lower triangleIt is iterated coefficient adjustment, ArriveAnd willIt is pushed to main diagonal angle addition module.

Main diagonal angle addition module is for inverse matrix X according to the elements in a main diagonal diagonal matrix^-1WithObtain Complete an iteration, wherein:

K is iterations；

Then will obtainIt is pushed to lower triangle pulsation multiplier module and carries out next iteration, be pushed to detection simultaneously Module detects.

The wave filter output that detection module will receivePulse what multiplier module obtained with lower triangleIt is multiplied and is passed The estimation of defeated signal vector And then obtain result of linear detection based on extensive MIMO.

Described iterations k is 2-6 time.

Described lower triangle pulsation multiplier module includes (1+M) M/2 adder and (1+M) M/2 multiplier, wherein M table Show the quantity of user.

Described vector multiplier M multiplier.

Described main diagonal angle addition module includes M adder.

Described detection module includes M multiplier, M adder.

Diagonal matrix addition module in described iteration module is provided with depositor, iteration mould with lower triangle pulsation multiplier module The data of the diagonal matrix addition module output in block first leave in depositor, and lower triangle pulsation multiplier module is passing through depositor Call the data that the diagonal matrix addition module in iteration module exports.

A kind of linearity test method of extensive MIMO linearity test hardware architecture under correlated channels, comprises the following steps:

Step 1, obtains Wei Sha spy matrix G=H by lower for channel response matrix H input triangle pulsation multiplier module^HH；By Wei Spy Sha matrix G and noise variance σ²It is input to add module of making an uproar, adds module of making an uproar by noise σ²It is added on the diagonal of Wei Sha spy matrix G Try to achieve matrix A to be inverted=G+ σ²I_M=E+X, wherein matrix X is the elements in a main diagonal diagonal matrix of matrix A to be inverted, and E is for treating Deducting the matrix of the elements in a main diagonal diagonal matrix in finding the inverse matrix A, H is channel response matrix, σ²For noise variance, I_MFor unit Battle array, (.)^HFor conjugate transposition operation.

Step 2, the elements in a main diagonal diagonal matrix X inverts by diagonal matrix module of inverting, and the most each the elements in a main diagonal is asked down；? Inverse matrix X to the elements in a main diagonal diagonal matrix^-1, and by inverse matrix X of the elements in a main diagonal diagonal matrix^-1It is input to vector take advantage of Diagonal matrix addition module in musical instruments used in a Buddhist or Taoist mass and iteration module.

Step 3, obtains inverse matrix X by step 2^-1The elements in a main diagonal pair is deducted with in the matrix A to be inverted of step 1 acquisition The matrix E of angle battle array is input in vector multiplier be multiplied and negate, and obtains matrix Q=-X^-1(A-X), wherein E=A-X, and The lower triangle pulsation multiplier module that matrix Q is input in iteration module will be obtained.

Step 4, in iteration module, main diagonal angle addition module, coefficient adjustment module and lower triangle pulsation multiplier module enter Row loop iteration, matrix Q and diagonal addition module that described lower triangle pulsation multiplier module inputs according to vector multiplier are defeated The result gone outObtainAnd will obtainIt is input to coefficient adjustment module；Coefficient adjustment module pair Carry out coefficient adjustment, obtainAnd willIt is pushed to main diagonal angle addition module, wherein a_k-1For adjusting system Number, coefficient is positive integer or the negative integer times of 2；Main diagonal angle addition module is according to inverse matrix X of the elements in a main diagonal diagonal matrix^-1WithObtainWherein:

$A_k^{-1}=\left\{\begin{array}{cc}{X}^{-1}+a_{k-1}{\mathrm{QA}}_{k-1}^{-1},& k>1\\ X^{-1},& k=1\end{array}\right.;$

Then will obtainBeing pushed to lower triangle pulsation multiplier module respectively and carry out next iteration, k is iteration time Number, is pushed to detection module simultaneously and detects；When iteration, control iterations by clock signal.

Regulation coefficient a_kOn The Choice, can be according to formula:

$A_k^{-1}=\underset{m=1}{\overset{k-1}{\Σ}}{(I-X^{-1}A)}^{m-1}{X}^{-1}+0.5\×{(I-X^{-1}A)}^{k-1}{X}^{-1}.$

Difference according to channel relevancy and the difference of detection precise requirements, iterations is 2-6 time.

