CN102624420A - Subspace zero forcing code assist method for suppressing code division multiple access (CDMA) system digit narrowband interference - Google Patents

Abstract

In order to solve a problem that a computation complexity is high when existing direct zero forcing (ZF) code assist method is used to suppress digit narrowband interference in a code division multiple access (CDMA) system, the invention provides a subspace ZF 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. Performance of the subspace ZF code assist method provided in the invention is similar with the direct ZF code assist method but computation complexity is lower than the direct ZF code assist method so that high-performance digit narrowband interference suppression with the low computation complexity can be realized.

Description

Suppress the subspace of cdma system numeral narrow band interference and compel zero sign indicating number householder method

Technical field

The present invention relates in the cdma wireless communication system, suppress the method for digital narrow band interference.The present invention be more particularly directed to solve the existing high problem of zero (ZF, Zero Forcing) sign indicating number householder method computation complexity of directly compeling.

Background technology

The reason that spread spectrum system is used widely in wireless channel is frequency selective fading and its superior function in being total to the road channel that it can effectively cause anti-multipath; Wherein representative core technology is code division multiple access (CDMA, a Code Division Multiple Access) technology.Narrow band interference is often to get involved the interference of spread spectrum system, and wherein digital narrow band interference is a kind of novel narrow band interference type.Though self possesses certain anti-interference capability spread spectrum system, effectively interference mitigation technology can significantly improve systematic function.

Initial spread spectrum Anti-Jamming Technique originates from the seventies in 20th century; Until the end of the eighties, the main focus of Anti-Jamming Technique is that main scientific research personnel achieves the highest with Milstein on the directly-enlarging system Suppression of narrow band interference based on prediction/estimation filtering and frequency domain filtering; Summary like document " L B Milstein; Interference rejectiontechniques in spread spectrum communication, IEEE Proceedings, 1988 ".Get into the mid-90; Along with the arrival of CDMA research boom, focus has been transferred to disturb in the associating inhibition, like the summary of document " H V Poor; L A Rusch; Narrowbandinterference suppression in spread spectrum CDMA, IEEE PersonalCommunication, 1994 " of cdma system based on technology such as linear prediction, nonlinear prediction and Multiuser Detection more with the scientific research personnel headed by Poor and the Rusch.20th century; Scientific research personnel such as Wang further develop into Predicting Technique, transform domain technology and sign indicating number ancillary technique with the cdma system interference mitigation technology; Summary like document " Xiaodong Wang, H V Poor, Wireless Communication Systems-AdvancedTechniques for Signal Reception; Beijing:Publishing House of Electronics Industry, 2005 ".In above each technology, Predicting Technique comprises linear prediction and nonlinear prediction two big branches, and its research concentrates in the improvement that receives structure; But owing to taked the processing mode by bit, the typical problem of existence is that the error rate is high, like document " J Wang; L B Milstein; Adaptive LMS filters forcellular CDMA overlay situations, IEEE Select Areas Commun, 1996 ".Under this viewpoint, utilize the signal code characteristic to carry out the sign indicating number ancillary technique that piece handles and seem especially effective, it is that multi-user system is disturbed one of the most promising technology of inhibition, the achievement of Poor and Wang is the highest in this field.In existing sign indicating number ancillary technique, directly compel zero (ZF, Zero Forcing) sign indicating number householder method because excellent performance and be easy to the self adaptation realization and obtained broad research; Like document " S Buzzi; M Lops, H V Poor, Code-Aided Interference Suppression for DS/CDMA Overlay Systems; IEEEProcessing, 2002 ".But directly computation complexity is high, the problem of waste system resource because matrix inversion exists for ZF sign indicating number ancillary technique.

Accordingly, reduce direct ZF sign indicating number householder method computation complexity, the method that does not reduce simultaneously systematic function is needs significantly.

Summary of the invention

Technology of the present invention is dealt with problems and is: a kind of subspace ZF sign indicating number householder method that can suppress digital narrow band interference in the cdma system is provided, can reduces existing direct ZF sign indicating number householder method computation complexity, keep system's high output Signal to Interference plus Noise Ratio performance simultaneously.

Technical solution of the present invention is: the subspace of high-performance low computational complexity compels zero, and (ZF, ZeroForcing) the sign indicating number householder method comprises the following steps:

(1) receives wireless communication signals, comprise CDMA signal, white noise and digital narrow band interference.

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

(3) the said intermediate-freuqncy signal of digitlization is to provide digital signal.

(4) with said digital demodulation signal, so that baseband signal to be provided.

(5) with said baseband signal envelope r (t) through cutting general matched filtering so that sampled signal r to be provided (m), 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 the formula (m) representes the 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) representes digital narrow band interference sampled signal; ε (m) expression power spectral density does

The white Gaussian noise sampled signal, And system synchronization.

The 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 the formula _zExpression 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) k subscriber signal stream of expression (1 or-1), b ₀(n) be expectation subscriber signal stream, s _kRepresent k user's direct sequence spread spectrum codes sampled value, s ₀Be desired user direct sequence spread spectrum codes sampled value, the time domain frequency expansion sequence does before the sampling

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

{ s in the formula _{K, j}: j=1 ..., N} is k user's a spreading code (1 or-1), and k=0,1 ... K _Z, ψ _c() is duration T _cReturn-change waveform, N=T _b/ T _cBe spreading gain, T _bBe the signal period, T _cBe the spreading code cycle, and spreading code is independent of signal.

