CN103368700B - The Delay-dependent space-time code mode blind identification of feature based amount pre-estimation - Google Patents

Abstract

The Delay-dependent space-time code mode blind identification of a kind of feature based amount pre-estimation of disclosure.Implementation step is: (1) extracts space-time code set, obtains characteristic quantity set；(2) calculate incidence matrix, and calculate characteristic quantity functional value vector；(3) utilize characteristic quantity functional value vector pre-estimation characteristic quantity, obtain new space-time code set and noise power estimation value；(4) albefaction incidence matrix, obtains decorrelation matrix；(5) the relevant norm of time lag of decorrelation matrix is calculated；(6) the time lag correlation matrix of pattern in new space-time code set is calculated；(7) parameter in step (5) and (6) is utilized to calculate time lag relevance vector；(8) pattern corresponding to element that in time lag relevance vector, value is minimum is taken for judgement pattern.Instant invention overcomes prior art and cause greatly the shortcoming that system complexity is high due to operand, increase the identification range of existing space-time code blind-identification method, can be used for the space-time code pattern that blind recognition transmitting terminal uses.

Description

The Delay-dependent space-time code mode blind identification of feature based amount pre-estimation

Technical field

The invention belongs to communication technical field, further relate to the Space-Time Block Coding coding mode blind-identification method in signal detection technique field during sky, can be used for, in multiple-input and multiple-output mimo system, Space-Time Block Coding being carried out blind recognition.

Background technology

Mimo system is the key technology of next generation wireless communication, and space-time code is the important component part of mimo system.The blind recognition of space-time code is the field in the urgent need to research, the communication countermeasure field, and it can provide basis and technical support for mimo system countermeasure techniques, has important theory significance and application value in army, has caused the concern of academia.

The blind recognition of space-time code is an emerging problem, and existing algorithm is divided into maximum likelihood algorithm and time lag related algorithm.Maximum likelihood algorithm construction of function is simple, and computation complexity is low, but it is for the block length pattern None-identified identical with group separator count, and None-identified goes out BLAST coding mode when transmission antenna number is equal with reception antenna number；The discernible pattern of time lag related algorithm is more, but its computation complexity increases along with sampling number exponentially, is dfficult to apply to real-time detection in practice.

Document [1V.Choqueuse, M.Marazinetal., Blindrecognitionoflinearspacetimeblockcodes:Alikelihoodb asedapproach.IEEETrans.SignalProcessing, 58 (3), 2010,1290-1299] in propose code parameter detecting algorithm belong to maximum likelihood algorithm.According to maximum-likelihood criterion to all Candidate Set patterns, construct only relevant with coding parameter likelihood function, by comparing the likelihood function of different coding pattern, coding parameter is made judgement, and then judges pattern.The method decision function simple structure, computation complexity is low, is widely used in MIMO blind recognition system.But when the method carries out MIMO detection in engineering practice, the deficiency existed is: to the multiple coding mode None-identified having same packets length and being often grouped internal symbol number, when reception antenna number is equal to transmission antenna number, None-identified goes out BLAST coding mode.

Document [2V.Choqueuse, K.Yaoetal., Blindrecognitionoflinearspacetimeblockcodes.IEEEInt.Conf .Acoust.SpeechSignalProcess, 2008,2833-2836] in propose Decision Classfication detection algorithm belong to time lag related algorithm.Its diversity according to Frobenius norm under different delay of the correlation matrix of different Space-Time Block Codings, adopts and contrasts step by step, it is achieved the blind recognition to Space-Time Block Coding.Owing to the discernible pattern of the method is relatively wide, and the detection performance of orthogonal space time packet is very superior, therefore in MIMO detects, have also been obtained certain application.But the method there is also a lot of deficiencies in MIMO blind recognition system: the multiple pattern with identical F Norm Solution be cannot be distinguished by, and computation complexity becomes geometry multiple to increase along with sampling time length.

Summary of the invention

Present invention aims to the deficiency of above-mentioned prior art, it is proposed to the Delay-dependent space-time code mode blind identification of a kind of feature based amount pre-estimation, to improve space-time code identification of code type scope, reduce the complexity calculated.

