CN101442391B - Method for processing receiving terminal signal and apparatus for receiving signal - Google Patents

Abstract

The invention discloses a method for processing a signal in the receiving end, which is applied to a closed-loop MIMO-OFDM system. The method comprises the following steps: receiving a signal sent by more than one subchannel on the same subcarrier of the sending side; filtering the received signal; and converting color noise in the received signal into white noise. The invention also discloses a signal receiving device which is applied to the closed-loop MIMO-OFDM system; the device comprises a signal receiving module for receiving the signal sent by more than one subchannel on the same subcarrier of the sending side, and a filtering module for filtering the received signal and converting the color noise of the received signal into the white noise. Through the proposal, the method filters the received signals sent by a plurality of subchannels of the same subcarrier, converts the color noise of the received signal into the white noise, eliminates interference between a plurality of the subchannels caused by time delay of feedback and increases system gain.

Description

Method and signal receiving device that receiving end signal is processed

Technical field

The present invention relates to the signal processing technology field, particularly a kind of method and a kind of signal receiving device of receiving end signal processing.

Background technology

All adopt multiple-input, multiple-output (the Multiple Input Multiple Output of many antennas at transmitting terminal and receiving terminal, MIMO) the theoretical channel capacity of system increases with smaller value approximately linear in number of transmit antennas and the reception antenna number, and the mode that mimo system improves capacity is called space division multiplexing.Mimo system and OFDM (Orthogonal Frequency Division Multiplexing, OFDM) the MIMO-OFDM system that combines of technology, can effectively resist the multipath fading of wireless channel, be acknowledged as the most competitive technology of the 4th third-generation mobile communication.

Usually, the transmitting terminal of mimo system is not known channel condition information, yet, if transmitting terminal can be known channel condition information, then we can necessarily process transmitting at transmitting terminal, reduce and disturb, improve average received signal to noise ratio (Signal to Noise Ratio, SNR), thereby reduce average error sign ratio, we claim the precoding that is treated to of this transmitting terminal.

In a symmetrical time division duplex (Time Division Duplex, TDD) system, the channel information of up-downgoing channel can be shared mutually, in this case, does not need system configuration is done any change, and base station and travelling carriage can both adopt precoding.For asymmetric TDD system or Frequency Division Duplexing (FDD) (Frequency Division Duplex, FDD) system, receiving terminal need to be passed to transmitting terminal so that it knows descending channel information with channel information by a feedback channel.In a word, this mode by closed loop is so that transmitting terminal obtains channel information, thereby the mode that improves the mimo system capacity is one of study hotspot of present MIMO technology.

Under the mimo system of closed loop, do not consider feedback delay and feedback error, the conventional wave beam forming is to choose channel matrix eigenvalue of maximum characteristic of correspondence vector as launching beam moulding vector.This closed-loop MIMO system as shown in Figure 1, wherein, at transmitting terminal, feedback adjusting module 101 receives the channel information that receiving terminal feeds back to, after being the beam forming vector, according to this beam forming vector the transmitted signal direction is adjusted, then by transmitting antenna 102 emissions; At receiving terminal, after reception antenna 103 receives signal, in channel estimation module 104, receive whole OFDM symbol after, according to calculated signals channel matrix H corresponding to this OFDM symbol that receives, H is reception antenna number (W _r) * number of transmit antennas (W _t) complex matrix, feedback module 105 calculates the beam forming vector according to above-mentioned channel matrix H again, then feeds back to transmitting terminal.Wherein, the beam forming vector that calculates of feedback module 105 is matrix H ^HH has the characteristic vector of eigenvalue of maximum.By to matrix H ^HH carries out Eigenvalues Decomposition, can obtain: w _t=eigvec _Max[H ^HH]=μ ^Max, μ wherein ^MaxExpression H ^HThe eigenvalue of maximum characteristic of correspondence vector of H, this characteristic vector is that mould is 1 unit vector.

Concrete feedback principle is as follows:

Make that transmitted signal is d, at the multidimensional additive white Gaussian noise (AWGN) of receiving terminal be

And the covariance matrix of noise

Wherein Be unit matrix, N0 is original noise variance, makes μ ^MaxExpression H ^HThe eigenvalue of maximum characteristic of correspondence of H vector, then receiving terminal carries out to received signal high specific and merges that (the maximum weight vector that merges is: w _r=(Hw _t) ^H=(H μ ^Max) ^H) after, the decision signal that obtains can be expressed as:

$\hat{d}={\left({\mathrm{H\μ}}^{\max }\right)}^H{\mathrm{H\μ}}^{\max }d+{\left({\mathrm{H\μ}}^{\max }\right)}^{H}n$

$={\mathrm{\λ}}^{\max }{\left({\mathrm{\μ}}^{\max }\right)}^H{\mathrm{\μ}}^{\max }d+{\left({\mathrm{\μ}}^{\max }\right)}^H{H}^{H}n$

$={\mathrm{\λ}}^{\max }d+s^{\max }{U}^{\max }n---\left(1\right)$

Wherein, λ ^MaxFor with μ ^MaxCorresponding eigenvalue of maximum, s ^MaxAnd U ^MaxRepresent respectively H ^HIn the maximum singular value of H and the singular value decomposition (SVD) with μ ^MaxThose row of corresponding U battle array.

