CN102624421B - Blind subspace code assist method for suppressing accurate qualitative narrow-band interference of code division multiple access (CDMA) system - Google Patents

Abstract

In order to improve performance for an existing blind direct code assist method to suppress accurate qualitative narrow-band interference of a code division multiple access (CDMA) system, the invention provides a blind subspace code assist method. The method comprises the following steps: receiving a wireless communication signal and carrying out down conversion on the signal so as to obtain an intermediate frequency signal; digitizing the intermediate frequency signal and demodulating a digital signal to a baseband signal; carrying out chip matching filtering on the baseband signal so as to extract a sampling signal; carrying out windowing storage on the sampling signal so as to obtain a signal vector and carrying out interference suppression to the signal vector. Output SINR performance of the blind subspace code assist method provided in the invention is better than the blind direct code assist method so that high-performance processing of suppressing the accurate qualitative narrow-band interference of the CDMA system can be realized.

Description

Suppress the blind subspace code householder method that the accurate qualitative arrowband of cdma system disturbs

Technical field

The present invention relates to suppress in cdma wireless communication system the method that accurate qualitative arrowband disturbs, wherein accurate qualitative arrowband disturbs and comprises that audio disturbances and digital arrowband disturb.The present invention be more particularly directed to solve the not good problem of existing blind direct code householder method output Signal to Interference plus Noise Ratio performance.

Background technology

The reason that spread spectrum system is used widely in wireless channel is its frequency selective fading that can effectively cause anti-multipath and the superior function in Ta Gong road channel, wherein representative core technology is code division multiple access (CDMA, Code Division Multiple Access) technology.It is the interference that often gets involved spread spectrum system that arrowband disturbs, and wherein accurate qualitative arrowband interference is an extremely important and common class, comprises that audio disturbances and digital arrowband disturb.Although spread spectrum system self possesses certain antijamming capability, effectively interference mitigation technology can significantly improve systematic function.

Initial spread spectrum Anti-Jamming Technique originates from 20 century 70s, until the end of the eighties, the main focus of Anti-Jamming Technique is in the directly-enlarging system Suppression of narrow band interference based on prediction/estimation filtering and frequency domain filtering, scientific research personnel's achievement that the Milstein of take is master is the highest, as the summary of document " L B Milstein; Interference rejectiontechniques in spread spectrum communication, IEEE Proceedings, 1988 ".Enter the mid-90, arrival along with CDMA research boom, with the scientific research personnel headed by Poor and Rusch, focus has been transferred to disturb in associating inhibition of cdma system based on technology such as linear prediction, nonlinear prediction and Multiuser Detection more, as the summary of document " H V Poor; L A Rusch; Narrowbandinterference suppression in spread spectrum CDMA, IEEE PersonalCommunication, 1994 ".20th century, the scientific research personnel such as Wang further develop into cdma system interference mitigation technology Predicting Technique, transform domain technology and code ancillary technique, as document " Xiaodong Wang; H V Poor; Wireless Communication Systems-AdvancedTechniques for Signal Reception, Beijing:Publishing House of Electronics Industry, 2005;, summary.In above each technology, Predicting Technique comprises linear prediction and nonlinear prediction Liang great branch, its research concentrates in the improvement that receives structure, but due to the processing mode of having taked by bit, the typical problem of existence is that the error rate is high, as document " J Wang; L B Milstein; Adaptive LMS filters forcellular CDMA overlay situations, IEEE Select Areas Commun, 1996 ".Under this viewpoint, a code ancillary technique that utilizes signal code feature to carry out piece processing seems especially effective, and it is that multi-user system is disturbed one of the most promising technology of inhibition, and in this field, the achievement of Poor and Wang is the highest.In the last few years, the shortcoming that needs known disturbances priori for code ancillary technique, developed blind coding ancillary technique, as document " S Buzzi; M Lops, A M Tulino, Blind adaptive multiuser detection forasynchronous dual-rate DS/CDMA systems; IEEE Select Areas Commun, 2001 ".Existing busy direct code ancillary technique has been realized the blind Detecting without priori, but output Signal to Interference plus Noise Ratio performance is unsatisfactory, as document " S Buzzi; M Lops; H V Poor, Code-Aided InterferenceSuppression for DS/CDMA Overlay Systems, IEEE Processing; 2002 ", there is very large room for improvement.

Accordingly, improving existing blind direct code householder method disturbs the method for exporting Signal to Interference plus Noise Ratio performance while suppressing significantly to need to the accurate qualitative arrowband of cdma system.

Summary of the invention

Technology of the present invention is dealt with problems and is: provide a kind of and can suppress the blind subspace code householder method that in cdma system, accurate qualitative arrowband disturbs, can improve existing blind direct code householder method output Signal to Interference plus Noise Ratio performance, wherein accurate qualitative arrowband disturbs and comprises that audio disturbances and digital arrowband disturb.

Technical solution of the present invention is: the blind subspace of high-performance code householder method, comprises the following steps:

(1) receive wireless communication signals, comprise that CDMA signal, white noise and accurate qualitative arrowband disturb.

(2) described wireless communication signals is down-converted to intermediate-freuqncy signal.

(3) intermediate-freuqncy signal described in digitlization, to provide digital signal.

(4) by described digital demodulation signal, so that baseband signal to be provided.

(5) by described baseband signal envelope r (t) by cutting general matched filtering so that sampled signal r (m) to be provided, wherein m is sampled signal label and m=0,1,2 ...

Sampled signal r (m) can be modeled as:

r(m)＝y(m)+i(m)+ε(m)

＝y ₀(m)+z(m)+i(m)+ε(m)

Y in formula (m) represents CDMA sampled signal, comprises the signal component y of desired user 0 ₀(m) and multiple access disturb the signal component z (m) of (MAI, Multiple Access Interference); I (m) represents accurate qualitative arrowband interference sample signal; ε (m) represents that power spectral density is white Gaussian noise sampled signal, and system synchronization.