Step 5, the wave filter output that detection module will receivePulse what multiplier module obtained with lower triangleIt is multiplied Obtain the estimation of transmission signal vectors It is expressed as the wave filter output of y, And then obtain result of linear detection based on extensive MIMO.

Operation principle: the present invention uses leading diagonal Neumann's series to invert as entirety and inverts framework, it is considered to extensive The characteristic that the elements in a main diagonal of MIMO is dominant relative to other diagonal entries, uses leading diagonal matrix as X matrix, has Effect avoids and carries out division arithmetic in inversion approach, is especially suitable for realization within hardware, greatly reduces hardware complexity. Simultaneously take account of the correlated channels impact on main diagonal dominance performance, each term coefficient of Neumann's series is optimized and revised, and Coefficient control is positive integer or the negative integer times of 2, thus is moved to left by data and moved to right co-efficient multiplication and do not consumed extra hard Part resource.Higher to the adaptation ability of correlated channels by the linearity test framework after coefficient adjustment, its linearity test errored bit Curve gain suppression.And data stream is streamline form, there is extraordinary framework.

Concrete, to set up a mimo channel model and be simulated operation, employing is Kronecker model, according to Kronecker model writes out the channel response matrix H such as following formula of uplink in extensive mimo system:

H=R^1/2T

Wherein R^1/2Represent for receiving terminal correlation matrix.Transmission matrix T is modeled as independent rapid fading (fast Fading), geonetrical attenuation (geometric attenuation) and Lognormal shadowing (log-normal shadow Fading) coefficient matrix.

The formula of correlation matrix R is as follows:

$\left\{\begin{array}{cc}R(c,v)={\left({\mathrm{\ζe}}^{j\mathrm{\θ}}\right)}^{v-c},& c\≤v\\ R(c,v)=R{(c,v)}^{*},& c>v\end{array}\right.$

Wherein (c v) represents the element of c row v row in correlation matrix R to R；Wherein ζ (0≤ζ≤1) represents continuously The order of magnitude of correlation coefficient between transmission antenna, ζ is defined as channel coefficients by us, when ζ=0, for ideal communication channel model, when During ζ=1, characterize the transmission situation of correlation maximum, i.e. worst channel, θ is given phase place, and it has no effect on whole The performance of individual system.E is the end of natural logrithm, e=2.7182818...；(.)^*For conjugate operation.

So, the deciding factor affecting channel response matrix H is ζ, and his size illustrates channel correlation circumstance.

In extensive mimo system, typically there is N ＞ ＞ M (antenna for base station number N is much larger than number of users M).Allow s=[s₁,s₂, s₃,…,s_M]^TRepresent signal vector, s contains the transmission symbol produced from M user respectively.H table dimension is N × M channel Response matrix, is the channel response matrix set up in Channel Modeling, therefore the received signal vector y of base station end can be expressed as

Y=Hs+ γ

Wherein γ is the additive white Gaussian noise vector of N × 1 dimension, and it is σ that its element obeys zero-mean variance²Gauss Distribution.The multiuser signal detection task of base station is exactly the plus noise signal vector y=[y received from receiver₁,y₂, y₃,…,y_N]^TEstimate transmission signal code s.Assume that H can be obtained by time domain or pilot tone.Use least mean-square error (MMSE) linearity test is theoretical, and the estimation to transmission signal vectors is expressed as

$\hat{s}={(H^{H}H+{\mathrm{\σ}}^2{I}_M)}^{-1}{H}^{H}y=A^{-1}\stackrel{\‾}{y}$

Wherein (.)^HRepresent conjugate transposition operation,It is expressed as the wave filter output of y, by MMSE electric-wave filter matrix A It is expressed as

A=G+ σ²I_M

Wherein G=H^HH represents Wei Sha spy's matrix, wherein (.)^HRepresent conjugate transposition operation.I_MFor M rank unit matrix.