Then can draw, the signal to noise ratio of signal is:

$\mathrm{SNR}=\frac{E\left({\left({\mathrm{\λ}}^{\max }d\right)}^{*}\left({\mathrm{\λ}}^{\max }d\right)\right)}{E\left({\left(s^{\max }{U}^{\max }n\right)}^H\left(s^{\max }{U}^{\max }n\right)\right)}={\mathrm{\λ}}^{\max }\frac{E_s}{N_0}---\left(2\right)$

Since in the following formula, transmit signal energy and original noise variance N ₀Be definite value, so system performance gain is fully by the eigenvalue λ on the selected characteristic direction ^MaxDetermine.

Under the MIMO-OFDM system, maximum for guaranteeing system performance gain, receiving terminal is chosen N optimum direction in all characteristic direction the insides of all subcarriers, it is the characteristic vector direction of a corresponding N eigenvalue of maximum, and feed back to transmitting terminal, transmitting terminal with it as sending direction, namely can increase system gain, above-mentioned calculating process is also referred to as the optimal beam moulding algorithm under the MIMO-OFDM system, under the larger channel of the frequency selectivity that does not have feedback delay and mistake, the performance phase divided ring of optimal beam moulding (namely not adopting precoding) has very large gain.

But, have following shortcoming in the above-mentioned technology:

Receiving terminal at the closed-loop MIMO-ofdm system that has feedback processing, owing to just can calculate after channel matrix H need to be received whole OFDM symbol, then could calculate transmission beam forming vector and feed back to transmitting terminal according to channel matrix, so have at least an OFDM symbol lengths time T _SFeedback delay.If add frame structure, receiver Processing Algorithm and the needed time of transmitting feedback information of certain length, time of delay will be longer, so that there are the possibility that lost efficacy in the precoding vectors of receiving terminal feedback or matrix.Therefore, in the communication system of reality, must consider the impact of feedback delay.

Suppose to send with optimal direction on i the subcarrier, receiving so signal can be expressed as:

${\hat{d}}_i={\mathrm{\λ}}_i{d}_i+s_i{U}_{i}n---\left(3\right)$

If be the n moment this moment, corresponding channel matrix is H _i(n); When channel changes, become H _i(n+1) after, be equivalent to characteristic direction and characteristic value variation has all occured.The matrix character base of this moment just is not

And become

Equally, the characteristic value of this moment also becomes

And system still uses H this moment _i(n) ^HH _i(n) characteristic vector

As sending vector.Because Also be one group of base of this Linear Space, so we can be expressed as

That is, can regard as with

Projected to the New Characteristics vector

On.Wherein,

All be plural number, expression

On projection, and have

$c_i^{\mathrm{jk}}=\frac{{\left({\mathrm{\μ}}_i^j\right)}^H{\mathrm{\ω}}_i^k}{\left|\right|{\mathrm{\μ}}_i^j\left|\right|\left|\right|{\mathrm{\ω}}_i^k\left|\right|}---\left(4\right)$

Wherein || || expression is asked two norms to vector.Because

With

All be orthonormal basis, so

Therefore

${\hat{d}}_i={\left({\mathrm{\μ}}_i^j\right)}^H{H}_i{(n+1)}^H{H}_i(n+1){\mathrm{\μ}}_i^j{d}_j+{\left({\mathrm{\μ}}_i^j\right)}^H{H}_i{(n+1)}^{H}n$

$={\left(\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{c}_i^{\mathrm{jk}}{\mathrm{\ω}}_i^k\right)}^H{H}_i{(n+1)}^H{H}_i(n+1)\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{c}_i^{\mathrm{jk}}{\mathrm{\ω}}_i^k{d}_i+{\left({\mathrm{\μ}}_i^j\right)}^H{H}_i{(n+1)}^{H}n---\left(5\right)$

Then signal to noise ratio becomes

${\mathrm{SNR}}_i^j=\left(\underset{k}{\mathrm{\Σ}}{\left(c_i^{\mathrm{jk}}\right)}^H{c}_i^{\mathrm{jk}}{\mathrm{\η}}_i^j\right)\frac{E_s}{N_0}---\left(6\right)$

Can find out that gain is by λ _iBe reduced to

This is to cause because the launching beam forming direction no longer is the optimal energy direction of transfer of channel.If do not consider channel estimating, this gain reduction is unavoidable.Especially for optimal beam moulding algorithm, carry out beam forming owing to chosen two or more direction on some subcarrier, when not having feedback delay, the subchannel on these same subcarriers is orthogonal; After feedback delay had been arranged, they were orthogonality relations no longer just, must to interference effect is arranged each other, will have a strong impact on systematic function.Therefore, when having feedback delay and mistake, optimum beam forming algorithm performance descends very fast, even may be worse than open-loop performance.

Summary of the invention

In view of this, the invention provides method and a kind of receiving system that a kind of receiving end signal is processed, can improve systematic function.