Numeral narrow band interference sampled 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 the formula _iRepresent digital narrow band interference number, A _IkRepresent that k digital narrow band interference receives signal amplitude, b _Ik(n) k digital narrow-band interference signal stream of expression (1 or-1), the duration is T _Ik, the promptly digital narrow band interference cycle.Here T _Ik＞＞T _cAnd N _Vk=T _b/ T _IkBe integer, υ () representation unit height rectangular pulse.

(6) with said sampled signal r (m) through the windowing memory, send signal for n, handling [nT at interval _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 for sending signal label and n=0,1,2 ..., T _bBe the signal period, N is a 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 the formula (n) comprises the signal component y of desired user 0 for CDMA adds window signal ₀(n) and the signal component z of MAI (n), i (n) adds window signal for digital narrow band interference, and ε (n) adds window signal for white Gaussian noise.

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 the formula $s_k=\frac{1}{\sqrt{N}}{[s_{k,N-1},s_{k,N-2},\&CenterDot;\&CenterDot;\&CenterDot;,s_{k,0}]}^T,$ ${\mathrm{<figure-callout\; id="0"\; label="s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">s</figure-callout>}}_0=\frac{1}{\sqrt{N}}{[{\mathrm{<figure-callout\; id="0"\; label="s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">s</figure-callout>}}_{0,N-1},{\mathrm{<figure-callout\; id="0"\; label="s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">s</figure-callout>}}_{0,N-2},\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="0"\; label="s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">s</figure-callout>}}_{0,0}]}^T.$

Numeral narrow band interference windowing vector can be modeled as:

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

In the formula

And wherein each symbol repeats N _Ik=T _Ik/ T _cIndividual element, N _Ik=T _Ik/ T _cBe digital narrow band interference gain.

(7) said windowing vector r (n) is carried out the auxiliary filtering of subspace ZF sign indicating number of high-performance low computational complexity, comprise the steps:

Step 1: estimate the statistics autocorrelation matrix

R _rr(n)＝E{r(n)r ^T(n)|b}

＝R _yy(n)+R _ii(n)+R _εε(n)

＝R _yy0(n)+R _zz(n)+R _ii(n)+R _εε(n)

R in the formula _Yy(n)=E{y (n) y ^T(n) } be CDMA windowing signal autocorrelation matrix, comprise the autocorrelation matrix of desired user 0

Autocorrelation matrix R with MAI _Zz(n)=E{z (n) z ^T(n) }, R _Ii(n)=E{i (n) i ^T(n) } be digital narrow band interference windowing signal autocorrelation matrix, R _{ε ε}(n)=E{ ε (n) ε ^T(n) } be white Gaussian noise windowing autocorrelation matrix.

FI estimates the CDMA autocorrelation matrix

$R_{\mathrm{yy}}=\underset{k=0}{\overset{K_z}{\mathrm{\&Sigma;}}}{A}_k^2{s}_k{s}_k^T$

The autocorrelation matrix that comprises desired user 0 and MAI

${\mathrm{<figure-callout\; id="0"\; label="R\; yy"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">R</figure-callout>}}_{\mathrm{yy}}=A_0^2{s}_0{s}_0^T$

Estimative figure narrow band interference autocorrelation matrix

$R_{\mathrm{ii}}=\underset{k=1}{\overset{K_i}{\mathrm{\&Sigma;}}}{A}_{\mathrm{ik}}^{\&prime;2}{s}_{\mathrm{ik}}{s}_{\mathrm{ik}}^T$

Estimate the white Gaussian noise autocorrelation matrix

$R_{\mathrm{\&epsiv;\&epsiv;}}\left(n\right)={\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N$

I in the formula _NExpression N dimension diagonal matrix.

Step 2: autocorrelation matrix characteristic value decomposition

$R_{\mathrm{rr}}=U_s{\mathrm{\&Lambda;}}_s{U}_s^T+{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{U}_{\mathrm{\&epsiv;}}{U}_{\mathrm{\&epsiv;}}^T$

Λ in the formula _s=diag{ λ ₁, λ ₂..., λ _KComprise K=1+K _z+ K _iIndividual greater than characteristic value Diagonal matrix, U _s={ u ₁, u ₂..., u _K] be the matrix that a corresponding K orthogonal vectors constitute, satisfy

U _ε=[u _K+1, u _K+2..., u _N] be N-K characteristic value

The matrix that corresponding orthogonal vectors constitute,

Be the virtual CDMA subspace that CDMA user and digital narrow band interference are opened, its quadrature component is U _εThe noise subspace opened of row.