CDMA sampled signal can be modeled as:

$y\left(m\right)=\underset{k=0}{\overset{K_z}{\mathrm{\Σ}}}{A}_k\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_k\left(n\right)s_k(m-\mathrm{nN})$

Wherein the sampled signal model of desired user 0 and MAI is respectively

$y_0\left(m\right)=A_0\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_0\left(n\right)s_0(m-\mathrm{nN})$

$z\left(m\right)=\underset{k=1}{\overset{K_z}{\mathrm{\Σ}}}{A}_k\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_k\left(n\right)s_k(m-\mathrm{nN})$

K in formula _zrepresent MAI number of users (K _z>=1), A _krepresent that k user receives signal amplitude, A ₀for desired user receives signal amplitude, b _k(n) represent k subscriber signal stream (1 or-1), b ₀(n) be expectation subscriber signal stream, s _krepresent k user's direct sequence spread spectrum codes sampled value, s ₀for desired user direct sequence spread spectrum codes sampled value, before sampling, time domain frequency expansion sequence is

$s_k\left(t\right)=\frac{1}{\sqrt{N}}\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{s}_{k,j}{\mathrm{\ψ}}_c(t-j{T}_c)$

{ s in formula _{k, j}: j=1 ..., the spreading code that N} is k user (1 or-1), and k=0,1 ... K _z, ψ _c() is duration T _cnormalization waveform, N=T _b/ T _cfor spreading gain, T _bfor the signal period, T _cfor the spreading code cycle, and spreading code is independent of signal.

Accurately determining signal comprises that audio disturbances and digital arrowband disturb, and wherein audio disturbances sampled signal can be modeled as:

$i\left(m\right)=\frac{1}{\sqrt{N}}\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}\sin c\left(f_{\mathrm{ik}}{T}_c\right)\cos (2\mathrm{\π}{f}_{\mathrm{ik}}{T}_{c}m+{\mathrm{\πf}}_{\mathrm{ik}}{T}_c)$

$=\frac{1}{\sqrt{N}}\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}^{\′}\cos \left(\mathrm{\π}{f}_{\mathrm{ik}}^{\′}(2m+1)\right)$

K in formula _irepresent audio disturbances number, A _ikrepresent that k audio disturbances receives signal amplitude, f _ikexpression is with respect to k audio disturbances frequency of CDMA carrier frequency, A ' _ikexpression audio disturbances normalization amplitude A ' _ik=A _iksinc (f _ikt _c), f ' _ikrepresent audio disturbances normalized frequency f ' _ik=f _ikt _c, and Nf ' _ikfor integer.

Numeral arrowband interference sample signal can be modeled as:

$i\left(m\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}\frac{1}{\sqrt{N}}\underset{n_i=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_{\mathrm{ik}}\left(n_i\right)\mathrm{\υ}(m-n_i{N}_{\mathrm{vk}})$

K in formula _irepresentative digit arrowband disturbs number, A _ikrepresent that k digital arrowband disturbs reception signal amplitude, b _ik(n) represent k digital narrow-band interference signal stream (1 or-1), the duration is T _ik, i.e. digital arrowband interference period.Here T _ik> > T _cand N _vk=T _b/ T _ikfor integer, υ () representation unit height rectangular pulse.

(6) by described sampled signal r (m) by windowing memory, for n transmitted signal, processing interval [nT _b, (n+1) T _b] in to sampled signal r (m) windowing obtain (N * 1) dimension windowing vector r (n)=[r (nN+N-1), r (nN+N-2) ..., r (nN)] ^t, wherein n is transmitted signal label and n=0,1,2 ..., T _bfor the signal period, N is spreading gain.

Windowing vector r (n) can be modeled as:

r(n)＝y(n)+i(n)+ε(n)

＝y ₀(n)+z(n)+i(n)+ε(n)

Y in formula (n) is CDMA windowing signal, comprises the signal component y of desired user 0 ₀and the signal component z of MAI (n) (n), i (n) disturbs windowing signal at the certainty arrowband that is as the criterion, and ε (n) is white Gaussian noise windowing signal.

CDMA windowing vector can be modeled as:

$y\left(n\right)=\underset{k=0}{\overset{K_z}{\mathrm{\Σ}}}{A}_k{b}_k\left(n\right)s_k$

The windowing signal model that wherein comprises desired user 0 and MAI

y ₀(n)＝A ₀b ₀(n)s ₀

In formula $s_k=\frac{1}{\sqrt{N}}{[s_{k,N-1},s_{k,N-2},\·\·\·,s_{k,0}]}^T,$ $s_0=\frac{1}{\sqrt{N}}{[s_{0,N-1},s_{0,N-2},\·\·\·,s_{0,0}]}^T.$

Audio disturbances windowing vector can be modeled as:

$i\left(n\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}^{\′}{s}_{\mathrm{ik}}$

In formula $s_{\mathrm{ik}}=\frac{1}{\sqrt{N}}{[\cos (2(N-1)\mathrm{\π}{f}_{\mathrm{ik}}^{\′}+\mathrm{\π}{f}_{\mathrm{ik}}^{\′}),\cos (2(N-2){\mathrm{\πf}}_{\mathrm{ik}}^{\′}+{\mathrm{\πf}}_{\mathrm{ik}}^{\′}),\·\·\·,\cos \left({\mathrm{\πf}}_{\mathrm{ik}}^{\′}\right)]}^T.$

Numeral arrowband disturbs windowing vector to be modeled as:

$i\left(n\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}{s}_{\mathrm{ik}}$

In formula and wherein each symbol repeats N _ik=T _ik/ T _cindividual element, N _ik=T _ik/ T _cfor digital arrowband obstacle gain.

(7) described windowing vector r (n) is carried out to the blind subspace of high-performance code auxiliary filter, comprise the steps:

Step 1: blind estimation autocorrelation matrix

$R_{\mathrm{rr}}\left(n\right)=\frac{1}{L}\underset{n=0}{\overset{L-1}{\mathrm{\Σ}}}r\left(n\right)r^T\left(n\right)$

In formula, L represents to estimate the windowing number of autocorrelation matrix, receives signal number, sets 50≤L≤1000 in the present invention.

Step 2: autocorrelation matrix Eigenvalues Decomposition

$R_{\mathrm{rr}}=U_s{\mathrm{\Λ}}_s{U}_s^T+{\mathrm{\σ}}_{\mathrm{\ϵ}}^2{U}_{\mathrm{\ϵ}}{U}_{\mathrm{\ϵ}}^T$

Λ in formula _s=diag{ λ ₁, λ ₂..., λ _kcomprise K=1+K _z+ K _ithe individual characteristic value that is greater than diagonal matrix, U _s=[u ₁, u ₂..., u _k] be the matrix that a corresponding K orthogonal vectors form, meet u _ε=[u _k+1, u _k+2..., u _n] be N-K characteristic value the matrix that corresponding orthogonal vectors form, for CDMA user disturbs the virtual CDMA subspace of opening with accurate qualitative arrowband, its quadrature component is U _εthe noise subspace opened of row.