The method that a kind of receiving end signal provided by the invention is processed is applied in the closed loop MIMO-OFDM MIMO-OFDM system, and the method comprises:

Reception is from the signal that sends more than a sub-channels on the same subcarrier of transmitting terminal;

Carry out channel estimating, obtain current channel matrix H (n+1);

To H (n+1) ^HH (n+1) carries out Eigenvalues Decomposition, obtains matrix character base { ω ₁, ω ₂..., ω _n;

With described matrix character base { ω ₁, ω ₂..., ω _nThe expression described reception signal transmit direction;

According to described transmit direction described reception signal is carried out high specific and merge, obtain decision signal;

Obtain equivalent MIMO vertical demixing time space V-BLAST model according to described decision signal, the noise in this model is An, and wherein A is the coloured noise factor, and n is the initial noise of receiving terminal;

Behind described coloured noise factors A transposition, carry out singular value decomposition, obtain: Wherein, S _EqBe A ^HThe singular value diagonal matrix, Be described filtering parameter, U ^EqBe A _HLeft singular matrix;

Multiply by described filtering parameter with described decision signal, the coloured noise in the described reception signal is converted to white noise.

A kind of receiving system provided by the invention is applied in closed-loop MIMO-ofdm system, and this device comprises:

Signal receiving module is used for receiving the signal that sends more than a sub-channels from the same subcarrier of transmitting terminal;

The filtering parameter determination module is used for that described reception signal is carried out high specific and merges, and obtains decision signal, and determines filtering parameter according to this decision signal;

Filtration module is used for according to described definite filtering parameter described reception signal being carried out filtering, and the coloured noise in the described reception signal is converted to white noise;

Comprise in the described filtering parameter determination module:

The channel estimating submodule is used for carrying out channel estimating, obtains current channel matrix H (n+1);

The Eigenvalues Decomposition submodule is used for H (n+1) ^HH (n+1) carries out Eigenvalues Decomposition, obtains matrix character base { ω ₁, ω ₂..., ω _n;

Transmit direction represents submodule, is used for adopting described matrix character base { ω ₁, ω ₂..., ω _nThe expression described reception signal transmit direction;

High specific merges submodule, is used for the transmit direction according to the described matrix character basis representation of described employing, the signal of described reception is carried out high specific merge, and obtains decision signal;

Equivalence vertical demixing time space V-BLAST model is processed submodule, is used for obtaining equivalent MIMO V-BLAST model according to described decision signal, and the noise in this model is An, and wherein A is the coloured noise factor, and n is the initial noise of receiving terminal;

The singular value decomposition submodule is used for behind the described coloured noise factors A transposition, carries out singular value decomposition, obtains Wherein, S _EqBe A ^HThe singular value diagonal matrix,

Be described filtering parameter, U _EqBe A ^HLeft singular matrix.

Can be found out by such scheme, the signal that among the present invention a plurality of subchannels on the same subcarrier that receives is sent carries out filtering, the coloured noise that receives in the signal is converted to white noise, eliminated in closed-loop MIMO-ofdm system because the interference between above-mentioned a plurality of subchannels that feedback delay causes, thereby increased system gain, improved systematic function.

Description of drawings

Fig. 1 is closed-loop MIMO system schematic diagram of the prior art;

Fig. 2 is the flow chart of the method specific embodiment of receiving end signal processing of the present invention;

Fig. 3 is concrete calculating and the filtering flow chart in the step 202 of Fig. 2;

Fig. 4 is a kind of better composition schematic diagram of receiving system specific embodiment of the present invention;

Fig. 5 to Figure 13 is for to carry out the result schematic diagram that emulation obtains to prior art and the present invention program.

Embodiment

For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing, the present invention is described in further detail by specific embodiment.

The flow process of method the first embodiment that receiving end signal of the present invention is processed as shown in Figure 2, this embodiment is applied in the receiver side of closed-loop MIMO-ofdm system, be used for solving because the signal interference problem that closed-loop system feedback delay and other delays cause comprises the steps:

Step 201, reception are from the signal more than sub-channels transmission on the same subcarrier of transmitting terminal.

Can find out that from this step present embodiment is mainly on same subcarrier, in a plurality of subchannels of different directions situation of transmitted signals respectively, solve the interference problem between a plurality of subchannels on the above-mentioned same subcarrier.

Step 202, the signal that receives is carried out filtering, the coloured noise that receives in the signal is converted to white noise.

In this step, can obtain equivalent MIMO vertical demixing time space (V-BLAST) model according to receiving signal, according to the coloured noise factor calculation of filtered parameter in this model.

In the present embodiment, can not need from all characteristic directions of all subcarriers, to choose optimum N sub-channels, from each subcarrier 2N sub-channels that 2 directions optimum and suboptimum consist of separately, choose optimum N sub-channels and only use, can send data by two sub-channels at most on the subcarrier of selecting like this, this shortcut calculation complexity is very low and performance loss is also very little.