Step 3: estimator space ZF filter vector

Setting the auxiliary filter vector of subspace ZF sign indicating number is w=[w ₁, w ₂..., w _N] ^T(N * 1 dimension) is with w=U _sX substitution ZF sign indicating number aided algorithm cost function, x is an intermediate variable

$\left\{\begin{array}{c}{w}_{\mathrm{ZF}}=\arg \underset{w}{\min }E\left\{{\left|w^T(r\left(n\right)-\mathrm{\&epsiv;}\left(n\right))\right|}^2\right\}\\ w^{\mathrm{<figure-callout\; id="0"\; label="T\; s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">T</figure-callout>}}{s}_{}{}_0=1\end{array}\right.$

Obtain

Make that the following formula gradient is zero, obtain

$x_{\mathrm{ZF}}=-\frac{\mathrm{\&xi;}}{2}{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0$

Limited Lagrangian factor generation time following formula with following formula substitution constraints

obtains can get

$x_{\mathrm{ZF}}=\frac{{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0}$

According to

U wherein _sWith U _εThe character of quadrature can obtain

$U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s=U_s^T(R_{\mathrm{rr}}-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N)U_s$

$=U_s^T(U_s{\mathrm{\&Lambda;}}_s{U}_s^T+{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{U}_{\mathrm{\&epsiv;}}{U}_{\mathrm{\&epsiv;}}^T-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N)U_s$

$={\mathrm{\&Lambda;}}_s-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K$

The x that above two formulas are obtained _ZFSubstitution w=U _sX can estimator space ZF sign indicating number aided algorithm filter vector expression formula

$w_{\mathrm{ZF}}=\frac{{U_s\left({{\mathrm{\&Lambda;}}_s-\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{({\mathrm{\&Lambda;}}_s-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K)}^{-1}{U}_s^T{s}_0}$

Step 4: directly detected symbol judgement

According to directly detecting rule

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

Carry out symbol judgement after the detection and promptly obtain useful information.

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

The present invention has realized the high-performance low computational complexity filtering of cdma system numeral narrow band interference.The subspace ZF sign indicating number householder method matrix inversion part that provides is improved to K dimension diagonal matrix (K＜N) by N * N dimension non-singular matrix of existing direct ZF sign indicating number householder method; Greatly reduce the system-computed complexity, kept the height output Signal to Interference plus Noise Ratio performance close simultaneously with direct ZF sign indicating number householder method.

Description of drawings

Fig. 1 describes the cdma system reception block diagram that existing direct ZF sign indicating number householder method suppresses digital narrow band interference;

The cdma system that the subspace ZF sign indicating number householder method that Fig. 2 describes the high-performance low computational complexity suppresses digital narrow band interference receives block diagram;

Fig. 3 describes the subspace ZF sign indicating number householder method of high-performance low computational complexity and the performance simulation correlation curve that existing direct ZF sign indicating number householder method suppresses digital narrow band interference;

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

Embodiment

In the detailed description of the present invention, with reference to appended drawing, the specific exemplary embodiment of these accompanying drawing explainations invention can be implemented in these exemplary embodiments below.These embodiment describe with sufficient details, allowing those skilled in the art's embodiment of the present invention, but can utilize other embodiment, and can make logic, machinery, electrical equipment with other change, and do not depart from standard of the present invention.Therefore, following detailed should not regarded as restrictive, and scope of the present invention is limited by appended claims only.

The embodiment of subspace ZF sign indicating number householder method comprises the following steps:

(1) receives wireless communication signals, comprise CDMA signal, white noise and digital narrow band interference.

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

(3) the said intermediate-freuqncy signal of digitlization is to provide digital signal.

(4) with said digital demodulation signal, so that baseband signal to be provided.

(5) with said baseband signal envelope r (t) through cutting general matched filtering so that sampled signal r to be provided (m), 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 the formula (m) representes the 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) representes digital narrow band interference sampled signal; ε (m) expression power spectral density does The white Gaussian noise sampled signal,

And system synchronization.

The CDMA sampled signal can be modeled as:

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

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

$y_0\left(m\right)=A_0\underset{n=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{b}_0\left(n\right)s_0(m-\mathrm{nN})$

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

K in the formula _zExpression 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) k subscriber signal stream of expression (1 or-1), b ₀(n) be expectation subscriber signal stream, s _kRepresent k user's direct sequence spread spectrum codes sampled value, s ₀Be desired user direct sequence spread spectrum codes sampled value, the time domain frequency expansion sequence does before the sampling

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

{ s in the formula _{K, j}: j=1 ..., N} is k user's a spreading code (1 or-1), and k=0,1 ... K _Z, ψ _c() is duration T _cThe normalization waveform, N=T _b/ T _cBe spreading gain, T _bBe the signal period, T _cBe the spreading code cycle, and spreading code is independent of signal.

Numeral narrow band interference sampled signal can be modeled as:

$i\left(m\right)=\underset{k=1}{\overset{K_i}{\mathrm{\&Sigma;}}}{A}_{\mathrm{ik}}\frac{1}{\sqrt{N}}\underset{n_i=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{b}_{\mathrm{ik}}\left(n_i\right)\mathrm{\&upsi;}(m-n_i{N}_{\mathrm{vk}})$

K in the formula _iRepresent digital narrow band interference number, A _IkRepresent that k digital narrow band interference receives signal amplitude, b _Ik(n) k digital narrow-band interference signal stream of expression (1 or-1), the duration is T _Ik, the promptly digital narrow band interference cycle.Here T _Ik＞＞T _cAnd N _Vk=T _b/ T _IkBe integer, υ () representation unit height rectangular pulse.