Step 3: calculate blind subspace code auxiliary filter vector

Setting blind subspace code auxiliary filter vector is w=[w ₁, w ₂..., w _n] ^t(N * 1 dimension), by w=U _sx substitution minimum output energy (MMOE, Minimum Mean Output Energy) code aided algorithm cost function, x is intermediate variable

$\left\{\begin{array}{c}{w}_{\mathrm{MMOE}}=\arg \underset{w}{\min }E\left\{{\left|w^{T}r\left(n\right)\right|}^2\right\}\\ w^T{s}_0=1\end{array}\right.$

Obtain

Making above formula gradient is zero, obtains

$x_{\mathrm{MMOE}}=-\frac{\mathrm{\ξ}}{2}{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0$

By above formula substitution constraints the limited Lagrange factor generation time above formula obtaining, can obtain

$x_{\mathrm{MMOE}}=\frac{{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0}$

According to u wherein _swith U _εthe character of quadrature, can obtain

$U_s^T{R}_{\mathrm{rr}}{U}_s=U_s^T(U_s{\mathrm{\Λ}}_s{U}_s^T+{\mathrm{\σ}}_{\mathrm{\ϵ}}^2{U}_{\mathrm{\ϵ}}{U}_{\mathrm{\ϵ}}^T)U_s={\mathrm{\Λ}}_s$

The x that above two formulas are obtained _mMOEsubstitution w=U _sx, can obtain blind subspace MMOE code aided algorithm filter vector expression formula

$w_{\mathrm{MMOE}}=\frac{U_s{\mathrm{\Λ}}_s^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{\mathrm{\Λ}}_s^{-1}{U}_s^T{s}_0}$

Because needs are got symbol after filter vector is processed, therefore can do, cast out denominator and must process, obtain blind subspace code aided algorithm filter vector

$w\∝U_s{\mathrm{\Λ}}_s^{-1}{U}_s^T{s}_0$

Step 4: direct-detection symbol judgement

According to direct-detection rule

${\hat{b}}_0\left(n\right)=w^{T}r\left(n\right)$

After detection, carry out symbol judgement and obtain useful information.

The present invention's beneficial effect is compared with prior art:

The present invention has realized the high-performance filtering that the accurate qualitative arrowband of cdma system disturbs, and wherein accurate qualitative arrowband disturbs and comprises that audio disturbances and digital arrowband disturb.The blind subspace code householder method output Signal to Interference plus Noise Ratio performance providing is obviously better than existing blind direct code householder method output Signal to Interference plus Noise Ratio performance.

Accompanying drawing explanation

The existing blind direct code householder method of describing Fig. 1 suppresses the cdma system reception block diagram disturbing by accurate qualitative arrowband;

The CAMA system that the high performance blind subspace code householder method of describing Fig. 2 suppresses to disturb by accurate qualitative arrowband receives block diagram;

Describe Fig. 3 high performance blind subspace code householder method and existing blind direct code householder method suppress the performance simulation contrast curved surface of accurate deterministic disturbance, wherein Fig. 3 a describes the performance comparison curved surface that audio reception disturbs, and Fig. 3 b describes to receive the performance comparison curved surface that digital arrowband disturbs;

Fig. 4 describes algorithm implementing procedure figure of the present invention.

Embodiment

In detailed description of the present invention, with reference to appended drawing, these accompanying drawings are explained specific exemplary embodiment, invention can be implemented in these exemplary embodiments below.These embodiment describe with sufficient details, to allow those skilled in the art to implement the present invention, but can utilize other embodiment, and can make changing with other of logic, machinery, electrical equipment, and do not depart from standard of the present invention.Therefore, detailed description below should not be considered restrictive, and scope of the present invention is limited by appended claims only.

The embodiment of blind subspace code householder method comprises the following steps:

(1) receive wireless communication signals, comprise that CDMA signal, white noise and accurate qualitative arrowband disturb.

(2) described wireless communication signals is down-converted to intermediate-freuqncy signal.

(3) intermediate-freuqncy signal described in digitlization, to provide digital signal.

(4) by described digital demodulation signal, so that baseband signal to be provided.

(5) by described baseband signal envelope r (t) by cutting general matched filtering so that sampled signal r (m) to be provided, wherein m is sampled signal label and m=0,1,2 ...

Sampled signal r (m) can be modeled as:

r(m)＝y(m)+i(m)+ε(m)

＝y ₀(m)+z(m)+i(m)+ε(m)

Y in formula (m) represents CDMA sampled signal, comprises the signal component y of desired user 0 ₀(m) and multiple access disturb the signal component z (m) of (MAI, Multiple Access Interference); I (m) represents accurate qualitative arrowband interference sample signal; ε (m) represents that power spectral density is white Gaussian noise sampled signal, and system synchronization.

CDMA sampled signal can be modeled as:

$y\left(m\right)=\underset{k=0}{\overset{K_z}{\mathrm{\Σ}}}{A}_k\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_k\left(n\right)s_k(m-\mathrm{nN})$

Wherein the sampled signal model of desired user 0 and MAI is respectively

$y_0\left(m\right)=A_0\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_0\left(n\right)s_0(m-\mathrm{nN})$

$z\left(m\right)=\underset{k=1}{\overset{K_z}{\mathrm{\Σ}}}{A}_k\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_k\left(n\right)s_k(m-\mathrm{nN})$

K in formula _zrepresent MAI number of users (K _z>=1), A _krepresent that k user receives signal amplitude, A ₀for desired user receives signal amplitude, b _k(n) represent k subscriber signal stream (1 or-1), b ₀(n) be expectation subscriber signal stream, s _krepresent k user's direct sequence spread spectrum codes sampled value, s ₀for desired user direct sequence spread spectrum codes sampled value, before sampling, time domain frequency expansion sequence is

$s_k\left(t\right)=\frac{1}{\sqrt{N}}\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{s}_{k,j}{\mathrm{\ψ}}_c(t-j{T}_c)$

{ s in formula _{k, j}: j=1 ..., the spreading code that N} is k user (1 or-1), and k=0,1 ... K _z, ψ _c() is duration T _cnormalization waveform, N=T _b/ T _cfor spreading gain, T _bfor the signal period, T _cfor the spreading code cycle, and spreading code is independent of signal.