Suppose in step 201 signal that receives be transmitting terminal on a subcarrier at μ ₁And μ ₂The signal that the subchannel of two different directions sends comprises d ₁And d ₂Wherein, μ ₁And μ ₂For to transmitting terminal to H (n) ^HH (n) carries out maximum and time large characteristic value characteristic of correspondence vector that Eigenvalues Decomposition obtains, and H (n) is at n channel matrix constantly.Then concrete calculating and filtering comprise the steps: as shown in Figure 3 in the above-mentioned steps 202

Step 2021, carry out channel estimating at receiving terminal, obtain current channel matrix H (n+1).

Because it is the n+1 moment that the moment of channel estimating is carried out in the impact of time delay, receiving terminal, so obtain current channel matrix H (n+1).

Step 2022, to H (n+1) ^HH (n+1) carries out Eigenvalues Decomposition, and the matrix character base that obtains is { ω ₁, ω ₂..., ω _n.

The direction of the matrix character basis representation transmitted signal that step 2023, usefulness solve:

Wherein, c _1jAnd c _2jRepresent respectively μ ₁And μ ₂At ω _jOn projection, its method for solving is seen formula (4).

Step 2024, according to above-mentioned transmit direction μ ₁And μ ₂The signal that receives is carried out high specific merge, obtain decision signal With

${\hat{d}}_1={\left({\mathrm{\μ}}_1\right)}^H{\left(H(n+1)\right)}^{H}H(n+1)({\mathrm{\μ}}_1{d}_1+{\mathrm{\μ}}_2{d}_2)+{\left({\mathrm{\μ}}_1\right)}^H{\left(H(n+1)\right)}^{H}n$

$=\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\mathrm{\&eta;}}_j{\left(c_{1j}\right)}^H{c}_{1j}{d}_1+\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\mathrm{\&eta;}}_j{\left(c_{1j}\right)}^H{c}_{2\mathrm{<figure-callout\; id="2"\; label="j\; d"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">j</figure-callout>}}{d}_{}{}_2+{\left(H(n+1){\mathrm{\&mu;}}_1\right)}^{H}n---\left(7\right)$

${\hat{d}}_2={\left({\mathrm{\&mu;}}_2\right)}^H{\left(H(n+1)\right)}^{H}H(n+1)({\mathrm{\&mu;}}_1{d}_1+{\mathrm{\&mu;}}_2{d}_2)+{\left({\mathrm{\&mu;}}_2\right)}^H{\left(H(n+1)\right)}^{H}n$

$=\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\mathrm{\&eta;}}_j{\left(c_{2j}\right)}^H{c}_{2\mathrm{<figure-callout\; id="2"\; label="j\; d"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">j</figure-callout>}}{d}_{}{}_2+\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\mathrm{\&eta;}}_j{\left(c_{2j}\right)}^H{c}_{1j}{d}_1+{\left(H(n+1){\mathrm{\&mu;}}_2\right)}^{H}n---\left(8\right)$

Wherein,

For

The characteristic of correspondence value, n is the initial noise of receiving terminal.

Step 2025, obtain the equivalent MIMO V-BLAST model of above-mentioned decision signal.

It is as follows to obtain 22 MIMO V-BLAST models of receiving of equivalence according to above-mentioned decision signal:

$y=\left(\begin{array}{c}{\hat{d}}_1\\ {\hat{d}}_2\end{array}\right)=H_{\mathrm{eq}}\left(\begin{array}{c}{d}_1\\ {\mathrm{<figure-callout\; id="2"\; label="d"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">d</figure-callout>}}_2\end{array}\right)+\left(\begin{array}{cc}{\left({\mathrm{\&mu;}}_1\right)}^H& {\left(H(n+1)\right)}^H\\ {\left({\mathrm{\&mu;}}_2\right)}^H& {\left(H(n+1)\right)}^H\end{array}\right)n=H_{\mathrm{eq}}s+\left(\begin{array}{c}a\\ b\end{array}\right)n=H_{\mathrm{eq}}s+\mathrm{An}---\left(9\right)$

Wherein, H _EqBe equivalent channel matrix, s is transmitted signal, and a, b are intermediate parameters, and A is the coloured noise factor, and n is the initial noise of receiving terminal.Wherein:

$H_{\mathrm{eq}}=\left(\begin{array}{cc}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|c_{1j}\left|\right|}^2{\mathrm{\&eta;}}_j& \underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(c_{1j}\right)}^{*}{c}_{2j}{\mathrm{\&eta;}}_j\\ \underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(c_{2j}\right)}^{*}{c}_{1j}{\mathrm{\&eta;}}_j& \underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|c_{1j}\left|\right|}^2{\mathrm{\&eta;}}_j\end{array}\right)---\left(10\right)$

$A=\left(\begin{array}{cc}{\left({\mathrm{\&mu;}}_1\right)}^H& {\left(H(n+1)\right)}^H\\ {\left({\mathrm{\&mu;}}_2\right)}^H& {\left(H(n+1)\right)}^H\end{array}\right)={\left(\begin{array}{cc}\left(H(n+1)\right){\mathrm{\&mu;}}_1& \left(H(n+1)\right){\mathrm{\&mu;}}_2\end{array}\right)}^H$

$={\left(H(n+1)\left(\begin{array}{cc}{\mathrm{\&mu;}}_1& {\mathrm{\&mu;}}_2\end{array}\right)\right)}^H---\left(11\right)$

Unite above-mentioned formula (7) to formula (11) and can get H _Eq=AA ^H

An asks covariance matrix to noise:

$E\left[{\mathrm{Ann}}^H{A}^H\right]=H_{\mathrm{eq}}{\mathrm{\&sigma;}}_n^2---\left(12\right)$

Wherein, Be original noise variance.Because H _EqBe not diagonal matrix, so this moment, the noise An in the equivalent model was coloured noise, this is not quadrature just because of two sub-channels on the above-mentioned subcarrier each other, causes interference effect is arranged each other.