Realizing the object of the invention ground technical thought is: by adopting characteristic quantity pre-estimation technology, multipath reception signal carries out grouping feature amount pre-estimation, utilizes the grouping feature amount estimated, reduce space-time code set；In conjunction with noise power estimation, take and reduce the pattern making time lag degree of association minimum in space-time code set for adjudicating pattern.Concrete scheme comprises the steps:

1) receiving terminal receives, by r root reception antenna, the signal sequence that length is N that transmitting terminal sends, and obtains the reception signal matrix R' of r × N；

2) utilize the space-time code composition pattern set omega that be there is a need to identify, take combination (f, g) constitutive characteristic duration set (U, the V) of the group separator count f and block length g of every kind of pattern in Ω；In note characteristic quantity set (U, V), ith feature amount is combined as (s_i,k_i), i=1,2... Ζ, Ζ are number of combinations in characteristic quantity set (U, V)；

3) real part and the imaginary part parallel connection of signal matrix R' will be received, it is thus achieved that incidence matrixNamely

$\tilde{R}=\left[\begin{array}{c}\mathrm{Re}\left(R^{\′}\right)\\ \mathrm{Im}\left(R^{\′}\right)\end{array}\right]$

Wherein Re () represents the computing for the treatment of excess syndrome portion, and Im () expression takes imaginary-part operation；

4) characteristic quantity functional value is calculated

4a) (s is combined for the ith feature amount in characteristic quantity set (U, V)_i,k_i), structure packet correlation matrix R_i:

$R_i=\left[\begin{array}{cccc}\tilde{R}\left(1\right)& \tilde{R}(k_i+1)& \mathrm{...}& \tilde{R}\left(\right(\frac{N}{k_i}-1)k_i+1)\\ \tilde{R}\left(2\right)& \tilde{R}(k_i+2)& \mathrm{...}& \tilde{R}\left(\right(\frac{N}{k_i}-1)k_i+2)\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ \tilde{R}\left(k_i\right)& \tilde{R}\left(2k_i\right)& \mathrm{...}& \tilde{R}\left(\right(\frac{N}{k_i}\left)k_i\right)\end{array}\right]$

WhereinRepresent incidence matrixJth row, j=1,2...N；

4b) calculate packet correlation matrix R_iPacket covariance matrix: C_i=E [R_iR_i ^T], wherein expectation computing is sought in E [] expression, ()^TRepresent transposition computing；

4c) to packet covariance matrix C_iDo Eigenvalues Decomposition, the eigenvalue obtained is arranged in descending order, constitutive characteristic value vectorTake feature value vectorFront 2s_iIndividual eigenvalue constitutes validity feature value vectorWith residue character value Q_i=2rk_i-2s_iConstitute noise characteristic value vectorNamely ${\stackrel{\→}{\mathrm{\λ}}}_{i2}=\[{\mathrm{\ρ}}_{2s_i+1},{\mathrm{\ρ}}_{2s_i+2},...,{\mathrm{\ρ}}_{2{\mathrm{rk}}_i}\];$

4d) estimate characteristic quantity combination (s_i,k_i) corresponding noise power

${\mathrm{\σ}}_i^2=\sqrt[Q_i]{\Π{\stackrel{\→}{\mathrm{\λ}}}_{i2}}$

Wherein ∏ () represents that vector element connects multiplication,Represent and open Q_iPower computing；

4e) obtain characteristic quantity combination (s_i,k_i) characteristic of correspondence flow function value M (s_i,k_i):

$M(s_i,k_i)=\frac{N}{k_i}\left(log\right(\frac{\sqrt{\Π\stackrel{\→}{{\mathrm{\λ}}_{i1}}}}{{{\mathrm{\σ}}_i}^{2Q_i}}\left)\right);$

5) pre-estimation characteristic quantity, obtains new space-time code set Ω ':

5a) every kind of combination in characteristic quantity set (U, V) is repeated step 4, obtain every kind of combination characteristic of correspondence flow function value, composition characteristic flow function value vector: Φ=[M (s₁,k₁),M(s₂,k₂)...M(s_m,k_m) ...], wherein, m=1,2 ... Ζ；

5b) find out the element characteristic of correspondence amount combination that in characteristic quantity functional value vector Φ, numerical value is minimumAccording to step 4d) show that characteristic quantity combinesCorresponding noise power estimation value is

5c) taking out block length in space-time code set Ω isGroup separator count isAll patterns, constitute new space-time code set Ω '；

6) whitening matrix receiving signal is calculated:

6a) calculate incidence matrixCovariance matrixAnd integrating step 5b) in noise power estimation valueObtain removing the timesharing correlation matrix P of influence of noise:

Wherein I_2rRepresenting the unit matrix being sized to 2r*2r, * represents multiplying；

6b) timesharing correlation matrix P is carried out Eigenvalues Decomposition, the eigenvalue obtained is arranged in descending order, as the diagonal element of eigenvalue matrix Λ, obtain eigenvalue matrix Λ characteristic of correspondence vector matrix U:

U=P Λ P^-1,

Wherein, P^-1For the inverse matrix of timesharing correlation matrix P,ν_jFor the eigenvalue of timesharing correlation matrix P, j=1,2 ... 2r；

Above-mentioned parameter 6c) is utilized to calculate whitening matrix:Wherein Λ^-1Generalized inverse matrix for eigenvalue matrix Λ；

7) to incidence matrixCarry out albefaction, it is thus achieved that the decorrelation matrix after albefaction:

8) the relevant norm of time lag of decorrelation matrix Y is calculated

$\left|\right|\widetilde{\mathrm{\θ}}\left(\mathrm{\τ}\right)|{|}_F^2=\frac{1}{N^2}|\left|\underset{b=0}{\overset{N-1}{\Σ}}Y\left(b\right)Y{\left(\left(b+\mathrm{\τ}\right)\mathrm{mod}N\right)}^T\right|{|}_F^2,$

Wherein, τ=0,1, N, Y (b) is the b row of decorrelation matrix Y, b=0,1, N-1, mod is modulo operation, | | | |_F ²The Frobenius norm of representing matrix；

9) time lag correlation matrix θ (τ) of pattern in new space-time code set Ω ' is calculated:

$\mathrm{\θ}\left(\mathrm{\τ}\right)=\underset{u=0}{\overset{N-\hat{k}-1}{\Σ}}\left[\begin{array}{cc}\mathrm{Re}\left(A\right(u\left)\right)\×\mathrm{Re}{\left(A\right(u+\mathrm{\τ}\left)\right)}^T& 0\\ 0& \mathrm{Im}\left(A\right(u\left)\right)\×\mathrm{Im}{\left(A\right(u+\mathrm{\τ}\left)\right)}^T\end{array}\right],$

Wherein, A (u) is the u row of the encoder matrix A of pattern in new space-time code set Ω ', u=1,2 ... ω, ω are pattern block length；

10) judgement pattern is obtained:

Above-mentioned parameter 10a) is utilized to calculate the time lag degree of association d of pattern in new space-time code set Ω ':

$d=\underset{\mathrm{\τ}=1}{\overset{N-1}{\Σ}}{\left(\right|\left|\widetilde{\mathrm{\θ}}\right(\mathrm{\τ}\left)\right|{|}_F^2-2t*\frac{\left|\right|\mathrm{\θ}\left(\mathrm{\τ}\right)|{|}_F^2}{\left|\right|\mathrm{\θ}\left(0\right)|{|}_F^2})}^2,$

Wherein, t is transmission antenna number；

10b) repeat step 10a), obtain the time lag degree of association that in new space-time code set Ω ', every kind of pattern is corresponding, constitute time lag relevance vector Δ=[d (1), d (2), ... d (l)], wherein l is pattern number in new space-time code set Ω ', the pattern that element that in the stagnant relevance vector Δ that clocks, value is minimum is correspondingFor judgement pattern.

The present invention compared with prior art has the advantage that

First, owing to present invention employs characteristic quantity pre-estimation technology, according to multipath reception signal pre-estimation grouping feature amount, it is effectively reduced space-time code set, the pattern distinguished by calculating the relevant norm of time lag is significantly reduced, overcome the shortcoming that the system implementation complexity in time lag related algorithm, the relevant norm calculation amount of the time lag of space-time code caused greatly is high, make the complexity that the present invention realizes substantially reduce.

Second, due to the method that present invention employs the norm cascade detection relevant with receiving signal time lag of grouping feature amount, overcome time lag related algorithm in prior art and not can recognise that the pattern with the relevant norm of identical time lag, maximum likelihood function algorithm not can recognise that the deficiency with same packets length and class symbol number type so that invention increases the identification range of space-time code.