Accurately determining signal comprises that audio disturbances and digital arrowband disturb, and wherein audio disturbances sampled signal can be modeled as:

$i\left(m\right)=\frac{1}{\sqrt{N}}\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}\sin c\left(f_{\mathrm{ik}}{T}_c\right)\cos (2\mathrm{\π}{f}_{\mathrm{ik}}{T}_{c}m+{\mathrm{\πf}}_{\mathrm{ik}}{T}_c)$

$=\frac{1}{\sqrt{N}}\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}^{\′}\cos \left(\mathrm{\π}{f}_{\mathrm{ik}}^{\′}(2m+1)\right)$

K in formula _irepresent audio disturbances number, A _ikrepresent that k audio disturbances receives signal amplitude, f _ikexpression is with respect to k audio disturbances frequency of CDMA carrier frequency, A ' _ikexpression audio disturbances normalization amplitude A ' _ik=A _iksinc (f _ikt _c), f ' _ikrepresent audio disturbances normalized frequency f ' _ik=f _ikt _c, and Nf ' _ikfor integer.

Numeral arrowband interference sample signal can be modeled as:

$i\left(m\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}\frac{1}{\sqrt{N}}\underset{n_i=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_{\mathrm{ik}}\left(n_i\right)\mathrm{\υ}(m-n_i{N}_{\mathrm{vk}})$

K in formula _irepresentative digit arrowband disturbs number, A _ikrepresent that k digital arrowband disturbs reception signal amplitude, b _ik(n) represent k digital narrow-band interference signal stream (1 or-1), the duration is T _ik, i.e. digital arrowband interference period.Here T _ik> > T _cand N _vk=T _b/ T _ikfor integer, υ () representation unit height rectangular pulse.

(6) by described sampled signal r (m) by windowing memory, for n transmitted signal, processing interval [nT _b, (n+1) T _b] in to sampled signal r (m) windowing obtain (N * 1) dimension windowing vector r (n)=[r (nN+N-1), r (nN+N-2) ..., r (nN)] ^t, wherein n is transmitted signal label and n=0,1,2 ..., T _bfor the signal period, N is spreading gain.

Windowing vector r (n) can be modeled as:

r(n)＝y(n)+i(n)+ε(n)

＝y ₀(n)+z(n)+i(n)+ε(n)

Y in formula (n) is CDMA windowing signal, comprises the signal component y of desired user 0 ₀and the signal component z of MAI (n) (n), i (n) disturbs windowing signal at the certainty arrowband that is as the criterion, and ε (n) is white Gaussian noise windowing signal.

CDMA windowing vector can be modeled as:

$y\left(n\right)=\underset{k=0}{\overset{K_z}{\mathrm{\Σ}}}{A}_k{b}_k\left(n\right)s_k$

The windowing signal model that wherein comprises desired user 0 and MAI

y ₀(n)＝A ₀b ₀(n)s ₀

In formula $s_k=\frac{1}{\sqrt{N}}{[s_{k,N-1},s_{k,N-2},\·\·\·,s_{k,0}]}^T,$ $s_0=\frac{1}{\sqrt{N}}{[s_{0,N-1},s_{0,N-2},\·\·\·,s_{0,0}]}^T.$

Audio disturbances windowing vector can be modeled as:

$i\left(n\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}^{\′}{s}_{\mathrm{ik}}$

In formula $s_{\mathrm{ik}}=\frac{1}{\sqrt{N}}{[\cos (2(N-1)\mathrm{\π}{f}_{\mathrm{ik}}^{\′}+\mathrm{\π}{f}_{\mathrm{ik}}^{\′}),\cos (2(N-2){\mathrm{\πf}}_{\mathrm{ik}}^{\′}+{\mathrm{\πf}}_{\mathrm{ik}}^{\′}),\·\·\·,\cos \left({\mathrm{\πf}}_{\mathrm{ik}}^{\′}\right)]}^T;$

Numeral arrowband disturbs windowing vector to be modeled as:

$i\left(n\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}{s}_{\mathrm{ik}}$

In formula and wherein each symbol repeats N _ik=T _ik/ T _cindividual element, N _ik=T _ik/ T _cfor digital arrowband obstacle gain.

(7) described windowing vector r (n) is carried out to the blind subspace of high-performance code auxiliary filter, comprise the steps:

First: blind estimation autocorrelation matrix

$R_{\mathrm{rr}}\left(n\right)=\frac{1}{L}\underset{n=0}{\overset{L-1}{\mathrm{\Σ}}}r\left(n\right)r^T\left(n\right)$

In formula, L represents to estimate the windowing number of autocorrelation matrix, receives signal number, sets 50≤L≤1000 in the present invention.

Secondly: autocorrelation matrix Eigenvalues Decomposition

$R_{\mathrm{rr}}=U_s{\mathrm{\Λ}}_s{U}_s^T+{\mathrm{\σ}}_{\mathrm{\ϵ}}^2{U}_{\mathrm{\ϵ}}{U}_{\mathrm{\ϵ}}^T$

Λ in formula _s=diag{ λ ₁, λ ₂..., λ _kcomprise K=1+K _z+ K _ithe individual characteristic value that is greater than diagonal matrix, U _s=[u ₁, u ₂..., u _k] be the matrix that a corresponding K orthogonal vectors form, meet u _ε=[u _k+1, u _k+2..., u _n] be N-K characteristic value the matrix that corresponding orthogonal vectors form, for CDMA user disturbs the virtual CDMA subspace of opening with accurate qualitative arrowband, its quadrature component is U _εthe noise subspace opened of row.

Again: calculate blind subspace code auxiliary filter vector

Setting blind subspace code auxiliary filter vector is w=[w ₁, w ₂..., w _n] ^t(N * 1 dimension), by w=U _sx substitution minimum output energy (MMOE, Minimum Mean Output Energy) code aided algorithm cost function, x is intermediate variable

$\left\{\begin{array}{c}{w}_{\mathrm{MMOE}}=\arg \underset{w}{\min }E\left\{{\left|w^{T}r\left(n\right)\right|}^2\right\}\\ w^T{s}_0=1\end{array}\right.$

Obtain

Making above formula gradient is zero, obtains

$x_{\mathrm{MMOE}}=-\frac{\mathrm{\ξ}}{2}{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0$

By above formula substitution constraints the limited Lagrange factor generation time above formula obtaining, can obtain

$x_{\mathrm{MMOE}}=\frac{{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0}$