Step 2026, the coloured noise factors A in the above-mentioned MIMO V-BLAST model is decomposed, obtain filtering parameter.

In this step, make B=A ^H, so just can obtain H _Eq=B ^HB does singular value decomposition (SVD) with B, obtains

$B=U_{\mathrm{eq}}{S}_{\mathrm{eq}}{V}_{\mathrm{eq}}^H$

$A=B^H=V_{\mathrm{eq}}{S}_{\mathrm{eq}}{U}_{\mathrm{eq}}^H---\left(13\right)$

Wherein, S _EqBe A ^HThe singular value diagonal matrix,

Be filtering parameter, U _EqBe A ^HLeft singular matrix.

Step 2027, according to above-mentioned filtering parameter

Signal d1 and the d2 that receives carried out filtering.

According to the computational process of step 2026, can obtain:

$H_{\mathrm{eq}}=B^{H}B=V_{\mathrm{eq}}{S}_{\mathrm{eq}}{U}_{\mathrm{eq}}^H{U}_{\mathrm{eq}}{S}_{\mathrm{eq}}{V}_{\mathrm{eq}}^H=V_{\mathrm{eq}}{D}_{\mathrm{eq}}{V}_{\mathrm{eq}}^H---\left(14\right)$

Wherein, D _Eq=S _EqS _Eq, be H _EqThe characteristic value diagonal matrix, bring the signal model that formula (9) obtains into;

$y=H_{\mathrm{eq}}s+\mathrm{An}$

$=V_{\mathrm{eq}}{D}_{\mathrm{eq}}{V}_{\mathrm{eq}}^{H}s+V_{\mathrm{eq}}{S}_{\mathrm{eq}}{U}_{\mathrm{eq}}^{H}n---\left(15\right)$

After this, signal is carried out filtering, namely be multiplied by simultaneously filtering parameter on the both sides of following formula

Obtain:

$V_{\mathrm{eq}}^{H}y=V_{\mathrm{eq}}^H{H}_{\mathrm{eq}}s+V_{\mathrm{eq}}^H\mathrm{An}$

$=V_{\mathrm{eq}}^H{V}_{\mathrm{eq}}{D}_{\mathrm{eq}}{V}_{\mathrm{eq}}^{H}s+V_{\mathrm{eq}}^H{V}_{\mathrm{eq}}{S}_{\mathrm{eq}}{u}_{\mathrm{eq}}^{H}n$

$=D_{\mathrm{eq}}{V}_{\mathrm{eq}}^{H}s+S_{\mathrm{eq}}{U}_{\mathrm{eq}}^{H}n---\left(16\right)$

Noise for this moment just has:

$E\left[V_{\mathrm{eq}}^H{\mathrm{Ann}}^H{A}^H{V}_{\mathrm{eq}}\right]$

$=E\left[V_{\mathrm{eq}}^H{\mathrm{AA}}^H{V}_{\mathrm{eq}}\right]{\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">n</figure-callout>}}^2=E\left[V_{\mathrm{eq}}^H{H}_{\mathrm{eq}}{V}_{\mathrm{eq}}\right]{\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">n</figure-callout>}}^2=E\left[V_{\mathrm{eq}}^H{V}_{\mathrm{eq}}{D}_{\mathrm{eq}}{V}_{\mathrm{eq}}^H{V}_{\mathrm{eq}}\right]{\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">n</figure-callout>}}^2$

$=D_{\mathrm{eq}}{\mathrm{\&sigma;}}_n^2---\left(17\right)$

Because D _EqBe diagonal matrix, then the noise of this moment has become white noise, has eliminated two sub-channels interferences, has offset because the system gain that feedback delay causes descends.

The signal model of this moment becomes:

$r=V_{\mathrm{eq}}^{H}y$

$=V_{\mathrm{eq}}^H{H}_{\mathrm{eq}}s+V_{\mathrm{eq}}^H\mathrm{An}$

$=H_{\mathrm{eqa}}s+w---\left(18\right)$

Wherein, H _EqaBe filtered equivalent channel matrix, W is filtered noise.