In the extensive mimo system of uplink multi-users, MMSE linearity test mechanism is close to optimum detection mechanism.But, MMSE theory inevitably involves complex matrix A^-1Calculate, the most just can obtain MMSE and estimate.And the general matrix side of inverting The computation complexity of method is O (M³), when in extensive mimo system, the value of M is increased dramatically, this makes general inversion technique Complexity become to bear.The present invention simultaneously takes account of under the background of extensive MIMO, and channel transfer matrices has one Fixed characteristic, this asks for required inverse matrix as breach by other means.

In order to solve the above-mentioned inversion problem to matrix A, the present invention uses Neumann's series theory to invert to reach approximation, Because this method pertains only to additive operation and multiplying, it is particularly suitable for hardware and realizes, be therefore especially suitable for extensive MIMO System.

For an invertible matrix A, it is assumed that a matrix X meets

lim_m→∞(I-X^-1A)^m=0 or lim_m→∞(I-AX^-1)^m=0；

The inverse of so A can be expressed as

$A^{-1}={\mathrm{\Σ}}_{m=0}^{\mathrm{\∞}}{(I-X^{-1}A)}^m{X}^{-1};$

But in formula, the infinite series addition of display is wanted to realize on hardware is very unrealistic, i.e. time delay reaches nothing Limit for length.Therefore take the approximation of limited k rank Neumann's series as the final result inverted:

$A_k^{-1}={\mathrm{\Σ}}_{m=1}^k{(I-X^{-1}A)}^{m-1}{X}^{-1};$

Wherein, subscript m represents the label of exponent number, and k represents that the sum of exponent number, matrix X are the matrixes of an initial approximation, with Time it must be susceptible to realization and inverts.In view of the feature of channel model in the case of extensive MIMO, use the main diagonal matrix conduct of A Original matrix: 1, diagonal matrix is a kind of special sparse matrix, is beneficial to invert, its inverse matrix is exactly that each element of diagonal is asked down Number；Although leading diagonal advantage declines under 2 correlated channels, but the system adaptation energy to channel can be promoted by additive method Power.

Owing to, under correlated channels, A matrix leading diagonal advantage is weakened.Simple leading diagonal Neumann's series is linearly examined Survey framework convergence poor, all can not restrain under higher iteration cycle, therefore use coefficient adjustment algorithm to Neumann's series Coefficient is adjusted, thus improves the system adaptation ability to correlated channels.Add coefficient adjustment factor a_kAfter Nuo Yiman level Number is inverted, and iterative formula is rewritable is:

$A_k^{-1}=\left\{\begin{array}{cc}{X}^{-1}+a_{k-1}{\mathrm{QA}}_{k-1}^{-1},& k\≥1\\ X^{-1},& k=1\end{array}\right.$

Wherein, X is the main diagonal matrix of A, and Q is Q=-X^-1(A-X).The coefficient adjustment factor is positive integer or the negative integer times of 2.

Regulation coefficient a_kOn The Choice, can be according to one group of preferably empirical equation:

$A_k^{-1}=\underset{m=1}{\overset{k-1}{\Σ}}{(I-X^{-1}A)}^{m-1}{X}^{-1}+0.5\×{(I-X^{-1}A)}^{k-1}{X}^{-1}$

As it is shown in figure 1, the design uses extensive MIMO linearity test hardware architecture under correlated channels, including lower triangle Pulsation multiplier module, add module of making an uproar, diagonal matrix inverts module, vector multiplier, iteration module and detection module；Wherein, will letter Road response matrix H sequentially passes through triangle pulsation multiplier module and respectively enters diagonal matrix after adding module of making an uproar and invert module and vector is taken advantage of Musical instruments used in a Buddhist or Taoist mass；Diagonal matrix invert module take add make an uproar module output matrix A in leading diagonal composition matrix X and its invert, the side of inverting Method is exactly that step-by-step is inverted；Vector multiplier will remove the matrix E after diagonal entry from the matrix adding module output of making an uproar =A-X, and invert X required in module with diagonal matrix^-1It is multiplied to negate and obtains matrix Q；Iteration module contains diagonal addition Module, coefficient adjustment module and lower triangle pulsation multiplier module, diagonal matrix invert module result input to diagonal matrix addition mould Block, the result input of vector multiplier is to lower triangle pulsation multiplier module, diagonal addition module, coefficient adjustment module and lower three The matrix that angle pulsation multiplier module will generate after being circulated iterationInput detection module.