(6) with said sampled signal r (m) through the windowing memory, send signal for n, handling [nT at interval _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 for sending signal label and n=0,1,2 ..., T _bBe the signal period, N is a 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 the formula (n) comprises the signal component y of desired user 0 for CDMA adds window signal ₀(n) and the signal component z of MAI (n), i (n) adds window signal for digital narrow band interference, and ε (n) adds window signal for white Gaussian noise.

CDMA windowing vector can be modeled as:

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

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

y ₀(n)＝A ₀b ₀(n)s ₀

In the formula $s_k=\frac{1}{\sqrt{N}}{[s_{k,N-1},s_{k,N-2},\&CenterDot;\&CenterDot;\&CenterDot;,s_{k,0}]}^T,$ ${\mathrm{<figure-callout\; id="0"\; label="s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">s</figure-callout>}}_0=\frac{1}{\sqrt{N}}{[{\mathrm{<figure-callout\; id="0"\; label="s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">s</figure-callout>}}_{0,N-1},{\mathrm{<figure-callout\; id="0"\; label="s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">s</figure-callout>}}_{0,N-2},\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="0"\; label="s"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">s</figure-callout>}}_{0,0}]}^T.$

Numeral narrow band interference windowing vector can be modeled as:

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

In the formula

And wherein each symbol repeats N _Ik=T _Ik/ T _cIndividual element, N _Ik=T _Ik/ T _cBe digital narrow band interference gain.

(7) said windowing vector r (n) is carried out the auxiliary filtering of subspace ZF sign indicating number of high-performance low computational complexity, comprising:

At first: estimate the statistics autocorrelation matrix

R _rr(n)＝E{r(n)r ^T(n)|b}

＝R _yy(n)+R _ii(n)+R _εε(n)

＝R _yy0(n)+R _zz(n)+R _ii(n)+R _εε(n)

R in the formula _Yy(n)=E{y (n) y ^T(n) } be CDMA windowing signal autocorrelation matrix, comprise the autocorrelation matrix of desired user 0

Autocorrelation matrix R with MAI _Zz(n)=E{z (n) z ^T(n) }, R _Ii(n)=E{i (n) i ^T(n) } be digital narrow band interference windowing signal autocorrelation matrix, R _{ε ε}(n)=E{ ε (n) ε ^T(n) } be white Gaussian noise windowing autocorrelation matrix.

Estimate the CDMA autocorrelation matrix

$R_{\mathrm{yy}}=\underset{k=0}{\overset{K_z}{\mathrm{\&Sigma;}}}{A}_k^2{s}_k{s}_k^T$

The autocorrelation matrix that comprises desired user 0 and MAI

${\mathrm{<figure-callout\; id="0"\; label="R\; yy"\; filenames="HSA00000429932800021.png,HSA00000429932800031.png"\; state="\{\{state\}\}">R</figure-callout>}}_{\mathrm{yy}}=A_0^2{s}_0{s}_0^T$

Estimative figure narrow band interference autocorrelation matrix

$R_{\mathrm{ii}}=\underset{k=1}{\overset{K_i}{\mathrm{\&Sigma;}}}{A}_{\mathrm{ik}}^{\&prime;2}{s}_{\mathrm{ik}}{s}_{\mathrm{ik}}^T$

Estimate the white Gaussian noise autocorrelation matrix

$R_{\mathrm{\&epsiv;\&epsiv;}}\left(n\right)={\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N$

I in the formula _NExpression N dimension diagonal matrix.

Secondly: the autocorrelation matrix characteristic value decomposition

$R_{\mathrm{rr}}=U_s{\mathrm{\&Lambda;}}_s{U}_s^T+{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{U}_{\mathrm{\&epsiv;}}{U}_{\mathrm{\&epsiv;}}^T$

Λ in the formula _s=diag{ λ ₁, λ ₂..., λ _KComprise K=1+K _z+ K _iIndividual greater than characteristic value Diagonal matrix, U _s=[u ₁, u ₂..., u _K] be the matrix that a corresponding K orthogonal vectors constitute, satisfy

U _ε=[u _K+1, u _K+2..., u _N] be N-K characteristic value

The matrix that corresponding orthogonal vectors constitute, Be the virtual CDMA subspace that CDMA user and digital narrow band interference are opened, its quadrature component is U _εThe noise subspace opened of row.