Accompanying drawing explanation

Fig. 1 is the system block diagram that the present invention adopts；

Fig. 2 is the flowchart of the present invention；

Fig. 3 is with present invention recognition correct rate figure under different parameters；

Fig. 4 is the recognition correct rate comparison diagram of the present invention and existing two kinds of blind-identification methods.

Detailed description of the invention

With reference to Fig. 1, the system that the present invention relies on includes: 4 transmitting antennas, 8 or 6 reception antennas, modulation system is 4QAM.At transmitting terminal, be converted to transmitted in parallel sequence after serial transmission sequence is space-time encoded, then send after Parallel Sequence modulation.At receiving terminal, reception signal matrix is R':R'=HX+B, and wherein, R' is for receiving signal matrix, and H is the channel matrix obeying multiple Gauss distribution of element independence, and X is the information sequence launched, and B is white Gaussian noise matrix.

The present invention is exactly that blind recognition goes out the space-time code pattern that transmitting terminal uses according to receiving signal matrix R'.

With reference to Fig. 2, the present invention is implemented as follows:

Step 1, it is thus achieved that receive signal matrix R':

Receiving terminal receives, by r root reception antenna, the signal sequence that length is N that transmitting terminal sends, and obtains the reception signal matrix R' of r × N, r=8 or r=6, N=1024 or N=512 in this example；

Step 2, obtains characteristic quantity set (U, V):

2a) utilize the space-time code composition space-time code set Ω that be there is a need to identify, space-time code set omega={ BLAST (4 in this example, 1), Tarokh-OSTBC (4, 8), Ganesan-OSTBC (3, 4), QOSTBC (4, 4), STBC (4, 4), STBC (8, 4), STBC (4, 2), STBC (6, 2) }, wherein, BLAST is hierarchical space-time code, OSTBC is orthogonal space time packet, QOSTBC is quasi-orthogonal space time block code, STBC is non-orthogonal space-time block, bracket after space-time code represents the combination (f of the group separator count f and block length g of pattern, g)；

2b) take combination (f, g) constitutive characteristic duration set (U, V), the characteristic quantity set (U in this example of the group separator count f and block length g of every kind of pattern in space-time code set Ω, V)={ (4,1), (4,8), (3,4), (4,4), (8,4), (4,2), (6,2) }, in note characteristic quantity set (U, V), ith feature amount is combined as (s_i,k_i), i=1,2...Z, Z is number of combinations in characteristic quantity set, takes Z=7 in this example, characteristic quantity combination (s_i,k_i) a kind of pattern in corresponding space-time code set Ω or multiple pattern.

Step 3, by parallel for the real part and imaginary part receiving signal matrix R', it is thus achieved that incidence matrixNamely

$\tilde{R}=\left[\begin{array}{c}\mathrm{Re}\left(R^{\′}\right)\\ \mathrm{Im}\left(R^{\′}\right)\end{array}\right],$

Wherein Re () represents the computing for the treatment of excess syndrome portion, and Im () expression takes imaginary-part operation.

Step 4, calculates characteristic quantity functional value:

4a) (s is combined for the ith feature amount in characteristic quantity set (U, V)_i,k_i), structure packet correlation matrix R_i:

$R_i=\left[\begin{array}{cccc}\tilde{R}\left(1\right)& \tilde{R}(k_i+1)& \mathrm{...}& \tilde{R}\left(\right(\frac{N}{k_i}-1)k_i+1)\\ \tilde{R}\left(2\right)& \tilde{R}(k_i+2)& \mathrm{...}& \tilde{R}\left(\right(\frac{N}{k_i}-1)k_i+2)\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ \tilde{R}\left(k_i\right)& \tilde{R}\left(2k_i\right)& \mathrm{...}& \tilde{R}\left(\right(\frac{N}{k_i}\left)k_i\right)\end{array}\right],$

WhereinRepresent incidence matrixJth row, j=1,2...N；

4b) calculate packet correlation matrix R_iPacket covariance matrix: C_i=E [R_iR_i ^T], wherein expectation computing is sought in E [] expression, ()^TRepresent transposition computing；

4c) to packet covariance matrix C_iDo Eigenvalues Decomposition, the eigenvalue obtained is arranged in descending order, constitutive characteristic value vectorTake feature value vectorFront 2s_iIndividual eigenvalue constitutes validity feature value vectorWith residue character value Q_i=2rk_i-2s_iConstitute noise characteristic value vectorNamely ${\stackrel{\→}{\mathrm{\λ}}}_{i2}=\[{\mathrm{\ρ}}_{2s_i+1},{\mathrm{\ρ}}_{2s_i+2},...,{\mathrm{\ρ}}_{2{\mathrm{rk}}_i}\];$