Wherein, lower triangle pulsation matrix multiplication module: classical lower triangle pulsation multiplier module can be used to solution formula H^HH, assumes to have obtained channel response matrix H, then as input, H can be passed through lower triangle pulsation multiplication modulo in the present embodiment Block, thus obtain H^HH.Output result passes through G=H^HH is passed through and adds module of making an uproar.On hardware architecture, lower triangle pulsation matrix multiplication mould Block needs (1+M) M/2 adder, and with (1+M) M/2 multiplier, wherein M is number of users.

Add module of making an uproar: matrix G as adding an input of module of making an uproar, noise σ²It it is another input adding module of making an uproar.Will Numerical value σ²It is added on the diagonal of G, thus tries to achieve matrix A to be inverted=G+ σ²I_M.On hardware architecture, adding module of making an uproar needs M to add Musical instruments used in a Buddhist or Taoist mass.

Diagonal matrix is inverted module: diagonal matrix is the most special a kind of sparse matrix, and its inverse matrix is exactly by diagonal Element the most inverted, and X^-1Remain a diagonal matrix.When here seeking inverse, FPGA is used to table look-up to obtain method. Look-up table can exempt the use of divider, is the requisite step of hardware optimal design.

Vector multiplier: for the matrix Q, Q=-X that ask^-1(A-X). i.e. diagonal matrix X^-1Take advantage of with general matrix E=(A-X) Musical instruments used in a Buddhist or Taoist mass.Can be by X^-1In the elements in a main diagonal be seen as a vectorEach element of vector is right with E's Answer row element to be multiplied, so can be obtained by Q.This module passes through M multiplier, can realize taking advantage of of diagonal matrix and Arbitrary Matrix Method.The lower triangle pulsation multiplier module that result Q is passed through in iteration module as one of the input of iteration.

Iteration module: iteration module here is coefficient adjustable type based on Neumann's series, matrix inversion calculates iteration, By X^-1Iteration module is inputted respectively with Q.Iteration module is controlled iterations by by clock signal, during iterative computation one time, and approximation MatrixDuring iteration 2 times, export the matrix of 2 rank approximationsTherefore during iteration k time, output The matrix of k rank approximationAs K → ∞, approximate solutionEqual to accurately solving A^-1。 So iteration module has been divided into diagonal addition module, coefficient adjustment module and lower triangle pulsation multiplier module three by the present invention Point.Being passed through the output Q that two of multiplier module inputs one are vector multiplier, one is the approximation knot of last loop iteration ReallyObtain after being multipliedOwing to the present invention completes two multiplications of matrices by pulsation matrix.Due to here Discuss is inverting of A Mi conjugate matrices, therefore all matrixes related in framework are all A Mi conjugate matrices, then herein Multiplication result be also A Mi conjugation, so we only need to complete lower triangle pulsation matrix, upper triangular portions is by being total to Yoke is obtained.On hardware architecture, lower triangle pulsation matrix multiplication module needs (1+M) M/2 adder, with (1+M) M/2 multiplication Device.Result by lower triangle pulsation matrix multiplication moduleIt is passed through coefficient adjustment module, strengthens system and correlated channels is fitted Should be able to power.Regulation coefficient a_kIt is positive integer or the negative integer times of 2, thus by moving to left or having moved to right co-efficient multiplication And do not introduce extra multiplier resources.Result after coefficient adjustment is passed through addition of matrices module realize Afterwards by continuous loop iteration, obtain exponent numberRequire until meeting.The hardware of addition of matrices module herein Complexity is M adder.

Detection module: complete the multiplying of general matrix and vector.Inverse matrix required by inputWith receive to AmountBoth are multiplied and obtain final detection resultThis is our result of linear detection to transmission vector s.Detection Module needs M multiplier, M adder.