According to u wherein _swith U _εthe character of quadrature, can obtain

$U_s^T{R}_{\mathrm{rr}}{U}_s=U_s^T(U_s{\mathrm{\Λ}}_s{U}_s^T+{\mathrm{\σ}}_{\mathrm{\ϵ}}^2{U}_{\mathrm{\ϵ}}{U}_{\mathrm{\ϵ}}^T)U_s={\mathrm{\Λ}}_s$

The x that above two formulas are obtained _mMOEsubstitution w=U _sx, can obtain blind subspace MMOE code aided algorithm filter vector expression formula

$w_{\mathrm{MMOE}}=\frac{U_s{\mathrm{\Λ}}_s^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{\mathrm{\Λ}}_s^{-1}{U}_s^T{s}_0}$

Because needs are got symbol after filter vector is processed, therefore can do, cast out denominator and must process, obtain blind subspace code aided algorithm filter vector

$w\∝U_s{\mathrm{\Λ}}_s^{-1}{U}_s^T{s}_0$

Last: direct-detection symbol judgement

According to direct-detection rule

${\hat{b}}_0\left(n\right)=w^{T}r\left(n\right)$

After detection, carry out symbol judgement and obtain useful information.

The existing blind direct code householder method of describing Fig. 1 suppresses the cdma system reception block diagram disturbing by accurate qualitative arrowband, down conversion module 101, blind direct code auxiliary filter module 102 and blind direct code auxiliary filter vector calculation module 103, consists of.

As shown in the figure, the wireless communication signals 105 that antenna 104 receives comprises useful signal CDMA signal, audio disturbances and white Gaussian noise.Antenna 104 is coupled to down conversion module 101.In down conversion module 101, first by band pass filter 106, process wireless communication signals 105, the frequency of wanting is selected on this filter optimization ground, for example, with the frequency of CDMA signal correction connection.Band pass filter 106 is coupled to amplifier 107, and this amplifier amplifies the signal from band pass filter 106.Blender 108 mixes the output of amplifier 107 with the oscillator signal from local oscillator 109.Like this, the output signal of blender 108 down-conversion amplifiers 107, to provide intermediate-freuqncy signal 110.After initial down-conversion, by modulus a/d transducer 111, intermediate-freuqncy signal 110 is transformed into digital signal 112.Because QPSK signal can be regarded as the stack of two quadrature 2PSK signals, so go demodulation with the crossing coherent carrier of two-way.Wherein a road signal enters blender 115 and mixes with the signal from digital controlled oscillator 113, and another road signal enters blender 116 and with the signal after pi/2 phase shift 114 mixes from digital controlled oscillator 113.Blender 115 is connected respectively to low pass filter 117 and low pass filter 118 with the signal of blender 116 outputs.Thereafter, the output signal of the output signal of low pass filter 117 and low pass filter 118 is connected respectively to sampling decision device 119 and sampling decision device 120, and by the output signal of sampling decision device 119 and sampling decision device 120 after parallel/serial device 121 conversion, become baseband signal 122 outputs.

Like this, band pass filter 106, amplifier 107, blender 108, local oscillator 109 have completed the process that is down-converted to intermediate-freuqncy signal, modulus a/d transducer 111, digital controlled oscillator 113, pi/2 phase shift 114, blender 115,116, low pass filter 117,118, sampling decision device 119,120, parallel/serial device 121 have completed the process that is down-converted to baseband signal by intermediate-freuqncy signal.Above process has completed down conversion module 101.

Existing blind direct code householder method is carried out filtering by blind direct code auxiliary filter module 102.Baseband signal 122 envelope r (t) are cut to general matched filtering sampling 123, sampled signal r (m) 124 is provided.By sampled signal r (m) 124 by windowing memory 125, to sampled signal r (m) 124 windowings obtain (N * 1) dimension windowing vector r (n)=[r (nN+N-1), r (nN+N-2) ..., r (nN)] ^t126.The filter vector element w that blind direct code auxiliary filter vector calculation module 103 is obtained ₁131, w ₂132...w _n13N sends into linear combination estimator 1:w with element r (nN+N-1) 141, r (nN) 142...r (nN+N-2) 14N of windowing vector r (n) 126 respectively ₁r (nN+N-1) 151, linear combination estimator 2:w ₂r (nN+N-2) 152... linear combination estimator N:w _nr (nN) 15N, obtains linear combination signal through adder 161 finally, to bit estimated signal send into symbol judgement device 162, obtain useful signal 163.

Like this, and general filtering sampling 123; Sampling windowing storage 125; 2 152... linear combinations estimation N 15N are estimated in linear combination estimation 1 151, linear combination; Adder 161; Symbol judgement device 162 has formed blind direct code auxiliary filter module 102 jointly.

The filter vector element w that blind direct code auxiliary filter module 102 is required ₁131, w ₂132...w _n13N is provided by blind direct code auxiliary filter vector calculation module 103.According to channel estimating principle, windowing vector r (n) 126 is sent into blind estimation autocorrelation matrix computing module 127 the relevant parameter that blind estimation autocorrelation matrix computing module 127 is obtained is sent into blind direct code auxiliary filter vector calculation module 128 the filter vector element w finally blind direct code auxiliary filter vector calculation module 128 being obtained ₁131, w ₂132...w _n13N sends into blind direct code auxiliary filter module 102.

Like this, blind estimation autocorrelation matrix computing module 127, blind direct code auxiliary filter vector calculation module 128 has formed blind direct code auxiliary filter vector calculation module 103.

Fig. 2 describes the CAMA system reception block diagram that high performance blind subspace code householder method suppresses audio disturbances, down conversion module 201, blind subspace code auxiliary filter module 202 and blind subspace code auxiliary filter vector calculation module 203, consists of.

As shown in the figure, the wireless communication signals 205 that antenna 204 receives comprises useful signal CDMA signal, audio disturbances and white Gaussian noise.Antenna 204 is coupled to down conversion module 201.In down conversion module 201, first by band pass filter 206, process wireless communication signals 205, the frequency of wanting is selected on this filter optimization ground, for example, with the frequency of CDMA signal correction connection.Band pass filter 206 is coupled to amplifier 207, and this amplifier amplifies the signal from band pass filter 206.Blender 208 mixes the output of amplifier 207 with the oscillator signal from local oscillator 209.Like this, the output signal of blender 208 down-conversion amplifiers 207, to provide intermediate-freuqncy signal 210.After initial down-conversion, by modulus a/d transducer 211, intermediate-freuqncy signal 210 is transformed into digital signal 212.Because QPSK signal can be regarded as the stack of two quadrature 2PSK signals, so go demodulation with the crossing coherent carrier of two-way.Wherein a road signal enters blender 215 and mixes with the signal from digital controlled oscillator 213, and another road signal enters blender 216 and with the signal after pi/2 phase shift 214 mixes from digital controlled oscillator 213.Blender 215 is connected respectively to low pass filter 217 and low pass filter 218 with the signal of blender 216 outputs.Thereafter, the output signal of the output signal of low pass filter 217 and low pass filter 218 is connected respectively to sampling decision device 219 and sampling decision device 220, and by the output signal of sampling decision device 219 and sampling decision device 220 after parallel/serial device 221 conversion, become baseband signal 222 outputs.