4d) estimate characteristic quantity combination (s_i,k_i) corresponding noise power

${\mathrm{\σ}}_i^2=\sqrt[Q_i]{\Π{\stackrel{\→}{\mathrm{\λ}}}_{i2}}$

Wherein ∏ () represents that vector element connects multiplication,Represent and open Q_iPower computing；

4e) obtain characteristic quantity combination (s_i,k_i) characteristic of correspondence flow function value M (s_i,k_i):

$M(s_i,k_i)=\frac{N}{k_i}\left(log\right(\frac{\sqrt{\Π\stackrel{\→}{{\mathrm{\λ}}_{i1}}}}{{{\mathrm{\σ}}_i}^{2Q_i}}\left)\right).$

Step 5, pre-estimation characteristic quantity, obtain new space-time code set Ω ':

5a) every kind of combination in characteristic quantity set (U, V) is repeated step 4, obtain every kind of combination characteristic of correspondence flow function value, composition characteristic flow function value vector: Φ=[M (s₁,k₁),M(s₂,k₂)...M(s_m,k_m) ...], wherein, m=1,2 ... Ζ；

5b) find out the element characteristic of correspondence amount combination that in characteristic quantity functional value vector Φ, numerical value is minimumAccording to step 4d) computing formula, show that characteristic quantity combinesCorresponding noise power estimation value is

5c) taking out block length in space-time code set Ω isGroup separator count isAll patterns, constitute new space-time code set Ω '.

Step 6, calculates the whitening matrix receiving signal:

6a) calculate incidence matrixCovariance matrixAnd integrating step 5b) in noise power estimation valueObtain removing the timesharing correlation matrix P of influence of noise:

$P=E\[\tilde{R}{\tilde{R}}^T\]-\frac{{\widehat{\mathrm{\σ}}}^2}{2}{I}_{2r},$

Wherein, I_2rRepresenting the unit matrix being sized to 2r*2r, * represents multiplying；

6b) timesharing correlation matrix P is carried out Eigenvalues Decomposition, the eigenvalue obtained is arranged in descending order, as the diagonal element of eigenvalue matrix Λ, obtain eigenvalue matrix Λ characteristic of correspondence vector matrix U:

U=P Λ P^-1,

Wherein, P^-1For the inverse matrix of timesharing correlation matrix P,ν_jFor the eigenvalue of timesharing correlation matrix P, j=1,2 ... 2r；

6c) utilize features described above value matrix Λ and eigenvectors matrix U, calculate whitening matrix W:

Wherein Λ^-1Generalized inverse matrix for eigenvalue matrix Λ.

Step 7, to incidence matrixCarry out albefaction, it is thus achieved that the decorrelation matrix Y after albefaction:

Step 8, calculates the relevant norm of time lag of decorrelation matrix Y

$\left|\right|\widetilde{\mathrm{\θ}}\left(\mathrm{\τ}\right)|{|}_F^2=\frac{1}{N^2}|\left|\underset{b=0}{\overset{N-1}{\Σ}}Y\left(b\right)Y{\left(\left(b+\mathrm{\τ}\right)\mathrm{mod}N\right)}^T\right|{|}_F^2,$

Wherein, τ=0,1 ..., N, Y (b) is the b row of decorrelation matrix Y, b=0,1 ..., N-1, mod is modulo operation, | | | |_F ²The Frobenius norm of representing matrix.

Step 9, calculates time lag correlation matrix θ (τ) of pattern in new space-time code set Ω ':

$\mathrm{\θ}\left(\mathrm{\τ}\right)=\underset{u=0}{\overset{N-\hat{k}-1}{\Σ}}\left[\begin{array}{cc}\mathrm{Re}\left(A\right(u\left)\right)\×\mathrm{Re}{\left(A\right(u+\mathrm{\τ}\left)\right)}^T& 0\\ 0& \mathrm{Im}\left(A\right(u\left)\right)\×\mathrm{Im}{\left(A\right(u+\mathrm{\τ}\left)\right)}^T\end{array}\right],$

Wherein, A (u) is the u row of the encoder matrix A of pattern in new space-time code set Ω ', u=1,2 ... ω, ω are the block length that every kind of pattern is corresponding.