The concrete shifting function of coefficient adjustment module is schematically as follows:

Data z

Sign bit

N position

N-1 position

N-2 position

…

2

1

0

z×2

Sign bit

N-1 position

N-2 position

N-3 position

…

1

0

" 0 " bit

z÷2

Sign bit

" sign bit "

N position

N-1 position

…

3

2

1

Use the linearity test side based on MIMO linearity test hardware architecture extensive under correlated channels that the present invention provides Method, comprises the following steps:

Step 1: channel response matrix H is sequentially input lower triangle pulsation multiplier module and adds the module generator matrix A that makes an uproar；Square Battle array A=(H^HH+σ²I_M), wherein, H is channel response matrix, σ²For noise variance, I_MFor unit battle array, (.)^HGrasp for conjugate transpose Make；

Step 2: module of being inverted by the elements in a main diagonal diagonal matrix of matrix A, described diagonal matrix module proposes to treat in matrix A Described diagonal matrix X is also inverted by the elements in a main diagonal composition matrix X, and the most each the elements in a main diagonal is asked down；By matrix X^-1It is input to Diagonal matrix addition module in vector multiplier and iteration module；

Step 3: step 2 is obtained inverse matrix X^-1It is multiplied with (A-X) Input matrix to vector multiplier and negates Operation, obtain matrix Q=-X^-1(A-X)；And the lower triangle pulsation multiplier module that matrix Q is input in iteration module will be obtained；

Step 4: in iteration module, main diagonal angle addition module, coefficient adjustment module and lower triangle pulsation multiplier module enter Row loop iteration, according to formulaObtain approximation inverse matrixWherein, k is Iterations, clock signal controls iterations, a_kFor the regulation coefficient in loop iteration, this coefficient is the positive integer of 2 or negative Integral multiple；

Step 5: the approximation inverse matrix that will obtain in step 4With receiveVector is input in detection module be multiplied (It is expressed as the wave filter output of y), obtain result of linear detection based on extensive MIMO, to transmission signal vectors Estimation

As shown in Figures 2 and 3, in the case of channel relevancy is relatively big, (ζ=0.3) (ζ=0.6) is of the present invention With the error code curve of coefficient control method compared to traditional simple leading diagonal Neumann's series scheme, with Cholesky decomposes the contrast of performance on inversion technique, it can be seen that the design can preferably complete similar with exact algorithm Performance, saves 34 iteration cycles compared to simple leading diagonal Neumann's series framework.Have a distinct increment in performance, Therefore add the system after channel coefficients adjusts and the adaptation ability of correlated channels has been had be obviously enhanced.In Fig. 2 with Fig. 3, the design adopts Formula be:

$A_k^{-1}=\underset{m=1}{\overset{k-1}{\Σ}}{(I-X^{-1}A)}^{m-1}{X}^{-1}+0.5\×{(I-X^{-1}A)}^{k-1}{X}^{-1}$

As shown in Figure 4, the iteration rule of data stream can be clearly seen from sequential chart, it is also possible to clearly calculate the present invention Time delay be (k+3) M+k-1, wherein k is iterations, and M is number of users.

Coefficient of the present invention adjustable Nuo Yiman approximately linear detection framework hardware complexity: lower triangle pulsation matrix multiplication mould Block consumes (1+M) M/2 adder, (1+M) M/2 multiplier；Add module of making an uproar and consume M adder；Diagonal matrix is inverted and is tabled look-up Can obtain；-X^-1E solves module and consumes M multiplier；Addition section in iteration module consumes M adder；In iteration module Multiplication part disappears (1+M) M/2 adder, with (1+M) M/2 multiplier；Coefficient adjustment module in iteration module is for moving to left Move to right realization, do not bring extra consumption；Detection module consumes M adder and M multiplier.Amount to M²+ 4M adder With M²+ 3M multiplier.

As shown in table 1, the hardware giving three kinds of schemes contrasts:

Table 1:

From hardware synthesis Comparative result table it can be seen that Neumann's series approximately linear detection framework ratio is based on accurately asking The linearity test that inverse Cholesky decomposes is framed in hardware resource occupancy and has higher advantage on maximum clock frequency. Due to the difference of iteration exponent number needed for Neumann's series under different scenes.The simple diagonal Nuo Yiman scheme that goes out carries with the design The coefficient adjustable type linearity test scheme gone out not what difference on hardware consumption, but what its algorithmic statement performance was brought Difference result in the difference of its throughput.

Such as Fig. 2, shown in Fig. 3, when channel coefficients ζ is 0.3, simple main diagonal angle framework needs five rank iteration and the adjustable structure of coefficient Frame needs three rank iteration；When channel coefficients ζ is 0.6, simple main diagonal angle framework needs nine rank iteration and the adjustable framework of coefficient to need Six rank iteration.Throughput when thus we provide channel situation change contrasts, as shown in table 2.

