CN101001225A - High precision characteristic parameter adaptive blind estimating method - Google Patents

Abstract

This invention provides an accurate character parameter adaptive blind estimation method, which applies time-varying step to improve the convergency speed of the algorithm and get the best filter weighting coefficient quickly and applies modified soft judgement weight to maintain the Rodust of hard judgement errors and channel noises. This invention is realized easily and can be used in all kinds of communication systems applying OFDM for modulation, CDMA or TDMA. This idea can be used in all RLS algorithms and their derived algorithms and devices.

Description

The adaptive blind estimation method for characteristic parameter that precision is high

Technical field:

The present invention relates to the high adaptive blind estimation method for characteristic parameter of a kind of precision.Be particularly suitable for channel estimating, belong to the digital mobile communication field of using the electromagnetic wave technology, particularly Digital Television, single carrier ofdm communication system, multi-carrier OFDM communication system, wireless lan (wlan) etc. adopt the channel estimation technique in the OFDM modulated digital communication system.Simultaneously, the present invention not only can be used for carrying out channel estimating in code division multiple access (CDMA) and time division multiple access (TDMA) system, and thought of the present invention also can be used for all RLS methods with and the method for deriving in estimate other characteristic parameters in fields such as communication, radar, space flight, remote-control romote-sensing, sonar, image processing, computer vision, biomedical engineering.

Background technology:

As everyone knows, the high frequency band radio wave will be through multiple transmission environment reflection such as house, vehicle, high mountain or diffraction in transmission course.That is: the primary signal that receives of antenna amplitude difference not only, and phase place also there are differences.The result of these signal amplitude combinations will cause the violent fluctuation of signal amplitude, promptly so-called multipath fading.

In ofdm communication system, this multipath fading wireless channel be frequency selectivity with time selectivity, so, before the ofdm signal demodulation, dynamic channel is estimated it is very important.

Channel estimating can adopt to be inserted training sequence with certain cycle and realizes on the subcarrier of OFDM symbol, also can adopt the mode of inserting training sequence on each OFDM symbol.The former is called block-type pilot channel estimation, is mainly used in the estimation to slow fading channel.The latter is called the Comb Pilot channel estimating, is mainly used in the estimation of fast fading channel.These two kinds of methods all can be further divided into least square (Least Square, LS) method or least mean-square error (Minimum Mean-Square-Error, MMSE) method etc.Yet these methods have all taken useful bandwidth and have reduced data transmission efficiency.

The blind Channel Estimation Based that does not adopt training sequence then is another kind of thinking.The bandwidth resources that this method biggest advantage is exactly a preciousness of no use exchange performance for estimating channel for.

In the blind algorithm for estimating of channel, best blind algorithm for estimating must possess following 3 conditions.The first, the convergence of the blind algorithm for estimating of channel must be fast, and promptly channel must accurately estimate in the least possible symbol; The second, the adaptive adjustment capability of algorithm must be very strong so that can follow the trail of channel variation adaptively to satisfy the needs of communication; Three, the computation complexity of blind algorithm and hardware implementation complexity should be lower, to strengthen its practicality.

Although proposed many channel estimating and balanced blind algorithm in recent years, have seldom that algorithm can satisfy fast convergence rate simultaneously, adaptive ability is strong and computation complexity hangs down these three requirements.(as: subspace (SS) algorithm is seen document L.Tong and Q.Zhao to deterministic batch algorithm, " Joint order detection and channel estimation by least squares smoothing " .InProc.50th Conf.Information Science and Systems, Princeton, N.J., Mar.1998., cross-correlation (cross relation, CR) algorithm is seen document Q.Zhao and L.Tong, " Adaptive blind channel estimation by least squares smoothing ", IEEE Trans.SignalProcessing, 47 (11): 3000-3012, Nov.1999., the associating rank are detected and channel estimation method is seen document GXu, H.Liu, L.Tong, and T.Kailath, " Aleast-squares approach to blind channel identification " .IEEE Trans.Signal Processing, SP-43 (12): 2982-2993, Dec.1995. etc.) fast convergence rate, yet amount of calculation is big, and adaptive performance is poor.Linear prediction (LP) algorithm K.Abed-Meraim, E.Moulines, and P.Loubaton. " Prediction error method for second-order blind identification ", IEEE Trans.Signal Processing, SP-45 (3): 694-705, strengthen though March 1997. can follow the trail of the ability of channel, its performance depends critically upon the statistics irrelevance of list entries, and such Algorithm Convergence is relatively poor.It is big to utilize many side informations to carry out the algorithm computation amount of the blind estimation of channel.As: at document B.Muquet, M.deCourville, P.Duhamel.and V.Buenac, " A subspace basaed blind and semi-blind channel identification method for OFDM systems " .inProc.SPAWC, Annapolis, MD utilizes the subspace structure of time-domain signal correlation matrix to estimate channel among the May 1999..At document H.Wang, Y.Lin and B.Chen, " Blind OFDM channel estimation using receiver diversity ", in Proc.Conf.Info.Sci.Sys., Princeton, NJ by receive diversity, utilizes two reception antennas that the principle of the response unanimity of identical information symbol is obtained channel among the March2002..