Like this, band pass filter 206, amplifier 207, blender 208, local oscillator 209 have completed the process that is down-converted to intermediate-freuqncy signal, modulus a/d transducer 211, digital controlled oscillator 213, pi/2 phase shift 214, blender 215,216, low pass filter 217,218, sampling decision device 219,220, parallel/serial device 221 have completed the process that is down-converted to baseband signal by intermediate-freuqncy signal.Above process has completed down conversion module 201.

Blind subspace code householder method is carried out filtering by blind subspace code auxiliary filter module 202.Baseband signal 222 envelope r (t) are cut to general matched filtering sampling 223, sampled signal r (m) 224 is provided.By sampled signal r (m) 224 by windowing memory 225, to sampled signal r (m) 224 windowings obtain (N * 1) dimension windowing vector r (n)=[r (nN+N-1), r (nN+N-2) ..., r (nN)] ^t226.The filter vector element w that blind subspace code auxiliary filter vector calculation module 203 is obtained ₁231, w ₂232...w _n23N sends into linear combination estimator 1:w with element r (nN+N-1) 241, r (nN) 242...r (nN+N-2) 24N of windowing vector r (n) 226 respectively ₁r (nN+N-1) 251, linear combination estimator 2:w ₂r (nN+N-2) 252... linear combination estimator N:w _nr (nN) 25N, obtains linear combination signal through adder 261 finally, by bit estimated signal send into symbol judgement device 262, obtain useful signal 263.

Like this, and general filtering sampling 223; Sampling windowing storage 225; 2 252... linear combinations estimation N 25N are estimated in linear combination estimation 1 251, linear combination; Adder 261; Symbol judgement device 262 has formed blind subspace code auxiliary filter module 202 jointly.

The filter vector element w that blind subspace code auxiliary filter module 202 is required ₁231, w ₂232...w _n23N is provided by blind subspace code auxiliary filter vector calculation module 203.According to channel estimating principle, by windowing vector r (n) 226 blind estimation autocorrelation matrix computing modules 227 the autocorrelation matrix that blind estimation autocorrelation matrix computing module 227 is obtained is sent into autocorrelation matrix Eigenvalues Decomposition module 228 the relevant parameter again autocorrelation matrix Eigenvalues Decomposition module 228 being obtained is sent into blind subspace code auxiliary filter vector calculation module 229 the filter vector element w finally blind subspace code auxiliary filter vector calculation module 229 being obtained ₁231, w ₂232...w _n23N sends into blind subspace code auxiliary filter module 202.

Like this, blind estimation autocorrelation matrix computing module 227, autocorrelation matrix Eigenvalues Decomposition module 228, blind subspace code auxiliary filter vector calculation module 229 has formed blind subspace code auxiliary filter vector calculation module 203.

Describe Fig. 3 high performance blind subspace code householder method and existing blind direct code householder method suppress the performance simulation contrast curved surface of accurate deterministic disturbance, wherein Fig. 3 a describes the performance comparison curved surface that audio reception disturbs, and Fig. 3 b describes to receive the performance comparison curved surface that digital arrowband disturbs.

The cdma system that simulated conditions is set spreading gain N=63 comprises 4 users, and wherein user 0 is desired user, signal power, i.e. unit signal energy other MAI and desired user constant power; CDMA spreading code is all chosen the Gold sequence of coefficient correlation 1/N.Channel circumstance white Gaussian noise power spectral density be that the relative ambient noise power of signal power is 20dB (after despreading).In Fig. 3 a, setting audio disturbs as single-tone interference and normalized frequency f ' _i=2/N ,-10dB≤J≤30dB; In Fig. 3 b, set the interference of digital arrowband and only exist a digital arrowband to disturb, N _v=4 and-10dB≤J≤30dB.In diagram of block, variable is respectively the power-10dB≤J≤30dB disturbing by accurate qualitative arrowband and receives signal number 50≤L≤1000, result is output Signal to Interference plus Noise Ratio (SINR, Signal to Interference and Noise Rate), unit is dB.

As shown in Figure 3 a, SINR 301 expression system output SINR, L 302 represents to receive signal number, and J 303 represents interference power.Curved surface 304 represents blind direct code householder method output SINR filtering curved surface, and curved surface 305 represents blind subspace code householder method output SINR filtering curved surface.As shown in the figure, when suppressing audio disturbances, the performance of blind subspace code householder method is obviously better than blind direct blind coding householder method.

As shown in Figure 3 b, SINR 306 expression system output SINR, L 307 represents to receive signal number, and J 308 represents interference power.Curved surface 309 represents blind direct code householder method output SINR filtering curved surface, and curved surface 310 represents blind subspace code householder method output SINR filtering curved surface.As shown in the figure, when suppressing the interference of digital arrowband, the performance of blind subspace code householder method is obviously better than blind direct blind coding householder method.

Fig. 4 is the flow chart of method.In step 401, wireless communication signals 205 is down-converted to intermediate-freuqncy signal 210.In step 402, first use modulus a/d transducer 211 that intermediate-freuqncy signal 210 is digitized as to digital signal, carry out thereafter QPSK demodulation output baseband signal 222.In step 403, baseband signal 222 is obtained to sampled signal r (m) 224 by cutting general matched filtering sampler 223.In step 404, sampled signal r (m) 224 is obtained to windowing vector r (n) 226 by windowing memory 225.In step 405, by blind estimation autocorrelation matrix computing module 227 counting statistics autocorrelation matrixes in step 406, by 228 pairs of autocorrelation matrix characteristic values of autocorrelation matrix Eigenvalues Decomposition module, decompose in step 407, by blind subspace code auxiliary filter vector calculation module 229, calculate blind subspace code auxiliary filter vector in step 408, the filter vector element w that blind subspace code auxiliary filter vector calculation module 203 is obtained ₁231, w ₂232...w _n23N sends into linear combination estimator 1:w with element r (nN+N-1) 241, r (nN) 242...r (nN+N-2) 24N of windowing vector r (n) 226 respectively ₁r (nN+N-1) 251, linear combination estimator 2:w ₂r (nN+N-2) 252... linear combination estimator N:w _nr (nN) 25N, obtains linear combination signal through adder 261 in step 409, by bit estimated signal send into symbol judgement device 262, obtain useful signal 263.In step 410, symbolic label increases progressively n=n+1.In step 411, judge whether n is greater than transmitted signal information sum, if so, process ends, if not, as from 411 send to return to arrow indicated.