Table 2

According to above-mentioned throughput table it can be seen that when ζ=0.3, simple main diagonal angle framework throughput with classical framework Maintain an equal level, and the throughput advantage of framework of the present invention has manifested out；When ζ=0.6, simple main diagonal angle framework convergence capabilities Drastically declining, throughput is relatively low, and the present invention is framed on throughput maintaining an equal level with classical framework, but has huge excellent on hardware Gesture；When channel coefficients approaches 1, Neumann's series approximation is inverted and can not be restrained.It is contemplated that practical situation lower channel Worst situation can not be reached, so the demand that framework of the present invention can meet degree of accuracy has relatively low complexity simultaneously

The above is only the preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art For Yuan, under the premise without departing from the principles of the invention, it is also possible to make some improvements and modifications, these improvements and modifications also should It is considered as protection scope of the present invention.

Claims (10)

1. extensive MIMO linearity test hardware architecture under a correlated channels, it is characterised in that: include lower triangle pulsation multiplication Module, add module of making an uproar, diagonal matrix inverts module, vector multiplier, iteration module and detection module, described iteration module includes main Diagonal angle addition module, coefficient adjustment module, wherein:

Lower triangle pulsation multiplier module is for obtaining Wei Sha spy matrix G=H according to channel response matrix H^HH, and by this Wei Sha spy's square Battle array G=H^HH flows to add module of making an uproar；The knot exported for the matrix Q inputted according to vector multiplier and diagonal addition module ReallyObtainWhereinFor the iteration result once obtained before iteration module, and will obtainIt is input to Coefficient adjustment module；

Add module of making an uproar for accepting the noise variance σ of input², and by noise variance σ²It is added to the pulsation multiplier module input of lower triangle Wei Sha spy matrix G diagonal on try to achieve matrix A to be inverted, A=G+ σ²I_M=E+X, wherein matrix X is matrix A to be inverted The elements in a main diagonal diagonal matrix, E is the matrix deducting the elements in a main diagonal diagonal matrix in matrix A to be inverted, and H is channel response square Battle array, σ²For noise variance, I_MFor unit battle array, (.)^HFor conjugate transposition operation；And the elements in a main diagonal diagonal matrix X is sent to right Angle battle array is inverted module, and the matrix E deducting the elements in a main diagonal diagonal matrix in matrix A to be inverted is sent to vector multiplier；

Diagonal matrix module of inverting, for inverting the elements in a main diagonal diagonal matrix X, obtains the inverse square of the elements in a main diagonal diagonal matrix Battle array X^-1, and by inverse matrix X of the elements in a main diagonal diagonal matrix^-1The diagonal matrix being input in vector multiplier and iteration module adds Method module；

Vector multiplier is for according to matrix E and the leading diagonal unit deducting the elements in a main diagonal diagonal matrix in matrix A to be inverted Inverse matrix X of element diagonal matrix^-1It is multiplied and negates, obtaining matrix Q=-X^-1(A-X), wherein E=A-X, and matrix will be obtained The lower triangle pulsation multiplier module that Q is input in iteration module；

Coefficient adjustment module is for according to the pulsation multiplier module input of lower triangleIt is iterated coefficient adjustment, obtainsAnd willIt is pushed to main diagonal angle addition module；

Main diagonal angle addition module is for inverse matrix X according to the elements in a main diagonal diagonal matrix^-1WithObtainComplete An iteration, wherein:

K is iterations；

Then will obtainIt is pushed to lower triangle pulsation multiplier module and carries out next iteration, be pushed to detection module simultaneously Detect；

The wave filter output that detection module will receivePulse what multiplier module obtained with lower triangleIt is multiplied and obtains transmission letter Number vectorial estimation And then obtain result of linear detection based on extensive MIMO.

Extensive MIMO linearity test hardware architecture under correlated channels the most according to claim 1, it is characterised in that: described Iterations k is 2-6 time.

Extensive MIMO linearity test hardware architecture under correlated channels the most according to claim 1, it is characterised in that: described Lower triangle pulsation multiplier module includes (1+M) M/2 adder and (1+M) M/2 multiplier, and wherein M represents the quantity of user.