Once more: estimator space ZF filter vector

Setting the auxiliary filter vector of subspace ZF sign indicating number is w=[w ₁, w ₂..., w _N] ^T(N * 1 dimension) is with w=U _sX substitution ZF sign indicating number aided algorithm cost function, x is an intermediate variable

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

Obtain

Make that the following formula gradient is zero, obtain

$x_{\mathrm{ZF}}=-\frac{\mathrm{\&xi;}}{2}{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0$

Limited Lagrangian factor generation time following formula with following formula substitution constraints

obtains can get

$x_{\mathrm{ZF}}=\frac{{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0}$

According to

U wherein _sWith U _εThe character of quadrature can obtain

$U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s=U_s^T(R_{\mathrm{rr}}-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N)U_s$

$=U_s^T(U_s{\mathrm{\&Lambda;}}_s{U}_s^T+{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{U}_{\mathrm{\&epsiv;}}{U}_{\mathrm{\&epsiv;}}^T-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N)U_s$

$={\mathrm{\&Lambda;}}_s-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K$

The x that above two formulas are obtained _ZFSubstitution w=U _sX can estimator space ZF sign indicating number aided algorithm filter vector expression formula

$w_{\mathrm{ZF}}=\frac{{U_s\left({{\mathrm{\&Lambda;}}_s-\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{({\mathrm{\&Lambda;}}_s-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K)}^{-1}{U}_s^T{s}_0}$

At last: directly detected symbol judgement

According to directly detecting rule

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

Carry out symbol judgement after the detection and promptly obtain useful information.

Fig. 1 describes the cdma system reception block diagram that existing direct ZF sign indicating number householder method suppresses digital narrow band interference, is made up of down conversion module 101, the auxiliary filtration module 102 of direct ZF sign indicating number and the auxiliary filter vector computing module 103 of direct ZF sign indicating number.

As shown in the figure, the wireless communication signals 105 that antenna 104 receives comprises useful signal CDMA signal, digital narrow band interference and white Gaussian noise.Antenna 104 is coupled to down conversion module 101.In down conversion module 101, at first handle wireless communication signals 105 through band pass filter 106, this filter is optimally selected the frequency wanted, the frequency that for example joins with CDMA signal correction.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 oscillator signal from local oscillator 109.Like this, the output signal of blender 108 down-conversion amplifiers 107 is to provide intermediate-freuqncy signal 110.After initial down-conversion, intermediate-freuqncy signal 110 is transformed into digital signal 112 through modulus a/d transducer 111.Because the QPSK signal can be regarded as the stack of two quadrature 2PSK signals, so the coherent carrier that intersects with two-way goes demodulation.Wherein one road signal gets into blender 115 and mixes with signal from digital controlled oscillator 113, another road signal entering blender 116 and with mix from the signal of digital controlled oscillator 113 after pi/2 phase shift 114.The signal of blender 115 and blender 116 outputs is connected low pass filter 117 and low pass filter 118 respectively.Thereafter; The output signal of low pass filter 117 and the output signal of low pass filter 118 are connected sampling decision device 119 and sampling decision device 120 respectively; And the output signal of will sample decision device 119 and sampling decision device 120 becomes baseband signal 122 outputs after parallel/serial device 121 conversion.

Like this; Band pass filter 106, amplifier 107, blender 108, local oscillator 109 have been accomplished 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 been accomplished the process that is down-converted to baseband signal by intermediate-freuqncy signal.Above process has been accomplished down conversion module 101.

Existing direct ZF sign indicating number householder method is carried out filtering through the auxiliary filtration module 102 of direct ZF sign indicating number.Baseband signal 122 envelope r (t) are cut general matched filtering sampling 123, sampled signal r (m) 124 is provided.With sampled signal r (m) 124 through 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 the auxiliary filter vector computing module 103 of direct ZF sign indicating number 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 the linear combination signal through adder 161 At last, bit estimated signal

Send into symbol judgement device 162, obtain useful signal 163.

Like this, and general filtering sampling 123; Sampling windowing storage 125; Linear combination estimates 1 151, linear combination estimates that 2 152... linear combinations estimate N 15N; Adder 161; Symbol judgement device 162 has constituted the auxiliary filtration module 102 of direct ZF sign indicating number jointly.

The direct required filter vector element w of the auxiliary filtration module of ZF sign indicating number 102 ₁131, w ₂132...w _N13N is provided by the auxiliary filter vector computing module 103 of direct ZF sign indicating number.According to the channel estimating principle, windowing vector r (n) 126 is sent into estimation statistics autocorrelation matrix computing module 127R _Rr(n)=E{r (n) r ^T(n) | b}=R _Yy(n)+R _Ii(n)+R _{ε ε}(n)=R _Yy0(n)+R _Zz(n)+R _Ii(n)+R _{ε ε}(n).Send estimating the relevant parameter that statistics autocorrelation matrix computing module 127 obtains into the direct ZF filter vector computing module 128 of estimation

To estimate the filter vector element w that direct ZF filter vector computing module 128 obtains at last ₁131, w ₂132...w _N13N sends into the auxiliary filtration module 102 of direct ZF sign indicating number.

Like this, estimate statistics autocorrelation matrix computing module 127, add up direct ZF filter vector computing module 128 and constituted the auxiliary filter vector computing module 103 of direct ZF sign indicating number.

The cdma system that the subspace ZF sign indicating number householder method that Fig. 2 describes the high-performance low computational complexity suppresses digital narrow band interference receives block diagram, is made up of down conversion module 201, the auxiliary filtration module 202 of subspace ZF sign indicating number and the auxiliary filter vector computing module 203 of subspace ZF sign indicating number.