In fact, recurrence least square (RLS) algorithm is to estimate and Predicting Technique for self adaptation as you know, has a wide range of applications at different aspect.Yet though this algorithm computation is simple, convergence rate is fast inadequately, and estimated performance is good inadequately.

Summary of the invention:

The objective of the invention is: propose the high adaptive blind estimation method for characteristic parameter of a kind of precision, these methods will improve convergence rate, strengthen adaptive ability and precision of channel estimation with respect to the characteristic parameter method of estimation of prior art, realize simple.

To achieve these goals, the present invention proposes the high adaptive blind estimation method for characteristic parameter of a kind of precision.Its technical scheme is: change constant step-length of the prior art into variable step size, at first allow the filter weight coefficient be a bigger value, after the filter weight coefficient converged to the best weights coefficient by the time, step-length reduced to obtain better estimated performance.Simultaneously, in order to keep the robustness of hard decision errors and interchannel noise, also the soft weight decisions of receiver soft decision information function as weight coefficient is weighted and revises.Thought in this patent, has proposed the high adaptive blind estimation method for characteristic parameter of a kind of precision thus, that is: the time change step length soft-decision weighted least-squares method (TVCPMSDWRLS) of correction.

The high adaptive blind estimation method for characteristic parameter of a kind of precision that the present invention proposes can estimate significant condition parameter (as: channel parameter) effectively.

Channel estimating model of the present invention and principle are described in detail as follows:

Theorem 1: suppose to have accurate decision error information, can utilize the weight coefficient of this information like this as the judgement of RLS method.The influence that this coefficient will make noise and mistake in judgment produce reduces.

Suppose θ _iAnd φ _iArgument in the demodulation process process when being soft-decision and hard decision respectively, definition p _iThe normalized value between [0,1] for difference between reflection soft-decision and the hard decision has:

$p_i=1-\frac{|{\mathrm{\φ}}_i-{\mathrm{\θ}}_i|}{\mathrm{\π}/S}---\left(1\right)$

Wherein, S is alternative number of symbols.

Because channel can be expressed as the tap time-delay of a plurality of time delays, so have:

u(n)＝[u(n)，u(n-1)，...，u(n-M+1)] ^T (2)

X(n)＝[x(n)，x(n-1)，...，x(n-M+1)] ^T (3)

Correspondingly, the weight at moment n need reflect the accuracy of M judgement in the past.So the set of the weight that this is possible is:

a _n＝p _np _n-1...p _n-M+1 (4)

The soft weight decisions RLS estimator of time change step length that theorem 2:(revises) the soft weight decisions RLS estimator of the time change step length algorithm of revising is determined to formula (9) by formula (5), wherein a _nDetermine by formula (4).

${\widehat{\mathrm{\β}}}_n={\widehat{\mathrm{\β}}}_{n-1}+{\mathrm{\μ}}_n{a}_n{H}_n^{-1}X\left(n\right)e\left(n\right)---\left(5\right)$

H _n＝λH _n-1+X(n)X ^T(n) (6)

$e\left(n\right)=Y\left(n\right)-X{\left(n\right)}^T{\widehat{\mathrm{\β}}}_{n-1}---\left(7\right)$

Wherein: μ _n=α _n* μ ₀(8)

${\mathrm{\α}}_n=C^{\frac{1}{1+{\mathrm{an}}^b}}---\left(9\right)$

In the present invention, this method adopts time change step length can make algorithm the convergence speed faster, and hypothesis has accurate decision error information in this method, can utilize weight coefficient and the correction of this information as the judgement of RLS method like this.The influence that this coefficient will make noise and mistake in judgment produce reduces.

Prove also through the ofdm communication system link simulation, compare that the present invention has fast convergence rate, estimated accuracy height, characteristics that computation complexity is low with other conventional methods.