Step 10, extracts judgement pattern:

Time lag 10a) is utilized to be correlated with normWith time lag correlation matrix θ (τ), calculate the time lag degree of association d of pattern in new space-time code set Ω ':

$d=\underset{\mathrm{\τ}=1}{\overset{N-1}{\Σ}}{\left(\right|\left|\widetilde{\mathrm{\θ}}\right(\mathrm{\τ}\left)\right|{|}_F^2-2t*\frac{\left|\right|\mathrm{\θ}\left(\mathrm{\τ}\right)|{|}_F^2}{\left|\right|\mathrm{\θ}\left(0\right)|{|}_F^2})}^2,$

Wherein, t is transmission antenna number, takes t=4 in this example；

10b) repeat step 10a), obtain the time lag degree of association that in new space-time code set Ω ', every kind of pattern is corresponding, constitute time lag relevance vector Δ=[d (1), d (2), ... d (l)], wherein l is pattern number in new space-time code set Ω '

10c) by pattern corresponding for element minimum for value in time lag relevance vector ΔAs judgement pattern.

The effect of the present invention can be passed through following emulation and further describe.

Emulation 1: under parameter three groups different, diplomatic copy is invented the recognition correct rate of space-time code in space-time code set Ω, and simulation result is Fig. 3 such as.Wherein:

It is 4 that solid line in Fig. 3 represents transmitting antenna, and reception antenna is 8, sends the system identification accuracy of sequence length N=1024；,

It is 4 that zone circle solid line in Fig. 3 represents transmitting antenna, and reception antenna is 6, sends the system identification accuracy of sequence length N=1024,

It is 4 that dotted line in Fig. 3 represents transmitting antenna, and reception antenna is 8, sends the system identification accuracy of sequence length N=512,

As can be seen from Figure 3: solid line, on zone circle solid line, illustrates to improve the present invention recognition correct rate to space-time code set Ω by the quantity of raising reception antenna；Solid line, on dotted line, illustrates that increasing transmission sequence length can improve the present invention recognition correct rate to space-time code set Ω.

Emulation 2: with existing two kinds of blind-identification methods, space-time code set Ω is identified by the present invention.

Existing two kinds of blind-identification methods are:

Document [1V.Choqueuse, M.Marazinetal., Blindrecognitionoflinearspacetimeblockcodes:Alikelihoodb asedapproach.IEEETrans.SignalProcessing, 58 (3), 2010, 1290-1299] the middle code parameter detecting algorithm proposed, it according to maximum-likelihood criterion to all Candidate Set patterns, construct only relevant with coding parameter likelihood function, by comparing the likelihood function of different coding pattern, coding parameter is judged, and then draw judgement pattern, this emulation is abbreviated as a yard parameter detection method.

Document [2V.Choqueuse, K.Yaoetal., Blindrecognitionoflinearspacetimeblockcodes.IEEEInt.Conf .Acoust.SpeechSignalProcess, 2008,2833-2836] in the Decision Classfication detection algorithm that proposes, its diversity according to Frobenius norm under different delay of the correlation matrix of different Space-Time Block Codings, adopt and contrast step by step, realize the blind recognition to Space-Time Block Coding, this emulation is abbreviated as Decision Classfication detection method.

If emulation SNR ranges is-5dB～10dB, 1000 Monte Carlo Experiments are emulated every 1dB, space-time code in space-time code set Ω is sent identification by each Monte Carlo Experiment successively, record and correctly identify number of times under each signal to noise ratio, and then obtain the recognition correct rate under each signal to noise ratio, the i.e. ratio of recognizable code type duty time-code set omega, simulation result is Fig. 4 such as.Wherein solid line represents the recognition correct rate of the present invention, and zone circle solid line represents the recognition correct rate of code parameter detection method, and dotted line represents the recognition correct rate of Decision Classfication detection method.

As can be seen from Figure 4: solid line, on zone circle solid line and dotted line, illustrates under same signal to noise ratio, the recognition correct rate of space-time code set Ω is higher than existing two kinds of blind-identification methods by the present invention.