As shown in the figure, the wireless communication signals 205 that antenna 204 receives comprises useful signal CDMA signal, digital narrow band interference and white Gaussian noise.Antenna 204 is coupled to down conversion module 201.In down conversion module 201, at first handle wireless communication signals 205 through band pass filter 206, this filter is optimally selected the frequency wanted, the frequency that for example joins with CDMA signal correction.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 oscillator signal from local oscillator 209.Like this, the output signal of blender 208 down-conversion amplifiers 207 is to provide intermediate-freuqncy signal 210.After initial down-conversion, intermediate-freuqncy signal 210 is transformed into digital signal 212 through modulus a/d transducer 211.Because the QPSK signal can be regarded as the stack of two quadrature 2PSK signals, so the coherent carrier that intersects with two-way goes demodulation.Wherein one road signal gets into blender 215 and mixes with signal from digital controlled oscillator 213, another road signal entering blender 216 and with mix from the signal of digital controlled oscillator 213 after pi/2 phase shift 214.The signal of blender 215 and blender 216 outputs is connected low pass filter 217 and low pass filter 218 respectively.Thereafter; The output signal of low pass filter 217 and the output signal of low pass filter 218 are connected sampling decision device 219 and sampling decision device 220 respectively; And the output signal of will sample decision device 219 and sampling decision device 220 becomes baseband signal 222 outputs after parallel/serial device 221 conversion.

Like this; Band pass filter 206, amplifier 207, blender 208, local oscillator 209 have been accomplished 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 been accomplished the process that is down-converted to baseband signal by intermediate-freuqncy signal.Above process has been accomplished down conversion module 201.

Subspace ZF sign indicating number householder method is carried out filtering through the auxiliary filtration module 202 of subspace ZF sign indicating number.Baseband signal 222 envelope r (t) are cut general matched filtering sampling 223, sampled signal r (m) 224 is provided.With sampled signal r (m) 224 through 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 ZF sign indicating number auxiliary filter vector computing module 203 in subspace 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 the linear combination signal through adder 261

At last, with the bit estimated signal

Send into symbol judgement device 262, obtain useful signal 263.

Like this, and general filtering sampling 223; Sampling windowing storage 225; Linear combination estimates 1 251, linear combination estimates that 2 252... linear combinations estimate N 25N; Adder 261; Symbol judgement device 262 has constituted the auxiliary filtration module 202 of subspace ZF sign indicating number jointly.

The filter vector element w that the auxiliary filtration module of subspace ZF sign indicating number 202 is required ₁231, w ₂232...w _N23N is provided by the auxiliary filter vector computing module 203 of subspace ZF sign indicating number.According to the channel estimating principle, windowing vector r (n) 226 is sent into estimation statistics autocorrelation matrix computing module 227R _Rr(n)=E{r (n) r ^T(n) | b}=R _Yy(n)+R _Ii(n)+R _Zz(n)=R _Yy0(n)+R _Zz(n)+R _Ii(n)+R _{ε ε}(n).Send estimating the autocorrelation matrix that statistics autocorrelation matrix computing module 227 obtains into autocorrelation matrix characteristic value decomposition module 228

The relevant parameter that again autocorrelation matrix characteristic value decomposition module 228 is obtained is sent into estimator space ZF filter vector computing module 229 The filter vector element w that at last estimator space ZF filter vector computing module 229 is obtained ₁231, w ₂232...w _N23N sends into the auxiliary filtration module 202 of subspace ZF sign indicating number.

Like this, estimate statistics autocorrelation matrix computing module 227, autocorrelation matrix characteristic value decomposition module 228, statistics subspace ZF filter vector computing module 229 has constituted the auxiliary filter vector computing module 203 of subspace ZF sign indicating number.

Fig. 3 describes the subspace ZF sign indicating number householder method of high-performance low computational complexity and the performance simulation correlation curve that existing direct ZF sign indicating number householder method suppresses digital narrow band interference, wherein comprises the emulation correlation curve to single-tone disturbs and multitone disturbs respectively.

The cdma system that simulated conditions is set spreading gain N=63 comprises 4 users; Wherein user 0 is a desired user; Signal power, i.e. unit signal energy

other MAI and desired user constant power; The CDMA spreading code is all chosen the Gold sequence of coefficient correlation 1/N.Channel circumstance white Gaussian noise power spectral density

is that the relative ambient noise power of signal power is 20dB (after the despreading).Interference power is with " J " expression, and unit is dB.Set and only have a digital narrow band interference, N _v=4 and-10dB≤J≤30dB.Output Signal to Interference plus Noise Ratio (SINR, Signal to Interference and Noise Rate) has reflected the ratio of system's output mixed signal desired signal and interference and noise power, the dB of unit.

As shown in Figure 3, the output SINR of SINR 301 expression systems, J 302 expression interference powers.When disturbing to digital narrow band interference, the performance 303 of subspace ZF sign indicating number householder method is close with the performance 304 of direct ZF sign indicating number householder method.This has also proved for digital narrow band interference; The subspace ZF sign indicating number householder method that the present invention proposes is with directly ZF sign indicating number householder method performance is close; But its matrix inversion part is improved to K dimension diagonal matrix (K＜N), greatly reduce the system-computed complexity by the N * N dimension non-singular matrix of existing direct ZF sign indicating number householder method.Explain that ZF sign indicating number householder method more existing direct ZF sign indicating number householder method in subspace has significant advantage.