The present invention is applicable to the system that all adopt ofdm system to modulate, and is particularly useful for the channel estimation technique in the ofdm system.Though technical scheme of the present invention is primarily aimed at ofdm system, but, any engineer with knowledge background such as signal processing, communications can design corresponding channel estimating apparatus at code division multiple access, time division multiple access according to the present invention, and these all should be included among inventive concept and the scope.Simultaneously, the thought of this patent also can be used to adopt the LMS method with and deriving method estimate other characteristic parameters in fields such as communication, radar, space flight, remote-control romote-sensing, these methods also should be included in thought of the present invention and the scope.

Description of drawings:

Fig. 1 is the art of this patent block diagram.As shown in the figure, the art of this patent comprises 13 modules, wherein initial value be provided with 7, time change step length structure 8, upgrade matrix construction 9, soft decision information structure 10, revise soft decision information 11, control information 12, characteristic parameter estimates that 13 are this patented technology and routine techniques difference.

Emulation major parameter from Fig. 2 to Fig. 4 is: channel model is the Rummler channel and the wireless mobile channel of aforesaid standard, QPSK modulation, forgetting factor λ=0.99 of RLS algorithm, RLS constant step size mu ₀=0.02, RLS time change step length factor a=0.001, b=1.2, c=5.0.

In emulation, adopt two kinds of different channel circumstances.That is: Rummler channel and wireless mobile Rayleigh channel.The Rummler channel is made up of three multipaths, and wherein preceding two time delay is more close, and therefore, it is that two multipaths are formed that this channel model can be regarded as, that is: a direct-view (LOS) footpath and a reflection footpath.And certain attenuation law is satisfied in each tap of wireless mobile Rayleigh channel response, and this decay can respond with a single pole low-pass filter and describe, and can be expressed as:

G(v)＝A(1-(v/f _m) ²) ^-1/2 (28)

Wherein, A is the decay of tap, and v is a translational speed.f _mFor the 3dB frequency, represent with Doppler frequency sometimes.

Fig. 2 is time change step length soft-decision weighting recurrence least square blind (TVCPMSWRLS) method of estimation of blind recurrence least square (BRLS) method of estimation, correction.

For the performance of the art of this patent and prior art relatively, we adopt the absolute difference between real impulse response and its estimated value squared and on average describe.That is:

$\mathrm{Error}=\frac{1}{N_c-N_0-1}\underset{n=N_0}{\overset{N_c}{\mathrm{\Σ}}}{|\mathrm{\β}-{\widehat{\mathrm{\β}}}_n|}^T|\mathrm{\β}-\widehat{\mathrm{\β}}|---\left(29\right)$

Wherein, N ₀Be than a constantly bigger integer of the initial transient response of estimator.

Compare thus as can be known, the art of this patent has improved the convergence rate and the channel estimating performance of channel estimation methods.Though Fig. 1 only is a simulated example, its conclusion is of universal significance.

Fig. 3 is respectively the channel estimating performance of time change step length soft-decision weighting recurrence least square blind (TVCPMSDWRLS) method of estimation correspondence when difference postpones expansion of blind recurrence least square (BRLS) method of estimation, correction.Wherein, signal to noise ratio is fixed as 10dB, and the delay expansion of Rummler channel changes between 1 mark space to 5 mark space in a space increment.More as can be seen, when difference postponed expansion, the art of this patent performance was excellent especially by this figure.

Fig. 4 is a constant step-length RLS algorithm and the art of this patent corresponding channel estimating performance when the different signal to noise ratio.In the figure, adopt the Rummler channel model, postpone expansion and being fixed as a mark space, signal to noise ratio changes between from 0dB to 30dB.

Relatively should scheme as can be seen: when identical signal to noise ratio, this paper proposed projects has improved performance for estimating channel effectively.

Embodiment:

Below by concrete enforcement technical scheme of the present invention is further described.

Concrete steps are:

1, transmitting terminal is sent into the OFDM base band signal modulated, produces protection at interval, and by D/A and formed filter, generation transmits.

2, at receiving terminal, received signal by A/D and low pass filter after, protection is at interval deleted, obtains received signal matrix Y.

Y＝Xh+v (1)

3, setup parameter μ ₀, a, b value, calculate step-length matrix μ _nWherein:

μ _n＝α _n×μ ₀ (2)

${\mathrm{\α}}_n=C^{\frac{1}{1+{\mathrm{an}}^b}}---\left(3\right)$

4, suppose to have accurate decision error information, can utilize the weight coefficient of this information like this as the judgement of RLS method.The influence that this coefficient will make noise and mistake in judgment produce reduces.

Suppose θ _iAnd φ _iArgument in the demodulation process process when being soft-decision and hard decision respectively, definition p _iThe normalized value between [0,1] for difference between reflection soft-decision and the hard decision has:

$p_i=1-\frac{|{\mathrm{\φ}}_i-{\mathrm{\θ}}_i|}{\mathrm{\π}/S}---\left(4\right)$

Wherein, S is alternative number of symbols.

