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Wavelet weighted multi-modulus blind equalization algorithm based on fractional lower order statistics (WT-FLOSWMMA)

A multi-mode blind equalization, fractional low-order technology, used in baseband system components, multi-carrier systems, shaping networks in transmitters/receivers, etc.

Inactive Publication Date: 2012-02-15
NANJING UNIV OF INFORMATION SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

But in this method, the environmental noise is assumed to be Gaussian noise, which does not match the reality

Method used

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  • Wavelet weighted multi-modulus blind equalization algorithm based on fractional lower order statistics (WT-FLOSWMMA)
  • Wavelet weighted multi-modulus blind equalization algorithm based on fractional lower order statistics (WT-FLOSWMMA)
  • Wavelet weighted multi-modulus blind equalization algorithm based on fractional lower order statistics (WT-FLOSWMMA)

Examples

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Embodiment

[0102] [Example 1] The underwater acoustic channel c=[0.3132, -0.1040, 0.8908, 0.3134], and the transmission sequence is 64QAM. The signal-to-noise ratio is 25dB α-stable noise, its characteristic index α=1.7, β″=b=0, γ is determined by the signal-to-noise ratio SNR, γ=σ 2 / 10 SNR / 10 (σ 2 is the variance of the input sequence). In FLOSCMA, the step size factor μ 1 = 0.00008; in FLOSWMMA, the step factor μ 2 =0.00008; in the WT-FLOSWMMA of the present invention, the step size factor μ 3 =0.005, the length of the equalizer is 16, the fifth tap coefficient is initialized to 1, and the rest are all 0, the weighting factors are all λ=1.7, using db2 wavelet, second-order decomposition, the power is initialized to 10, smoothing factor β'= 0.99. The simulation results of Monte Carlo 3000 times, such as figure 2 shown.

[0103] from figure 2 (d) It can be seen that in the alpha stable noise environment, the convergence rate of WT-FLOSWMMA of the present invention is about 10...

Embodiment 2

[0105] [Example 2] Channel c=[0.9656, -0.0906, 0.0578, 0.2368], and the transmission sequence is 256QAM. α-stable noise with a signal-to-noise ratio of 30dB, in FLOSCMA, the step factor μ 1 = 0.00001; in FLOSWMMA, the step factor μ 2 =0.00002, weighting factor λ 1 =1.7; In the WT-FLOSWMMA of the present invention, the step size factor μ 3 =0.009, weighting factor λ 2 =1.8, the equalizer length is 16, the 8th tap coefficient is initialized to 1, all the other are 0, other parameters are all the same as in embodiment 1, the simulation result of Monte Carlo 4000 times, such as image 3 shown.

[0106] from image 3 (d) It can be seen that the steady-state error of WT-FLOSWMMA of the present invention is about 1dB smaller than FLOSWMMA, and 7dB smaller than FLOSCMA; the convergence speed of WT-FLOSWMMA of the present invention is about 2000 steps faster than FLOSWMMA, and about 6000 steps faster than FLOSCMA. And the output signal constellation diagram of the WT-FLOSWMMA of ...

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Abstract

The invention discloses a wavelet weighted multi-modulus blind equalization algorithm based on fractional lower order statistics (WT-FLOSWMMA), which comprises the following steps of: obtaining a channel output vector x(n) from a transmitted signal a(n) through a pulse response channel c(n); obtaining an input signal y(n) of an orthogonal wavelet transformer (WT) by using [alpha] stable distribution channel noise w(n) and the channel output vector x(n); and processing y(n) by the orthogonal WT to obtain the input R(n) of an equalizer f(n), wherein the output of the equalizer f(n) is z(n), and meantime the WT-FLOSWMMA error and the iterative formula of a weight vector are as shown in the specification. In the invention, the [alpha] stable noise is suppressed by use of the fractional lower order statistics, the prior information of the signal source is sufficiently used, and the modulus is corrected adaptively in the iteration process; and moreover, orthogonal wavelet transformation is performed on the input signal of the equalizer, self correlation of the input signal is recued, and the equalizing performance is improved.

Description

technical field [0001] The invention relates to a wavelet weighted multi-mode blind equalization method based on fractional low-order statistics. Background technique [0002] In the traditional blind equalization system, the environmental noise is mainly assumed to obey the Gaussian distribution, but the noise encountered in some practical applications has significant spike characteristics, and this type of non-Gaussian noise has a long tail, such as underwater sound Signals, low-frequency atmospheric noise, many biomedical signals, and many man-made noises, etc., usually use the α-stable distribution model (see: literature [1] Changning Li; Gang Yu. A New Statistical Model for Rolling Element Bearing Fault Signals Based on Alpha-Stable Distribution[C].Computer Modeling and Simulation, 2010.ICCMS'10.Second International Conference on, IEEE.2010, Vol.4:386-390; Literature [2] Jia Xu; Wei Han; Xiu-feng He; Ren- xi Chen. Small Target Detection in SAR Image Using the Alpha-sta...

Claims

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Application Information

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IPC IPC(8): H04L25/03H04L25/02H04L27/34
Inventor 郭业才许芳郭军
Owner NANJING UNIV OF INFORMATION SCI & TECH
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