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, at first use modulus a/d transducer 211 that intermediate-freuqncy signal 210 is digitized as digital signal, carry out QPSK demodulation output baseband signal 222 thereafter.In step 403, baseband signal 222 is obtained sampled signal r (m) 224 through cutting general matched filtering sampler 223.In step 404, sampled signal r (m) 224 is obtained windowing vector r (n) 226 through windowing memory 225.In step 405, through estimating statistics autocorrelation matrix computing module 227 counting statistics autocorrelation matrix R _Rr(n)=E{r (n) r ^T(n) | b}=R _Yy(n)+R _Ii(n)+R _{ε ε}(n)=R _Yy0(n)+R _Zz(n)+R _Ii(n)+R _{ε ε}(n).In step 406, decompose through 228 pairs of autocorrelation matrix characteristic values of autocorrelation matrix characteristic value decomposition module

In step 407, through estimator space ZF filter vector computing module 229 calculating subspace ZF filter vectors

In step 408, the filter vector element w that ZF sign indicating number auxiliary filter vector computing module 203 in subspace 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 the linear combination signal through adder 261

In step 409, with the 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 n whether greater than sending the signal message sum, if, process ends, if not, as from 411 send return arrow indicated.

Claims (5)

1. high-performance low computational complexity method that in wireless CDMA systems, suppresses digital narrow band interference is characterized in that comprising:

(1) receives wireless communication signals;

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

(3) the said intermediate-freuqncy signal of digitlization is to provide digital signal;

(4) with said digital demodulation signal, so that baseband signal to be provided;

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

(6) with said sampled signal r (m) through the windowing memory, send signal for n, handling [nT at interval _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 for sending signal label and n=0,1,2 ..., T _bBe the signal period, N is a spreading gain;

(7) with the filtering of said windowing vector r (n) high-performance low computational complexity.

2. a kind of high-performance low computational complexity filtering method that in wireless CDMA systems, suppresses digital narrow band interference as claimed in claim 1 is characterized in that signal can be modeled as:

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 the formula (m) representes the 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) representes digital narrow band interference sampled signal; ε (m) expression power spectral density does

The white Gaussian noise sampled signal, And system synchronization;

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 the formula (n) comprises the signal component y of desired user 0 for CDMA adds window signal ₀(n) and the signal component z of MAI (n), i (n) adds window signal for digital narrow band interference, and ε (n) adds window signal for white Gaussian noise.

3. a kind of high-performance low computational complexity filtering method that in wireless CDMA systems, suppresses digital narrow band interference as claimed in claim 2 is characterized in that CDMA sampled signal and digital narrow band interference sampled signal can be modeled as:

The CDMA sampled signal can be modeled as:

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

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

$y_0\left(m\right)=A_0\underset{n=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{b}_0\left(n\right)s_0(m-\mathrm{nN})$

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

K in the formula _zExpression 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) k subscriber signal stream of expression (1 or-1), b ₀(n) be expectation subscriber signal stream, s _kRepresent k user's direct sequence spread spectrum codes sampled value, s ₀Be desired user direct sequence spread spectrum codes sampled value, the time domain frequency expansion sequence does before the sampling

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

{ s in the formula _{K, j}: j=1 ..., N} is k user's a spreading code (1 or-1), and k=0,1 ... K _Z, ψ _c() is duration T _cThe normalization waveform, N=T _b/ T _cBe spreading gain, T _bBe the signal period, T _cBe the spreading code cycle, and spreading code is independent of signal;

Numeral narrow band interference sampled signal can be modeled as:

$i\left(m\right)=\underset{k=1}{\overset{K_i}{\mathrm{\&Sigma;}}}{A}_{\mathrm{ik}}\frac{1}{\sqrt{N}}\underset{n_i=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{b}_{\mathrm{ik}}\left(n_i\right)\mathrm{\&upsi;}(m-n_i{N}_{\mathrm{vk}})$

K in the formula _iRepresent digital narrow band interference number, A _IkRepresent that k digital narrow band interference receives signal amplitude, b _Ik(n) k digital narrow-band interference signal stream of expression (1 or-1), the duration is T _Ik, the promptly digital narrow band interference cycle.Here T _Ik＞＞T _cAnd N _Vk=T _b/ T _IkBe integer, υ () representation unit height rectangular pulse.

4. a kind of high-performance low computational complexity filtering method that in wireless CDMA systems, suppresses digital narrow band interference as claimed in claim 2 is characterized in that CDMA windowing vector sum numeral narrow band interference windowing vector can be modeled as:

CDMA windowing vector can be modeled as:

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

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

y ₀(n)＝A ₀b ₀(n)s ₀

In the formula $s_k=\frac{1}{\sqrt{N}}{[s_{k,N-1},s_{k,N-2},\&CenterDot;\&CenterDot;\&CenterDot;,s_{k,0}]}^T,$ $s_0=\frac{1}{\sqrt{N}}{[s_{0,N-1},s_{0,N-2},\&CenterDot;\&CenterDot;\&CenterDot;,s_{0,0}]}^T;$

Numeral narrow band interference windowing vector can be modeled as:

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

In the formula

And wherein each symbol repeats N _Ik=T _Ik/ T _cIndividual element, N _Ik=T _Ik/ T _cBe digital narrow band interference gain.