Because channel can be expressed as the tap time-delay of a plurality of time delays, so have:

u(n)＝[u(n)，u(n-1)，...，u(n-M+1)] ^T (5)

X(n)＝[x(n)，x(n-1)，...，x(n-M+1)] ^T (6)

Correspondingly, the weight at moment n need reflect the accuracy of M judgement in the past.So the set of the weight that this is possible is:

a _n＝p _np _n-1…p _n-M+1 (7)

5, the value of setup parameter λ is for the soft weight decisions RLS estimator of the time change step length algorithm of revising, a _nDetermine by formula (7).And upgrade matrix be:

H _n＝λH _n-1+X(n)X ^T(n) (8)

6, setup parameter β ₀Value, calculate error matrix e (n).Wherein:

$e\left(n\right)=Y\left(n\right)-X{\left(n\right)}^T{\widehat{\mathrm{\β}}}_{n-1}---\left(9\right)$

7, by loop iteration, estimate the channel characteristics parameter

${\widehat{\mathrm{\β}}}_n={\widehat{\mathrm{\β}}}_{n-1}+{\mathrm{\μ}}_n{a}_n{H}_n^{-1}X\left(n\right)e\left(n\right)---\left(10\right)$

Claims (4)

1, the present invention relates to the high adaptive blind estimation method for characteristic parameter of a kind of precision, it is characterized in that comprising the steps:

Behind the feature parameter vector D of step 1 input signal vector u (n) by the unknown, suppose that noise is W, its output signal vector is Y (n).

Y＝UD+W (1)

Step 2 setup parameter μ ₀, a, b value, wherein, α _nBe the nonlinear time-varying parameter, the step-length matrix μ that becomes when calculating _n

μ _n＝α _n×μ ₀ (2)

${\mathrm{\α}}_n=C^{\frac{1}{1+{\mathrm{an}}^b}}---\left(3\right)$

Step 3 hypothesis has accurate decision error information, can utilize the weight coefficient of this information as the judgement of RLS method like this.The influence that this coefficient will make noise and mistake in judgment produce reduces.

Suppose θ _iAnd φ _iArgument in the demodulation process process when being soft-decision and hard decision respectively, definition p _iThe normalized value between [0,1] for difference between reflection soft-decision and the hard decision has:

$p_i=1-\frac{|{\mathrm{\φ}}_i-{\mathrm{\θ}}_i|}{\mathrm{\π}/S}---\left(4\right)$

Wherein, S is alternative number of symbols.

Because the unknown characteristics parameter system can be expressed as the tap time-delay of a plurality of time delays, so have:

X(n)＝[x(n)，x(n-1)，...，x(n-M+1)] ^T (5)

Correspondingly, the weight at moment n need reflect the accuracy of M judgement in the past.So the soft decision information set of the weight that this is possible is:

a _n＝p _np _n-1…p _n-M+1 (6)

Step 4 setup parameter λ and H ₀Value, calculate the renewal matrix H _n

H _n＝λH _n-1+X(n)X ^T(n) (7)

Step 5 setup parameter β ₀Value, calculate error matrix e (n).Wherein:

e(n)＝Y(n)-X(n) ^Tβ _n-1 (8)

Step 6 estimates characteristic parameter by loop iteration

${\mathrm{\β}}_n={\mathrm{\β}}_{n-1}+{\mathrm{\μ}}_n{a}_n{H}_n^{-1}X\left(n\right)e\left(n\right)---\left(9\right)$

2, the adaptive blind estimation method for characteristic parameter said as claim 1, that a kind of precision is high is characterized in that: become when step-length is.Its core concept is: allow the filter weight coefficient be a bigger value earlier, after the filter weight coefficient rapidly converged to the best weights coefficient by the time, step change reduced to obtain better estimated performance.

3, the high adaptive blind estimation method for characteristic parameter of a kind of precision as claimed in claim 1, it is characterized in that:, utilize receiver soft decision information function as the soft weight decisions of weight coefficient and the method for revising in order to keep the robustness of hard decision errors and interchannel noise.

4, the high adaptive blind estimation method for characteristic parameter of a kind of precision as claimed in claim 1, it is characterized in that: this patent thought can be used for estimating channel characteristics among OFDM, CDMA, the TDMA, also can be used for all existing RLS methods and the serial of methods of deriving by the RLS method in; This patent relates to fields such as communication, radar, space flight, remote-control romote-sensing, sonar, image processing, computer vision, biomedical engineering.

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