In the present embodiment, by the processing of step 202, with after receiving coloured noise in the signal and becoming white noise, can also carry out soft output detections to signal, specifically comprise:

At first, the soft output of V-BLAST system can be expressed as:

${\mathrm{\&Lambda;}}_k^{\left(i\right)}\&ap;\frac{1}{{\mathrm{\&sigma;}}_w^2}[\underset{d:d_k\&Element;D_0^i}{\min }{\left|\right|\hat{s}-H_{\mathrm{eqa}}d\left|\right|}^2-\underset{d:d_k\&Element;D_1^i}{\min }{\left|\right|\hat{s}-H_{\mathrm{eqa}}d\left|\right|}^2]---\left(19\right)$

Wherein, E _SBe transmit signal energy, the variance of the noise W in the formula (18)

In the formula (19), in the situation that adopts least mean-square error (MMSE) decoding to detect, maximum singular value

Wherein MMSE detects matrix

Then list of references is adopted in above-mentioned soft output: Dominik Seethaler, Gerald Matz, " An Efficient MMSE-Based Demodulator for MIMO Bit-Interleaved Coded Modulation " IEEE Communications Society Globecom 2004, the algorithm that provides, for:

${\mathrm{\&Lambda;}}_{k,\mathrm{MMSE}}^{\left(i\right)}=\frac{W_{k,k}}{1-W_{k,k}}[\underset{d\&Element;D_0^i}{\min }{\mathrm{\&psi;}}_k^2\left(d\right)-\underset{d\&Element;D_1^i}{\min }{\mathrm{\&psi;}}_k^2\left(d\right)]---\left(20\right)$

Wherein, $W_{k,k}={(I+D_{\mathrm{eq}}{\mathrm{\&sigma;}}_n^2{\left(H_{\mathrm{eq}}^{H}H\right)}^{-1})}^{-1},$ ${\mathrm{\&psi;}}_k^2\left(d\right)=|\frac{{\hat{s}}_k}{W_{k,k}}-d|,$ Subscript k represents subcarrier, the soft output of i bit in subscript (i) the expression estimate symbol,

Transmit i bit in the set of expression is 0 assemble of symbol,

Transmit i bit in the set of expression is 1 assemble of symbol.

In the formula (19), in the situation that adopts maximum likelihood (ML) decoding to detect, soft output can be expressed as:

${\mathrm{\&Lambda;}}_k^{\left(i\right)}\&ap;\frac{1}{{\mathrm{\&sigma;}}_{w,k}^2}[\underset{d:d_k\&Element;D_0^i}{\min }{\hat{s}-H_{\mathrm{eqa}}d\left|\right|}^2-\underset{d:d_k\&Element;D_1^i}{\min }{\left|\right|\hat{s}-H_{\mathrm{eqa}}d\left|\right|}^2]---\left(21\right)$

Wherein, ${\mathrm{\&sigma;}}_{w,\mathrm{<figure-callout\; id="2"\; label="k"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">k</figure-callout>}}^2={\left(D_{\mathrm{eq}}{\mathrm{\&sigma;}}_n^2\right)}_{k,k}.$

Soft output detections in the formula (19) also can adopt ZF (ZF) algorithm to carry out, because this algorithm is conventionally known to one of skill in the art, no longer illustrates here.

In the above part of present embodiment, the reception signal of the subcarrier that uses two sub-channels is calculated, and for the subcarrier that has only used a sub-channels, receiving signal can be expressed as:

$\hat{d}={\left(\mathrm{\&mu;}\right)}^H{\left(H(n+1)\right)}^{H}H{(n+1)}^{\&prime;}\mathrm{\&mu;d}+{\left(\mathrm{\&mu;}\right)}^H{\left(H(n+1)\right)}^{H}n$

$=\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{\mathrm{\&eta;}}_j{\left(c_{1j}\right)}^{*}{c}_{1j}d+{\left(\mathrm{\&mu;}\right)}^H{\left(H(n+1)\right)}^{H}n$

$=h_{\mathrm{eq}}d+w---\left(22\right)$

After the log-likelihood ratio of i position was used approximate formula, can obtain soft being output as:

${\mathrm{\&Lambda;}}^{\left(i\right)}\&ap;\frac{1}{{\mathrm{\&sigma;}}_w^2}[\underset{d\&Element;D_0^i}{\min }{\left|\right|r-h_{\mathrm{eq}}d\left|\right|}^2-\underset{d\&Element;D_1^i}{\min }{\left|\right|r-h_{\mathrm{eq}}d\left|\right|}^2]---\left(23\right)$

Wherein, r receives signal, h _EqBe the equivalent channel coefficient, d transmits, in addition, in the following formula

Draw by following formula:

${\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">w</figure-callout>}}^2=E\left[{\mathrm{ww}}^H\right]=E\left[{\left(\mathrm{\&mu;}\right)}^H{\left(H(n+1)\right)}^H{\mathrm{nn}}^{H}H(n+1)\mathrm{\&mu;}\right]$

$={\left(\mathrm{\&mu;}\right)}^H{\left(H(n+1)\right)}^{H}H(n+1)\mathrm{\&mu;}{\mathrm{\&sigma;}}_n^2---\left(24\right)$

$={\mathrm{\&alpha;\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="S2007101878494E00021.png,S2007101878494E00031.png"\; state="\{\{state\}\}">n</figure-callout>}}^2$

In the present embodiment, after above-mentioned steps 2021, because the MIMO-OFDM system is closed-loop system, the channel matrix that obtains according to channel estimating at receiving terminal sends the calculating of beam forming vector, and the transmission beam forming vector that will calculate feeds back to transmitting terminal.In this case, although system still exists feedback delay and other delays, but because in the signal processing method of the receiving terminal that the embodiment of the invention provides, by filtering the interference signal that is caused by feedback delay and other delays is eliminated, eliminate the impact of above-mentioned delay, improved system performance gain.