Claims (1)

1. suppress the subspace code householder method that the accurate qualitative arrowband of wireless CDMA systems disturbs, it is characterized in that comprising:

(1) receive wireless communication signals;

(2) described wireless communication signals is down-converted to intermediate-freuqncy signal;

(3) intermediate-freuqncy signal described in digitlization, to provide digital signal;

(4) by described digital demodulation signal, so that baseband signal to be provided;

(5) by described baseband signal envelope r (t) by cutting general matched filtering so that sampled signal r (m) to be provided, wherein m is sampled signal label and m=0,1,2

Sampled signal r (m) can be modeled as:

r(m)＝y(m)+i(m)+ε(m)

＝y ₀(m)+z(m)+i(m)+ε(m) (1)

Y in formula (m) represents CDMA sampled signal, comprises the signal component y of desired user 0 ₀(m) and multiple access disturb the signal component z (m) of MAI (Multiple Access Interference); I (m) represents accurate qualitative arrowband interference sample signal; ε (m) represents that power spectral density is white Gaussian noise sampled signal, ≤ 0.01, and system synchronization;

Wherein CDMA sampled signal can be modeled as:

$y\left(m\right)=\underset{k=0}{\overset{K_z}{\mathrm{\Σ}}}{A}_k\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_k\left(n\right)s_k(m-\mathrm{nN})---\left(2\right)$

Wherein the sampled signal model of desired user 0 and MAI is respectively:

$y_0\left(m\right)=A_0\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_0\left(n\right)s_0(m-\mathrm{nN})---\left(3\right)$

$z\left(m\right)=\underset{k=0}{\overset{K_z}{\mathrm{\Σ}}}{A}_k\underset{n=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_k\left(n\right)s_k(m-\mathrm{nN})---\left(4\right)$

K in formula _zrepresent MAI number of users (K _z>=1), A _krepresent that k user receives signal amplitude, A ₀for desired user receives signal amplitude, b _k(n) represent k subscriber signal stream (1 or-1), b ₀(n) be expectation subscriber signal stream, s _krepresent k user's direct sequence spread spectrum codes sampled value, s ₀for desired user direct sequence spread spectrum codes sampled value, before sampling, time domain frequency expansion sequence is:

$s_k\left(t\right)=\frac{1}{\sqrt{N}}\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{s}_{k,j}{\mathrm{\ψ}}_c(t-j{T}_c)---\left(5\right)$

{ s in formula _{k, j}: j=1 ..., the spreading code that N} is k user (1 or-1), and k=0,1 ... K _z, ψ _c() is duration T _cnormalization waveform, N is spreading gain, is defined as N=T _b/ T _c, T _bfor the signal period, T _cfor the spreading code cycle, and spreading code is independent of signal;

Accurately determining signal comprises that audio disturbances and digital arrowband disturb, and wherein audio disturbances sampled signal can be modeled as:

$\begin{array}{c}i\left(m\right)=\frac{1}{\sqrt{N}}\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}\sin c\left(f_{\mathrm{ik}}{T}_c\right)\cos (2\mathrm{\π}{f}_{\mathrm{ik}}{T}_{c}m+\mathrm{\π}{f}_{\mathrm{ik}}{T}_c)\\ =\frac{1}{\sqrt{N}}\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}^{\′}\cos \left(\mathrm{\π}{f}_{\mathrm{ik}}^{\′}(2m+1)\right)\end{array}---\left(6\right)$

K in formula _irepresent audio disturbances number, A _ikrepresent that k audio disturbances receives signal amplitude, f _ikexpression is with respect to k audio disturbances frequency of CDMA carrier frequency, A ' _ikexpression audio disturbances normalization amplitude A ' _ik=A _iksinc (f _ikt _c), N is spreading gain, f ' _ikrepresent audio disturbances normalized frequency f ' _ik=f _ikt _c, and Nf ' _ikfor integer;

Numeral arrowband interference sample signal can be modeled as:

$i\left(m\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}\frac{1}{\sqrt{N}}\underset{n_i=-\∞}{\overset{\∞}{\mathrm{\Σ}}}{b}_{\mathrm{ik}}\left(n_i\right)\mathrm{\υ}(m-n_i{N}_{\mathrm{vk}})---\left(7\right)$

K in formula _irepresentative digit arrowband disturbs number, A _ikrepresent that k digital arrowband disturbs reception signal amplitude, b _ik(n) represent k digital narrow-band interference signal stream (1 or-1), the duration is T _ik, T _ikrepresent k digital narrow-band interference signal stream duration, i.e. digital arrowband interference period, here T _ik> > T _cand N _vk=T _b/ T _ikfor integer, υ () representation unit height rectangular pulse, N is spreading gain;

(6) by described sampled signal r (m) by windowing memory, for n transmitted signal, processing interval [nT _b, (n+1) T _b] in to sampled signal r (m) windowing obtain N * 1 dimension windowing vector r (n)=[r (nN+N-1), r (nN+N-2) ..., r (nN)] ^t, wherein n is transmitted signal label and n=0,1,2 ..., T _bfor the signal period, N is spreading gain;

Windowing vector r (n) can be modeled as:

r(n)＝y(n)+i(n)+ε(n)

(8)

＝y ₀(n)+z(n)+i(n)+ε(n)

R in formula (n) is for receiving signal windowing vector, and y (n) is CDMA windowing signal, comprises the signal component y of desired user 0 ₀and the signal component z of MAI (n) (n), i (n) disturbs windowing signal at the certainty arrowband that is as the criterion, and ε (n) is white Gaussian noise windowing signal;

CDMA windowing vector can be modeled as:

$y\left(n\right)=\underset{k=0}{\overset{K_z}{\mathrm{\Σ}}}{A}_k{b}_k\left(n\right)s_k---\left(9\right)$