It can also be seen that from Fig. 4: signal to noise ratio is when more than 5dB, zone circle solid line levels off to 7/8, dotted line levels off to 6/8, solid line levels off to 1, illustrate that signal to noise ratio is when more than 5dB, the 7/8 of the pattern duty time-code set omega that code parameter detection method may identify which, the 6/8 of the pattern duty time-code set omega that Decision Classfication detection method may identify which, and the pattern in space-time code set Ω can all be identified by the present invention, namely the present invention can pattern more more than code parameter detection method and Decision Classfication detection method identification.

Claims (4)

1. a Delay-dependent space-time code mode blind identification for feature based amount pre-estimation, comprises the steps:

1) receiving terminal receives, by r root reception antenna, the signal sequence that length is N that transmitting terminal sends, and obtains the reception signal matrix R' of r × N；

2) utilize the space-time code composition pattern set omega that be there is a need to identify, take combination (f, g) constitutive characteristic duration set (U, the V) of the group separator count f and block length g of every kind of pattern in Ω；In note characteristic quantity set (U, V), ith feature amount is combined as (s_i,k_i), i=1,2... Ζ, Ζ are number of combinations in characteristic quantity set (U, V)；

3) real part and the imaginary part parallel connection of signal matrix R' will be received, it is thus achieved that incidence matrixNamely

$\tilde{R}=\left[\begin{array}{c}\mathrm{Re}\left(R^{\′}\right)\\ \mathrm{Im}\left(R^{\′}\right)\end{array}\right]$

Wherein Re () represents the computing for the treatment of excess syndrome portion, and Im () expression takes imaginary-part operation；

4) characteristic quantity functional value is calculated:

4a) (s is combined for the ith feature amount in characteristic quantity set (U, V)_i,k_i), structure packet correlation matrix R_i:

$R_i=\left[\begin{array}{cccc}\tilde{R}\left(1\right)& \tilde{R}(k_i+1)& ...& \tilde{R}\left(\right(\frac{N}{k_i}-1)k_i+1)\\ \tilde{R}\left(2\right)& \tilde{R}(k_i+2)& ...& \tilde{R}\left(\right(\frac{N}{k_i}-1)k_i+2)\\ \·& \·& & \·\\ \·& \·& & \·\\ \·& \·& & \·\\ \tilde{R}\left(k_i\right)& \tilde{R}\left(2k_i\right)& ...& \tilde{R}\left(\right(\frac{N}{k_i})k_i)\end{array}\right]$

WhereinRepresent incidence matrixJth row, j=1,2...N；

4b) calculate packet correlation matrix R_iPacket covariance matrix E [R_iR_i ^T], wherein expectation computing is sought in E [] expression, ()^TRepresent transposition computing；

4c) to packet covariance matrix C_iDo Eigenvalues Decomposition, the eigenvalue obtained is arranged in descending order, constitutive characteristic value vectorTake feature value vectorFront 2s_iIndividual eigenvalue constitutes validity feature value vectorWith residue character value Q_i=2rk_i-2s_iConstitute noise characteristic value vector ${\stackrel{\→}{\mathrm{\λ}}}_{i2}=\[{\mathrm{\ρ}}_{2s_i+1},{\mathrm{\ρ}}_{2s_i+2},...,{\mathrm{\ρ}}_{2{\mathrm{rk}}_i}\];$

4d) estimate characteristic quantity combination (s_i,k_i) corresponding noise power

${\mathrm{\σ}}_i^2=\sqrt[Q_i]{\Π{\stackrel{\→}{\mathrm{\λ}}}_{i2}}$

Wherein ∏ () represents that vector element connects multiplication,Represent and open Q_iPower computing；

4e) obtain characteristic quantity combination (s_i,k_i) characteristic of correspondence flow function value M (s_i,k_i):

$M(s_i,k_i)=\frac{N}{k_i}\left(log\right(\frac{\sqrt{\Π\stackrel{\→}{{\mathrm{\λ}}_{i1}}}}{{{\mathrm{\σ}}_i}^{2Q_i}}\left)\right);$

5) pre-estimation characteristic quantity, obtains new space-time code set Ω ':

5a) every kind of combination in characteristic quantity set (U, V) is repeated step 4, obtain every kind of combination characteristic of correspondence flow function value, composition characteristic flow function value vector: Φ=[M (s₁,k₁),M(s₂,k₂)…M(s_m,k_m) ...], wherein, m=1,2 ... Ζ；

5b) find out the element characteristic of correspondence amount combination that in characteristic quantity functional value vector Φ, numerical value is minimumAccording to step 4d) show that characteristic quantity combinesCorresponding noise power estimation value is

