CN101252559A - Method of training sequence time change step length least mean square - Google Patents

Abstract

The invention provides a training sequence variable step-size least-mean-square algorithm, in particular to a training sequence quick channel estimation method. Employing factors which are capable of adaptively tracking and controlling the changes of characteristic parameters through error estimation, the quick channel estimation method improves the convergence speed of the algorithm and acquires the weight coefficient of an optimum filter speedily. The invention discloses a training sequence least-mean-square characteristic parameter estimation method which is quicker in convergence speed and more precise than prior art. The method is easy to realize and is applicable in communication systems for modulated orthogonal frequency division multiplexing for channel estimation. Meanwhile, the idea of the invention can be applied to CDMA channel estimation devices and TDMA channel estimation devices, and can also be applied to LMS methods and the derivative methods thereof. The channel estimation method relates to the fields of communication, oil and seismic exploration, sonar, image processing, computer vision, biomedical engineering, vibration engineering, radar, remote control and telemetry, as well as aerospace field.

Description

A kind of method of training sequence time change step length least mean square

Technical field:

The present invention relates to a kind of method of training sequence time change step length least mean square.Relate in particular to the channel estimation technique in the employing OFDM modulated digital communication systems such as relating to digital terrestrial television, single carrier ofdm communication system, multi-carrier OFDM communication system, wireless lan (wlan).

Simultaneously, a kind of method of training sequence time change step length least mean square involved in the present invention, this thought can be used for code division multiple access (CDMA) and time division multiple access (TDMA) system carries out channel estimating, can also be used for field and other characteristic parameters of technology estimation such as oil seismic exploration, radar, space flight, sonar, biomedical engineering, image processing.

Background technology:

Lowest mean square (LMS) algorithm and recurrence least square (RLS) algorithm are to estimate and Predicting Technique for self adaptation as you know, have a wide range of applications at different aspect.Yet though the LMS algorithm calculates simply, convergence rate is fast inadequately, and estimated performance is good inadequately.Especially when characteristic parameter constantly changes, the variation that requires method of estimation to conform.

Proposed a kind ofly frequency deviation and channel to be carried out estimation approach when having frequency shift (FS) in patented technology 99102439.7 " channel tracking in the mobile receiver ", still, the channel estimating convergence rate is still fast inadequately in this method, and estimated performance is accurate inadequately.

Yet there is very big contradiction in the L M S algorithm of traditional fixed step size μ in the follow-up control of convergence rate, time-varying system and the requirement between the stable state imbalance.Little step size mu has little imbalance when guaranteeing stable state, but convergence of algorithm speed is slow, and poor to the follow-up control of unstable state system.On the other hand, big step size mu makes algorithm have the follow-up control that convergence rate is faster become reconciled, but this is to be cost with big imbalance.For solving this contradiction, multiple follow-on L M S algorithm is suggested, and concludes and gets up to mainly contain two kinds.

1. variable step L M S algorithm variable step L M S algorithm is based on such criterion: when weight coefficient during away from the best weights coefficient, step-length is bigger, to accelerate convergence rate and to the tracking velocity of time-varying system; When weight coefficient near the best weights coefficient, step-length is smaller, to obtain less stable state mistuning noise.

2. transform domain L M S algorithm is when input signal itself has very strong correlation, and time domain L M S algorithm will convergence rate slow down.Can pass through certain orthogonal transform, remove the correlation between the input signal earlier, carry out adaptive-filtering again, this algorithm just is called transform domain L M S algorithm.

How the difference of transform domain L M S algorithm and time domain L M S algorithm mainly has been the process of an orthogonal transform.

So,, have necessity of improving channel estimation methods for orthogonal FDM communication system for to satisfy the needs that obtain channel estimation value under the multidiameter fading channel in the short period of time.

Summary of the invention:

The objective of the invention is: propose a kind of method of training sequence time change step length least mean square, this method not only can be carried out the Fast Channel estimation in OFDM (OFDM) communication system, and can be used for the variation of other field estimating characteristic parameters.This method will improve convergence rate, strengthen adaptive ability, strengthen estimated accuracy and realization easily with respect to the training sequence LMS method of estimation of prior art.

To achieve these goals, the present invention proposes a kind of method of training sequence time change step length least mean square, this method is called the TVCPTLMS method.Its technical scheme is: utilize training sequence to carry out channel estimating, in estimation, adjust in the step-length by the control information self adaptation, allow the filter weight coefficient be a bigger value earlier, by the time after the filter weight coefficient rapidly converged to the best weights coefficient, step change reduced to obtain better estimated performance.

The present invention proposes a kind of method of training sequence time change step length least mean square, not only can be used for Fast Channel and estimate, and can estimate channel state parameter effectively and be used for demodulation, thereby effectively improve systematic function.

Be that example describes to carry out the channel estimating parameter in the ofdm system below.

Estimation model of the present invention and principle are as follows:

Theorem 1:(training sequence time change step length least mean square estimator) the training sequence time change step length least mean square algorithm for estimating is determined to formula (3) by formula (1):

${\widehat{\mathrm{\β}}}_n={\widehat{\mathrm{\β}}}_{n-1}+{\mathrm{\μ}}_{n}u\left(n\right)e\left(n\right)---\left(1\right)$

$e\left(n\right)=Y\left(n\right)-u{\left(n\right)}^T{\widehat{\mathrm{\β}}}_{n-1}---\left(2\right)$

μ(n)＝μ _max(1-e ^{-α‖e(n)x(n)‖}) (3)

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 training sequence least fibre 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 the above-mentioned discussion about 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 be used to estimate other characteristic parameters in fields such as communication, oil seismic exploration, space flight, radar, observing and controlling.