The windowing signal model that wherein comprises desired user 0 and MAI:

y ₀(n)＝A ₀b ₀(n)s ₀ (10)

$z\left(n\right)=\underset{k=0}{\overset{K_z}{\mathrm{\Σ}}}{A}_k{b}_k\left(n\right)s_k---\left(11\right)$

S in formula _krepresent spreading code windowing vector, be defined as s ₀represent desired user direct sequence spread spectrum codes windowing vector, be defined as

Audio disturbances windowing vector can be modeled as:

$i\left(n\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A}_{\mathrm{ik}}^{\′}{s}_{\mathrm{ik}}---\left(12\right)$

S in formula _ikrepresent audio disturbances signal stream windowing vector, be defined as $s_{\mathrm{ik}}=\frac{1}{\sqrt{N}}{[\cos (2(N-1)\mathrm{\π}{f}_{\mathrm{ik}}^{\′}+\mathrm{\π}{f}_{\mathrm{ik}}^{\′}),\cos (2(N-2)\mathrm{\π}{f}_{\mathrm{ik}}^{\′}+\mathrm{\π}{f}_{\mathrm{ik}}^{\′}),...,\cos \left(\mathrm{\π}{f}_{\mathrm{ik}}^{\′}\right)]}^T;$

Numeral arrowband disturbs windowing vector to be modeled as:

$i\left(n\right)=\underset{k=1}{\overset{K_i}{\mathrm{\Σ}}}{A_{\mathrm{ik}}s}_{\mathrm{ik}}---\left(13\right)$

S in formula _ikrepresentative digit narrow-band interference signal stream windowing vector, is defined as $s_{\mathrm{ik}}\left(n\right)=\frac{1}{\sqrt{N}}{[b_{\mathrm{ik}}(N_{\mathrm{vk}}-1)...,b_{\mathrm{ik}}(N_{\mathrm{vk}}-2)...,...,b_{\mathrm{ik}}\left(0\right)...]}^T,$ And wherein each symbol repeats N _ik=T _ik/ T _cindividual element, N _ik=T _ik/ T _cfor digital arrowband obstacle gain, T _ikbe k digital narrow-band interference signal stream duration, i.e. digital arrowband interference period;

(7) described windowing vector r (n) is carried out to high-performance filtering, wherein high-performance filtering refers to and takes subspace code householder method, comprises the steps:

Step 1: blind estimation autocorrelation matrix

$R_{\mathrm{rr}}\left(n\right)=\frac{1}{L}\underset{n=0}{\overset{L-1}{\mathrm{\Σ}}}r\left(n\right)r^T\left(n\right)---\left(14\right)$

In formula, L represents to estimate the windowing number of autocorrelation matrix, receives signal number, sets 50≤L≤1000 in the present invention;

Step 2: autocorrelation matrix Eigenvalues Decomposition

$R_{\mathrm{rr}}=U_s{\mathrm{\Λ}}_s{U}_s^T+{\mathrm{\σ}}_{\mathrm{\ϵ}}^2{U}_{\mathrm{\ϵ}}{U}_{\mathrm{\ϵ}}^T---\left(15\right)$

In formula represent white Gaussian noise power spectral density, Λ _s=diag{ λ ₁, λ ₂..., λ _kcomprise K=1+K _z+ K _ithe individual characteristic value that is greater than diagonal matrix, U _s=[u ₁, u ₂..., u _k] be the matrix that a corresponding K orthogonal vectors form, meet i _nrepresent N dimension diagonal matrix, N is spreading gain, U _ε=[u _k+1, u _k+2..., u _n] be N-K characteristic value the matrix that corresponding orthogonal vectors form, $\mathrm{range}\{s_0,s_1,...,s_{K_z},s_{i1},...,s_{{\mathrm{iK}}_i}\}=\mathrm{range}\left\{U_s\right\}$ For CDMA user disturbs the virtual CDMA subspace of opening with accurate qualitative arrowband, its quadrature component is U _εthe noise subspace opened of row;

Step 3: calculate blind subspace code auxiliary filter vector

Setting blind subspace code auxiliary filter vector is w=[w ₁, w ₂..., w _n] ^t(N * 1 dimension), by w=U _sx substitution minimum output energy MMOE (Minimum Mean Output Energy) code aided algorithm cost function, x is intermediate variable:

$\left\{\begin{array}{c}{w}_{\mathrm{MMOE}}=\arg \underset{w}{\min }E\left\{{\left|w^{T}r\left(n\right)\right|}^2\right\}\\ w^T{s}_0=1\end{array}\right.---\left(16\right)$

Obtain:

In formula, ξ is Lagrange factor, and making formula (17) gradient is zero, obtains:

$x_{\mathrm{MMOE}}=-\frac{\mathrm{\ξ}}{2}{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0---\left(18\right)$

X in formula _mMOEfor intermediate variable, by formula (18) substitution constraints the limited Lagrange factor generation time formula (18) obtaining, can obtain:

$x_{\mathrm{MMOE}}=\frac{{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{\left(U_s^T{R}_{\mathrm{rr}}{U}_s\right)}^{-1}{U}_s^T{s}_0}---\left(19\right)$

According to u wherein _swith U _εthe character of quadrature, can obtain:

$U_s^T{R}_{\mathrm{rr}}{U}_s=U_s^T(U_s{\mathrm{\Λ}}_s{U}_s^T+{\mathrm{\σ}}_{\mathrm{\ϵ}}^2{U}_{\mathrm{\ϵ}}{U}_{\mathrm{\ϵ}}^T)U_s={\mathrm{\Λ}}_s---\left(20\right)$

Bring formula (20) into formula (19) and obtain x _mMOE, by x _mMOEsubstitution w=U _sx, can obtain blind subspace MMOE code aided algorithm filter vector expression formula:

$w_{\mathrm{MMOE}}=\frac{U_s{\mathrm{\Λ}}_s^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{A}_s^{-1}{U}_s^T{s}_0}---\left(21\right)$

Because needs are got symbol after filter vector is processed, therefore can do the processing of casting out denominator, obtain:

$w\∝U_s{\mathrm{\Λ}}_s^{-1}{U}_s^T{s}_0---\left(22\right)$

Formula (22) is blind subspace code aided algorithm filter vector;

Step 4: direct-detection symbol judgement

Regular according to direct-detection:

${\hat{b}}_0\left(n\right)=w^{T}r\left(n\right)---\left(23\right)$

After detection, carry out symbol judgement and obtain useful information, in formula for the useful information obtaining.