5c) taking out block length in space-time code set Ω isGroup separator count isAll patterns, constitute new space-time code set Ω '；

6) whitening matrix receiving signal is calculated:

6a) calculate incidence matrixCovariance matrixAnd integrating step 5b) in noise power estimation valueObtain removing the timesharing correlation matrix P of influence of noise:

Wherein I_2rRepresenting the unit matrix being sized to 2r*2r, * represents multiplying；

6b) timesharing correlation matrix P is carried out Eigenvalues Decomposition, the eigenvalue obtained is arranged in descending order, as the diagonal element of eigenvalue matrix Λ, obtain eigenvalue matrix Λ characteristic of correspondence vector matrix U:

U=P Λ P^-1,

Wherein, P^-1For the inverse matrix of timesharing correlation matrix P,ν_jFor the eigenvalue of timesharing correlation matrix P, j=1,2 ... 2r；

Above-mentioned parameter 6c) is utilized to calculate whitening matrix:Wherein Λ^-1Generalized inverse matrix for eigenvalue matrix Λ；

7) to incidence matrixCarry out albefaction, it is thus achieved that the decorrelation matrix after albefaction:

8) the relevant norm of time lag of decorrelation matrix Y is calculated

$\left|\right|\widetilde{\mathrm{\θ}}\left(\mathrm{\τ}\right)|{|}_F^2=\frac{1}{N^2}|\left|\underset{b=0}{\overset{N-1}{\Σ}}Y\left(b\right)Y{\left(\right(b+\mathrm{\τ}\left)\mathrm{mod}N\right)}^T\right|{|}_F^2,$

Wherein, τ=0,1 ..., N, Y (b) is the b row of decorrelation matrix Y, b=0,1 ..., N-1, mod is modulo operation, | | | |_F ²The Frobenius norm of representing matrix；

9) time lag correlation matrix θ (τ) of pattern in new space-time code set Ω ' is calculated:

$\mathrm{\θ}\left(\mathrm{\τ}\right)=\underset{u=0}{\overset{N-\hat{k}-1}{\Σ}}\left[\begin{array}{cc}\mathrm{Re}\left(A\right(u))\×\mathrm{Re}{\left(A\right(u+\mathrm{\τ}\left)\right)}^T& 0\\ 0& \mathrm{Im}\left(A\right(u))\×\mathrm{Im}{\left(A\right(u+\mathrm{\τ}\left)\right)}^T\end{array}\right],$

Wherein, A (u) is the u row of the encoder matrix A of pattern in new space-time code set Ω ', u=1,2 ... ω, ω are pattern block length；

10) judgement pattern is obtained:

Above-mentioned parameter 10a) is utilized to calculate the time lag degree of association d of pattern in new space-time code set Ω ':

$d=\underset{\mathrm{\τ}=1}{\overset{N-1}{\Σ}}{\left(\right|\left|\widetilde{\mathrm{\θ}}\right(\mathrm{\τ}\left)\right|{|}_F^2-2t*\frac{\left|\right|\mathrm{\θ}\left(\mathrm{\τ}\right)|{|}_F^2}{\left|\right|\mathrm{\θ}\left(0\right)|{|}_F^2})}^2,$

Wherein, t is transmission antenna number；

10b) repeat step 10a), obtain the time lag degree of association that in new space-time code set Ω ', every kind of pattern is corresponding, constitute time lag relevance vector Δ=[d (1), d (2), ... d (l)], wherein l is pattern number in new space-time code set Ω ', the pattern that element that in the stagnant relevance vector Δ that clocks, value is minimum is correspondingFor judgement pattern.

2. the Delay-dependent space-time code mode blind identification of feature based amount pre-estimation according to claim 1, it is characterized in that, space-time code set Ω in described step (2), including orthogonal space time packet, quasi-orthogonal space time block code and non-orthogonal space-time block.

3. the Delay-dependent space-time code mode blind identification of feature based amount pre-estimation according to claim 1, it is characterised in that the characteristic quantity combination (s in described step (2)_i,k_i) a kind of pattern in corresponding space-time code set Ω or multiple pattern.

4. the Delay-dependent space-time code mode blind identification of feature based amount pre-estimation according to claim 1, it is characterised in that described step 5c) in new space-time code set Ω ' in the group separator count of every kind of pattern and block length equal.