Below again the specific embodiment of signal receiving device of the present invention is described.

The signal receiving device specific embodiment is applied in the MIMO-OFDM system of closed loop, is used for solving the signal interference problem that causes owing to closed-loop system feedback delay and other delays, and this signal receiving device specifically comprises:

Signal receiving module is used for receiving the signal that sends more than a sub-channels from the same subcarrier of transmitter side.

Filtration module is used for the signal that receives is carried out filtering, and the coloured noise that receives in the signal is converted to white noise.

Preferably, further can comprise in this device:

Soft demodulation module is used for described filtered signal is carried out soft output detections.

Preferably, further also can comprise in this device:

The filtering parameter determination module is used for that the signal that receives is carried out high specific and merges, and obtains decision signal, and determines filtering parameter according to this decision signal;

Above-mentioned filtration module carries out filtering according to described definite filtering parameter.

Preferably, comprise in the above-mentioned filtering parameter determination module:

High specific merges submodule, is used for that the signal that receives is carried out high specific and merges, and obtains decision signal;

Equivalence V-BLAST model is processed submodule, is used for obtaining equivalent MIMO V-BLAST model according to described judgement channel, and the noise in this model is An, and wherein A is the coloured noise factor, and n is the initial noise of receiving terminal;

The singular value decomposition submodule is used for behind the described coloured noise factors A transposition, carries out singular value decomposition, obtains

Wherein, S _EqBe A ^HThe singular value diagonal matrix,

Be described definite filtering parameter, U _EqBe A ^HLeft singular matrix.

Preferably, further comprise in the filtering parameter determination module described above:

The channel estimating submodule is used for carrying out channel estimating at receiving terminal, obtains current channel matrix H (n+1);

The Eigenvalues Decomposition submodule is used for H (n+1) ^HH (n+1) carries out Eigenvalues Decomposition, obtains matrix character base { ω ₁, ω ₂..., ω _n;

Transmit direction represents submodule, is used for adopting described matrix character base { ω ₁, ω ₂..., ω _nThe expression described reception signal transmit direction;

Then above-mentioned high specific merging submodule carries out the high specific merging to received signal according to described transmit direction.

Further comprise in the present embodiment:

Feedback module, the channel matrix H (n+1) that is used for obtaining according to above-mentioned channel estimating is calculated and is sent the beam forming vector, and should send the beam forming vector and feed back to transmitting terminal, so that can sending the beam forming vector according to this, transmitting terminal adjusts the signal transmit direction.

A kind of better implementation of signal receiving device specific embodiment as shown in Figure 4, wherein the annexation between the modules repeats no more here in above-mentioned existing explanation.

In order to prove that the present invention improves the technique effect of system gain, the inventor has carried out emulation experiment, and the environment of this experiment and the parameter that relates to are:

Under the channel of the bad city, 6 footpaths of COST207, carry out, wherein the concrete setting for the 6 footpath bad city channels of COST207 please see this reference paper for details: G.L.St ü ber, Priciples of Mobile Communication, 2nd ed.Norwell, MA:Kluwer, 2001.; The sub-carrier number of ofdm signal chooses 1024, i.e. N _IFFT=1024, cyclic prefix type during is 1/4, i.e. CP=256, and transmitted signal is taked the BPSK modulation, and signal bandwidth is 10M, and has taked ten OFDM symbol time length feedback delays; Chnnel coding adopts the convolution code of 3GPP LTE (1,2,9), and the octal system generator polynomial is (561,753); Weaving length is 10 OFDM symbols; Receiving terminal viterbi decoder decoding depth is 45; Antenna configuration is 22 receipts.

As shown in drawings, Fig. 5, Fig. 6 and Fig. 7 are the performances that receiving terminal adopts BPSK, QPSK and three kinds of different modulating modes of 16-QAM under the translational speed of 3km/h; Fig. 8, Fig. 9 and Figure 10 are the performance map of above-mentioned three kinds of modulation systems under 20km/h; Figure 11, Figure 12 and Figure 13 then are the performances of above-mentioned three kinds of modulation systems under 40km/h.Adopted MMSE and ML to carry out soft Output simulation.

In each accompanying drawing, traditional scheme refers to that each subcarrier selects the optimal direction of oneself to send separately.This scheme is simple in structure at receiver end, but when delay is arranged when, can accurately not calculate the attenuation coefficient of signal, and this BPSK and QPSK for hard decision the time does not affect, but that the 16-QAM of hard decision will demodulation occur is inaccurate.And after we add chnnel coding, no matter be 16-QAM or the modulation of simple phase modulation, soft output all can be affected.For the present invention program, then be to have selected in all subcarriers, a best N direction sends, and simultaneously, because receiving terminal has increased the consideration to time delay, has avoided self-interference on the same subcarrier.Scheme 1 is to have the subcarrier of having chosen two sub-channels to take the scheme of MMSE filtering to those, and scheme 2 then is these subcarriers to be taked the scheme of maximum likelihood filtering.

