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Orthogonal wavelet transformation super-exponential iteration (SEI) blind equalization algorithm based on ant colony optimization

An orthogonal wavelet, super-exponential technology, applied in baseband system components, shaping networks in transmitters/receivers, electrical components, etc. implementation issues

Inactive Publication Date: 2010-12-01
NANJING UNIV OF INFORMATION SCI & TECH
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AI Technical Summary

Problems solved by technology

Compared with the CMA algorithm, the SEI algorithm introduces a whitening matrix Q, which whitens the input signal of the equalizer and speeds up the convergence speed, but it still cannot meet the requirements of engineering realizability
Literature (Guo Yecai, Ding Xuejie, Guo Fudong, etc. Cascade Adaptive Blind Equalization Algorithm Based on Normalized Constant Modulus Algorithm [J]. Journal of System Simulation, 2008, 20(17): 4647-4650; Han Yingge, Guo Yecai , Li Baokun et al. Blind equalization algorithm for orthogonal wavelet transform with momentum term [J]. Journal of System Simulation; 2008, 20(6): 1559-1562; Cooklev T An Efficient Architecture for Orthogonal Wavelet Transforms[J]. (S1070-9980), 2006, 13(2): 77~79.) showed that: performing wavelet transform on the input signal of the equalizer and performing energy normalization processing can reduce the correlation between signal and noise, thus effectively Accelerate the convergence speed, but the wavelet blind equalization algorithm still finds the optimal weight vector according to the gradient direction, it is sensitive to the initialization of the weight vector, improper initialization will make the algorithm converge to the local minimum, or even diverge

Method used

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  • Orthogonal wavelet transformation super-exponential iteration (SEI) blind equalization algorithm based on ant colony optimization
  • Orthogonal wavelet transformation super-exponential iteration (SEI) blind equalization algorithm based on ant colony optimization
  • Orthogonal wavelet transformation super-exponential iteration (SEI) blind equalization algorithm based on ant colony optimization

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Experimental program
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Embodiment 1

[0057] Embodiment 1 Two-path underwater acoustic channel simulation

[0058] The unit impulse response of the two-path underwater acoustic channel is c=[-0.35, 0, 0, 1], the transmitted signal is 8PSK, the weight length of the equalizer is 16, and the signal-to-noise ratio is 25dB. In the SEI algorithm, the fourth tap Coefficients are set to 1, the rest are 0, and the step size μ SEI =0.00015,μ m =0.02; in the WT-SEI algorithm, the 11th tap coefficient is set to 1, and the rest are 0, the step size μ WT-SEI =0.01,μ m = 0.002; ACA-WT-SEI step size μ ACA-WT-SEI =0.004,μ m = 0.004. The input signal of each channel is decomposed by DB4 orthogonal wavelet, the decomposition level is 2 layers, the initial power value is set to 4, and the forgetting factor β=0.9999; 500 times of Monte Carlo simulation results, such as image 3 shown.

[0059] image 3 (a) shows that ACA-WT-SEI is about 8000 steps faster than SEI and about 4000 steps faster than WT-SEI in terms of convergence ...

Embodiment 2

[0060] Embodiment 2 mixed phase channel

[0061] The mixed phase channel is c=[0.3132 -0.1040 0.8908 0.3134], the transmitted signal is 16QAM, the weight length of the equalizer is 16, and the signal-to-noise ratio is 25dB. In the SEI algorithm, the third tap coefficient is set to 1, and the rest are set to 0. long μ SEI =0.00005,μ m =0.02; in the WT-SEI algorithm, the fourth tap coefficient is set to 1, and the rest are 0, the step size μ WT-SEI =0.0005,μ m = 0.02; ACA-WT-SEI step size μ ACA-WT-SEI =0.0006,μ m = 0.0006. The input signal of each channel is decomposed by DB4 orthogonal wavelet, the decomposition level is 2 layers, the initial power value is set to 4, and the forgetting factor β=0.9999; 500 times of Monte Carlo simulation results, such as Figure 4 shown.

[0062] Figure 4 (a) shows that ACA-WT-SEI is about 4000 steps faster than SEI and WT-SEI in terms of convergence speed. In the steady-state error, compared with SEI, ACA-WT-SEI has reduced by nearly...

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Abstract

The invention discloses an orthogonal wavelet transformation super-exponential iteration (SEI) blind equalization method based on ant colony optimization. The method utilizes ant colony optimization to search the optimal weight vector as an initial weight vector value inputted by an equalizer so as to avoid local convergence of the algorithm. The method utilizes a positive feedback mechanism to increase convergence rate, utilizes the whitening action of the super-exponential iteration (SEI) method on data, utilizes orthogonal wavelet transformation to perform decorrelation on signals and fully utilizes the global convergence of ant colony optimization. Underwater acoustic channel simulation results show that compared with the orthogonal wavelet transformation super-exponential iteration constant modulus algorithm (WT-SEI-CMA), the method has higher convergence rate and smaller state error, and an equalized eye pattern is clearer and compacter. Thus, the method has certain practical valve.

Description

technical field [0001] The invention relates to an orthogonal wavelet transform super-exponential iterative blind equalization method, in particular to an orthogonal wavelet transform super-exponential iterative blind equalization method based on ant colony optimization. Background technique [0002] In the underwater communication system, the inter-symbol interference (Inter-Symbol Inter-ferences, ISI) brought by the multipath effect and the limited bandwidth is the main factor affecting the communication quality, which needs to be eliminated by equalization technology (see literature: Guo Yecai , Chao Yang. Design and simulation of super-exponential iterative blind equalizer based on orthogonal wavelet transform [J]. Journal of System Simulation, 2009, 21(20)-: 6556-6559). The blind equalization algorithm that does not require training sequences only uses the statistical characteristics of the received signal itself to equalize the signal, but its convergence speed is slow...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/03H04B13/02
Inventor 郭业才陈佩佩
Owner NANJING UNIV OF INFORMATION SCI & TECH
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