5. a kind of high-performance low computational complexity filtering method that in wireless CDMA systems, suppresses digital narrow band interference as claimed in claim 1 is characterized in that taking the subspace to compel zero (ZF, Zero Forcing) sign indicating number householder method, comprises the steps:

Step 1: estimate the statistics autocorrelation matrix

R _rr(n)＝E{r(n)r ^T(n)|b}

＝R _yy(n)+R _ii(n)+R _εε(n)

＝R _yy0(n)+R _zz(n)+R _ii(n)+R _εε(n)

R in the formula _Yy(n)=E{y (n) y ^T(n) } be CDMA windowing signal autocorrelation matrix, comprise the autocorrelation matrix of desired user 0

Autocorrelation matrix R with MAI _Zz(n)=E{z (n) z ^T(n) }, R _Ii(n)=E{i (n) i ^T(n) } be digital narrow band interference windowing signal autocorrelation matrix, R _{ε ε}(n)=E{ ε (n) ε ^T(n) } be white Gaussian noise windowing autocorrelation matrix.

Estimate the CDMA autocorrelation matrix

$R_{\mathrm{yy}}=\underset{k=0}{\overset{K_z}{\mathrm{\&Sigma;}}}{A}_k^2{s}_k{s}_k^T$

The autocorrelation matrix that comprises desired user 0 and MAI

$R_{\mathrm{yy}0}=A_0^2{s}_0{s}_0^T$

Estimative figure narrow band interference autocorrelation matrix

$R_{\mathrm{ii}}=\underset{k=1}{\overset{K_i}{\mathrm{\&Sigma;}}}{A}_{\mathrm{ik}}^{\&prime;2}{s}_{\mathrm{ik}}{s}_{\mathrm{ik}}^T$

Estimate the white Gaussian noise autocorrelation matrix

$R_{\mathrm{\&epsiv;\&epsiv;}}\left(n\right)={\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N$

I in the formula _NExpression N dimension diagonal matrix;

Step 2: autocorrelation matrix characteristic value decomposition

$R_{\mathrm{rr}}=U_s{\mathrm{\&Lambda;}}_s{U}_s^T+{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{U}_{\mathrm{\&epsiv;}}{U}_{\mathrm{\&epsiv;}}^T$

Λ in the formula _s=diag{ λ ₁, λ ₂..., λ _KComprise K=1+K _z+ K _iIndividual greater than characteristic value

Diagonal matrix, U _s=[u ₁, u ₂..., u _K] be the matrix that a corresponding K orthogonal vectors constitute, satisfy

U _ε=[u _K+1, u _K+2..., u _N] be N-K characteristic value

The matrix that corresponding orthogonal vectors constitute,

Be the virtual CDMA subspace that CDMA user and digital narrow band interference are opened, its quadrature component is U _εThe noise subspace opened of row;

Step 3: estimator space ZF filter vector

Setting the auxiliary filter vector of subspace ZF sign indicating number is w=[w ₁, w ₂..., w _N] ^T(N * 1 dimension) is with w=U _sX substitution ZF sign indicating number aided algorithm cost function, x is an intermediate variable

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

Obtain

Make that the following formula gradient is zero, obtain

$x_{\mathrm{ZF}}=-\frac{\mathrm{\&xi;}}{2}{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0$

Limited Lagrangian factor generation time following formula with following formula substitution constraints

obtains can get

$x_{\mathrm{ZF}}=\frac{{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{\left(U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s\right)}^{-1}{U}_s^T{s}_0}$

According to

U wherein _sWith U _εThe character of quadrature can obtain

$U_s^T(R_{\mathrm{yy}}+R_{\mathrm{ii}})U_s=U_s^T(R_{\mathrm{rr}}-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N)U_s$

$=U_s^T(U_s{\mathrm{\&Lambda;}}_s{U}_s^T+{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{U}_{\mathrm{\&epsiv;}}{U}_{\mathrm{\&epsiv;}}^T-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_N)U_s$

$={\mathrm{\&Lambda;}}_s-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K$

The x that above two formulas are obtained _ZFSubstitution w=U _sX can estimator space ZF sign indicating number aided algorithm filter vector expression formula

$w_{\mathrm{ZF}}=\frac{{U_s\left({{\mathrm{\&Lambda;}}_s-\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K\right)}^{-1}{U}_s^T{s}_0}{s_0^T{U}_s{({\mathrm{\&Lambda;}}_s-{\mathrm{\&sigma;}}_{\mathrm{\&epsiv;}}^2{I}_K)}^{-1}{U}_s^T{s}_0}$

Step 4: directly detected symbol judgement

According to directly detecting rule

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

Carry out symbol judgement after the detection and promptly obtain useful information.

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