Description of drawings:

Fig. 1 is the art of this patent block diagram. As shown in the figure, the art of this patent comprises 9 modules, wherein initial value arrange 5, the time become the step Long structure 6, renewal matrix construction 7, control information 8, characteristic parameter estimate that 9 is this patented technology and routine techniques difference.

Emulation major parameter from Fig. 2 to Fig. 4 is: channel model is Rummler channel and the wireless mobile letter of aforesaid standard The road, the QPSK modulation. 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 front two time delay is more close, and therefore, this channel model can be seen Become two multipaths and form, that is: a direct-view (LOS) footpath and a reflection footpath. And the response of wireless mobile Rayleigh channel is every Certain attenuation law is satisfied in individual tap, and this decay can respond to describe with a single pole low-pass filter, can be expressed as:

G(v)＝A(1-(v/f _m) ²) ^-1/2    (4)

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

Fig. 2 is prior art TLMS channel estimation methods and the art of this patent performance comparison diagram. All minimum comprising training sequence Side's (TLMS) method of estimation, training sequence time change step length least mean square (TVCPTLMS) method of estimation.

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

$\mathrm{Error}=\frac{1}{N_c-N_0-1}\underset{n=N_0}{\overset{N_c}{\mathrm{\Σ}}}{|\mathrm{\β}-{\widehat{\mathrm{\β}}}_n|}^T|\mathrm{\β}-\widehat{\mathrm{\β}}|---\left(5\right)$

Wherein, N₀Than a constantly bigger integer of the initial transient response of estimator.

As shown in Figure 2, the art of this patent has improved convergence rate and the estimated performance of method of estimation. Although Fig. 2 only is an emulation Example, but its conclusion is of universal significance.

Fig. 3 is that TLMS and the art of this patent estimate that channel compares, and impulse response evaluated error duplicate ratio reflects Constant The training sequence LMS algorithm of long, time change step length corresponding channel estimating performance when difference postpones expansion. Wherein, signal to noise ratio is solid Be decided to be 10dB, the delay expansion of Rummler channel becomes between 1 mark space to 5 mark space in a space increment Change. When difference postpones expansion, the art of this patent when estimating channel response than corresponding prior art Constant long training sequence The LMS algorithm performance is more excellent.

Fig. 4 is constant step-length, time change step length training sequence LMS algorithm corresponding channel estimating performance when different signal to noise ratio. Among this figure, channel circumstance is wireless mobile channel. For time varying channel, when different signal to noise ratio, time change step length training sequence LMS Algorithm specific ray constant step-length LMS training sequence algorithm performance when estimating channel response is more excellent.

Embodiment:

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

Concrete steps are:

1, transmitting terminal is sent into OFDM base band signal modulated and training sequence u (n), 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.Wherein, v is a noise.

Y＝Uh+r

3, select training sequence u (n), setup parameter β ₀Value, calculate error matrix e (n).Wherein:

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

4, setup parameter α, μ _MaxValue, calculate step-length matrix μ (n).Have:

μ(n)＝μ _max(1-e ^{-α‖e(n)x(n)‖})

5, by loop iteration, estimate the channel characteristics parameter

${\widehat{\mathrm{\β}}}_n={\widehat{\mathrm{\β}}}_{n-1}+{\mathrm{\μ}}_{n}u\left(n\right)e\left(n\right)$

Claims (4)

1, the present invention relates to a kind of method of training sequence time change step length least mean square, it is characterized in that comprising the steps:

Step 1: for input signal vector and selected training sequence u (n), behind the unknown h of characteristic parameter system, its output signal vector is y (n).The supposing the system noise is v (n), then has:

y＝Uh+v(1)

Step 2: setup parameter β ₀Value, calculate error matrix e (n).Wherein:

e(n)＝Y(n)-u(n) ^Tβ _n-1(2)

Step 3: setup parameter α, μ _MaxValue, calculate step-length matrix μ (n).Have:

μ(n)＝μ _max(1-e ^{-α‖e(n)x(n)‖}) (3)

Step 4:, estimate characteristic parameter β (n) by loop iteration.

β(n)＝β(n-1)+μ(n)u(n)e(n) (4)

2, said as claim 1, a kind of method of training sequence time change step length least mean square is characterized in that: step-length is relevant with error matrix, time becomes.Its core concept is: at first allow the filter weight coefficient be a bigger value, again by error matrix regulation and control time change step length, after the filter weight coefficient rapidly converged to the best weights coefficient by the time, step change reduced to obtain better estimated performance.

3, a kind of method of training sequence time change step length least mean square as claimed in claim 1, it is characterized in that: this method not only can be used for the channel estimating in the communication systems such as OFDM, CDMA, TDMA, and can be used for other all LMS methods with and deriving method (as: training sequence LMS method, blind LMS method etc.).

4, a kind of method of training sequence time change step length least mean square as claimed in claim 1 is characterized in that: the thought of this patent can be used for fields such as communication, radar, space flight, observing and controlling, image processing, biomedical engineering, oil seismic exploration, sonar and estimate the various features parameter.The technology of every this thought of use and method all should be included within inventive concept and the scope.

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