Extensive MIMO linearity test hardware architecture under correlated channels the most according to claim 1, it is characterised in that: described Vector multiplier M multiplier；Described main diagonal angle addition module includes M adder.

Extensive MIMO linearity test hardware architecture under correlated channels the most according to claim 1, it is characterised in that: described Detection module includes M multiplier, M adder.

Extensive MIMO linearity test hardware architecture under correlated channels the most according to claim 1, it is characterised in that: described Diagonal matrix addition module in iteration module is provided with depositor with lower triangle pulsation multiplier module, the diagonal matrix in iteration module The data of addition module output first leave in depositor, and lower triangle pulsation multiplier module is calling iteration module by depositor In diagonal matrix addition module output data.

7. a linearity test method for extensive MIMO linearity test hardware architecture under the correlated channels described in claim 1, It is characterized in that, comprise the following steps:

Step 1, obtains Wei Sha spy matrix G=H by lower for channel response matrix H input triangle pulsation multiplier module^HH；By Wei Sha spy's square Battle array G and noise variance σ²It is input to add module of making an uproar, adds module of making an uproar by noise σ²It is added on the diagonal of Wei Sha spy matrix G try to achieve treat Finding the inverse matrix A=G+ σ²I_M=E+X, wherein matrix X is the elements in a main diagonal diagonal matrix of matrix A to be inverted, and E is square to be inverted Deducting the matrix of the elements in a main diagonal diagonal matrix in battle array A, H is channel response matrix, σ²For noise variance, I_MFor unit battle array, (.)^H For conjugate transposition operation；

Step 2, the elements in a main diagonal diagonal matrix X is inverted by diagonal matrix module of inverting, and obtains the inverse of the elements in a main diagonal diagonal matrix Matrix X^-1, and by inverse matrix X of the elements in a main diagonal diagonal matrix^-1It is input to the diagonal matrix in vector multiplier and iteration module Addition module；

Step 3, obtains inverse matrix X by step 2^-1The elements in a main diagonal diagonal matrix is deducted with in the matrix A to be inverted of step 1 acquisition Matrix E be input in vector multiplier be multiplied and negate, obtain matrix Q=-X^-1(A-X), wherein E=A-X, and will obtain Obtain the lower triangle pulsation multiplier module that matrix Q is input in iteration module；

Step 4, matrix Q and diagonal addition module that described lower triangle pulsation multiplier module inputs according to vector multiplier export ResultObtainAnd will obtainIt is input to coefficient adjustment module；Coefficient adjustment module pairEnter Row coefficient adjusts, and obtainsAnd willIt is pushed to main diagonal angle addition module, wherein a_k-1For regulation coefficient； Main diagonal angle addition module is according to inverse matrix X of the elements in a main diagonal diagonal matrix^-1WithObtainWherein:

$A_k^{-1}=\left\{\begin{array}{cc}{X}^{-1}+a_{k-1}{\mathrm{QA}}_{k-1}^{-1},& k>1\\ X^{-1},& k=1\end{array}\right.;$

Then will obtainBeing pushed to lower triangle pulsation multiplier module respectively and carry out next iteration, k is iterations, with Time be pushed to detection module and detect；When iteration, control iterations by clock signal；

Step 5, the wave filter output that detection module will receivePulse what multiplier module obtained with lower triangleIt is multiplied and obtains The estimation of transmission signal vectors And then obtain result of linear detection based on extensive MIMO.

The linearity test method of extensive MIMO linearity test hardware architecture under correlated channels the most according to claim 1, It is characterized in that: the model of coefficient adjustment in described step 4:

$A_k^{-1}=\underset{m=1}{\overset{k-1}{\Σ}}{(I-X^{-1}A)}^{m-1}{X}^{-1}+0.5\×{(I-X^{-1}A)}^{k-1}{X}^{-1}.$

The linearity test method of extensive MIMO linearity test hardware architecture under correlated channels the most according to claim 1, It is characterized in that: described regulation coefficient a_k-1It is positive integer or the negative integer times of 2.

The linearity test method of extensive MIMO linearity test hardware architecture under correlated channels the most according to claim 1, It is characterized in that: the iterations in described step 4 is 2-6 time.