From Fig. 5 to simulation result shown in Figure 13, we can find out, in low speed (3km/h, 20km/h), adopt the solution of the present invention, and there is comparatively significantly gain in system, and particularly after modulation system uprised, it was more obvious to gain.This be because, according to traditional algorithm of not considering feedback delay, when feedback delay occurring, can't accurately estimate the equivalent model of signal, this error is when the modulation of high-order more, impact can be more serious.And the present invention program can estimate the model of signal accurately, and performance just can fast-descending not occur with the rising of order of modulation.

In at a high speed (40km/h), at this moment the channel condition information owing to the closed loop that obtains at transmitting terminal is very inaccurate, and systematic function descends, but the present invention program still has certain gain than traditional scheme, and also be that order of modulation is higher, it is larger to gain.

In addition, we it can also be seen that as a result from above, when carrying out soft output detections, adopt the MMSE algorithm to compare with the ML algorithm, performance difference is also little, this is because only having subcarrier few in number is to have used 2 sub-channels to transmit, and only for these subcarriers, just can have ML algorithm and MMSE Algorithm Performance difference.So last the embodiment when arriving the whole system performance, it is very little that difference becomes.That is to say, when we carry out soft output with the MMSE algorithm, just can reach good performance, and needn't adopt the ML algorithm of high complexity.

More than be the explanation to the specific embodiment of the invention, in concrete implementation process, can be improved appropriately method of the present invention, to adapt to the concrete needs of concrete condition.Therefore be appreciated that according to the specific embodiment of the present invention just to play an exemplary role, not in order to limit protection scope of the present invention.

Claims (5)

1. the method that receiving end signal is processed is applied to it is characterized in that in the closed loop MIMO-OFDM MIMO-OFDM system, comprising:

Reception is from the signal that sends more than a sub-channels on the same subcarrier of transmitting terminal;

Carry out channel estimating, obtain current channel matrix H (n+1);

To H (n+1) ^HH (n+1) carries out Eigenvalues Decomposition, obtains matrix character base { ω ₁, ω ₂..., ω _n;

With described matrix character base { ω ₁, ω ₂..., ω _nThe expression described reception signal transmit direction;

According to described transmit direction described reception signal is carried out high specific and merge, obtain decision signal;

Obtain equivalent MIMO vertical demixing time space V-BLAST model according to described decision signal, the noise in this model is An, and wherein A is the coloured noise factor, and n is the initial noise of receiving terminal;

Behind described coloured noise factors A conjugate transpose, carry out singular value decomposition, obtain:

Wherein, S _EqBe A ^HThe singular value diagonal matrix,

Be filtering parameter, U _EqBe A ^HLeft singular matrix;

Multiply by described filtering parameter with described decision signal, the coloured noise in the described reception signal is converted to white noise.

2. method according to claim 1 is characterized in that, the coloured noise in the described reception signal is converted to white noise after, the method further comprises: described filtered signal is carried out soft output detections.

3. method according to claim 2 is characterized in that, described soft output detections adopts ZF ZF algorithm, least mean-square error MMSE algorithm or maximum likelihood ML algorithm.

4. signal receiving device is applied to it is characterized in that in closed-loop MIMO-ofdm system that this device comprises:

Signal receiving module is used for receiving the signal that sends more than a sub-channels from the same subcarrier of transmitting terminal;

The filtering parameter determination module is used for that described reception signal is carried out high specific and merges, and obtains decision signal, and determines filtering parameter according to this decision signal;

Filtration module is used for according to described definite filtering parameter described reception signal being carried out filtering, and the coloured noise in the described reception signal is converted to white noise;

Comprise in the described filtering parameter determination module:

The channel estimating submodule is used for carrying out channel estimating, obtains current channel matrix H (n+1);

The Eigenvalues Decomposition submodule is used for H (n+1) ^HH (n+1) carries out Eigenvalues Decomposition, obtains matrix character base { ω ₁, ω ₂..., ω _n;

Transmit direction represents submodule, is used for adopting described matrix character base { ω ₁, ω ₂..., ω _nThe expression described reception signal transmit direction;

High specific merges submodule, is used for the transmit direction according to the described matrix character basis representation of described employing, the signal of described reception is carried out high specific merge, and obtains decision signal;

Equivalence vertical demixing time space V-BLAST model is processed submodule, is used for obtaining equivalent MIMO V-BLAST model according to described decision signal, and the noise in this model is An, and wherein A is the coloured noise factor, and n is the initial noise of receiving terminal;

The singular value decomposition submodule is used for behind the described coloured noise factors A conjugate transpose, carries out singular value decomposition, obtains

Wherein, S _EqBe A ^HThe singular value diagonal matrix,

Be described filtering parameter, U _EqBe A ^HLeft singular matrix.

5. device according to claim 4 is characterized in that, further comprises in this device:

Soft demodulation module is used for described filtered signal is carried out soft output detections.

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