Normalized p-Norm Blind Equalization Algorithm with Adaptive Momentum under Impulsive Noise Environment

2015 ◽  
Vol 713-715 ◽  
pp. 1409-1412 ◽  
Author(s):  
Rui Ruan ◽  
Ying Xiao
Keyword(s):  
Convergence Rate ◽  
Impulsive Noise ◽  
Blind Equalization ◽  
Noise Environment ◽  
Equalization Algorithm ◽  
Convergence Performance ◽  
Simulation Results ◽  
Adaptive Momentum

Hereby a normalized p-norm blind equalization algorithm with adaptive momentum was proposed. Normalized p-norm LMS-CMA can obtain robust convergence performance under impulsive noise environment, however, the convergence rate is slow. To further improve the performance of the normalized p-norm LMS-CMA, adaptive momentum according to the instantaneous error is designed. If the instantaneous error based on CMA criterion and DD criterion has the same sign, the momentum factor remains unchanged. Otherwise, the momentum factor is set to 0. The simulation results show that, the proposed algorithm has faster convergence rate than the normalized p-norm blind equalization algorithm, furthermore, it has robust convergence performance under impulsive noise environment.

Double Error Function Decision Feedback Momentum Blind Equalization Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 2097-2101
Author(s):  
Li Kun Xing ◽  
Long Wu ◽  
Ye Cai Guo
Keyword(s):  
Wavelet Transform ◽  
Error Function ◽  
Blind Equalization ◽  
Decision Feedback ◽  
Improve Performance ◽  
Orthogonal Wavelet ◽  
Equalization Algorithm ◽  
Convergence Performance ◽  
Simulation Results ◽  
Orthogonal Wavelet Transform

Against the shortcomings of slow convergence and large residual error in norm decision feedback blind equalization, double error function decision feedback blind equalization algorithm based on orthogonal wavelet transform momentum (WT-DMCMA-DFE)was proposed. In the algorithm, the four combinations of two error functions, respectively, to make adjustments on the former right and the feedback right, and add momentum algorithm to the former right and the feedback right to accelerate the convergence rate, escape correlation by using the orthogonal wavelet transform and normalize the energy to further improve performance of the convergence. Underwater acoustic channel simulation results show that convergence performance and mean square error of WT-MCMA-DFE, WT-H-HMCMA-DFE, WT-H-CMCMA-DFE is different.

scholarly journals Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 196
Author(s):  
Jun Lu ◽  
Qunfei Zhang ◽  
Wentao Shi ◽  
Lingling Zhang ◽  
Juan Shi
Keyword(s):  
Adaptive Filtering ◽  
Interference Cancellation ◽  
Impulsive Noise ◽  
Weight Vector ◽  
Filtering Algorithm ◽  
Convergence Performance ◽  
Simulation Results ◽  
Robust Adaptive ◽  
Arctangent Cost Function ◽  
Simultaneous Transmission

Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

Orthogonal Wavelet Transform Blind Equalization Algorithm Based on the Optimization of Artificial Fish Swarm Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1399-1402
Author(s):  
Wei Huang ◽  
Ye Cai Guo
Keyword(s):  
Wavelet Transform ◽  
Convergence Rate ◽  
Blind Equalization ◽  
Weight Vector ◽  
Orthogonal Wavelet ◽  
Artificial Fish Swarm Algorithm ◽  
Equalization Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Orthogonal Wavelet Transform

According to disadvantages of big steady-state error, low convergence rate, and local convergence of traditional Constant Modulus blind equalization Algorithm (CMA), an orthogonal Wavelet Transform blind equalization Algorithm based on the optimization of Artificial Fish Swarm Algorithm(AFSA-WT-CMA) is proposed. In this proposed algorithm, the weight vector of the blind equalizer is regarded as artificial fish, the equalizer weight vector can be optimized via making full use of global search and information sharing mechanism of artificial fish school algorithm, the de-correlation ability of normalizing orthogonal wavelet transform. The computer simulations in underwater acoustic channels indicate that the proposed algorithm outperforms CMA and WT-CMA in convergence rate and mean square error.

scholarly journals Neural Network Blind Equalization Algorithm Applied in Medical CT Image Restoration

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yunshan Sun ◽  
Liyi Zhang ◽  
Jin Zhang ◽  
Lijuan Shi
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Blind Equalization ◽  
Ct Image ◽  
Original Image ◽  
Equalization Algorithm ◽  
Convergence Performance ◽  
And Performance ◽  
Blind Image ◽  
Method Analysis

A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived and used in conjunction with Zigzag coding to restore the original image. As a result, the effect of PSF can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (ISI). In order to obtain the estimation of the original image, what is proposed in this method is to optimize constant modulus blind equalization cost function applied to grayscale CT image by using conjugate gradient method. Analysis of convergence performance of the algorithm verifies the feasibility of this method theoretically; meanwhile, simulation results and performance evaluations of recent image quality metrics are provided to assess the effectiveness of the proposed method.

Super Exponential Iteration Adaptive Minimum Entropy Blind Equalization Algorithm Based on Quantum Artificial Fish Swarm Optimization

2013 ◽  
Vol 779-780 ◽  
pp. 1793-1796
Author(s):  
Ye Cai Guo ◽  
Wei Huan ◽  
Li Fu Wu ◽  
Ke Cheng Leng
Keyword(s):  
Convergence Rate ◽  
Blind Equalization ◽  
Iteration Algorithm ◽  
Minimum Entropy ◽  
Swarm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Equalization Algorithm ◽  
Artificial Fish Swarm ◽  
Speed Up ◽  
Accelerate Convergence

In order to improve equalization performance of higher-order non-constant modulus signals, adaptive minimum entropy super-exponential iteration blind equalization algorithm based on quantum artificial fish swarm optimization was proposed. The proposed algorithm can accelerate convergence rate via super-exponential iteration algorithm and decease mean square error (MSE) further via quantum artificial fish swarm algorithm. The simulation results demonstrate that the proposed algorithm has different equalization performance to the different modulation systems and can speed up convergence rate and decrease state MSE.

CMA Blind Equalization with Variable Momentum Based on Nonlinear Transformation Function

2014 ◽  
Vol 602-605 ◽  
pp. 2658-2661 ◽  
Author(s):  
Ying Xiao ◽  
Rui Ruan
Keyword(s):  
Convergence Rate ◽  
Blind Equalization ◽  
Transformation Function ◽  
Nonlinear Transformation ◽  
Output Error ◽  
Simulation Results

For the contradiction between convergence rate and convergence precision in the CMA blind equalization with the fixed momentum factor, a variable momentum CMA blind equalization is proposed The output error power of the blind equalizer is acted as the parameter, which control the adjustment of the momentum factor adaptively based on nonlinear transformation function. The algorithm can obtain faster convergence rate and higher convergence precision, also the performance of the blind equalization is improved. The simulation results show that, compared with the CMA blind equalization with the fixed momentum factor, CMA blind equalization with variable momentum based on nonlinear transform can obtain better performance

An Optimized Combination of Frequency Domain Adaptive Equalizers

2014 ◽  
Vol 548-549 ◽  
pp. 766-770
Author(s):  
Ke Cheng Leng ◽  
Cheng Bie ◽  
Xi Gong ◽  
Ran Xu ◽  
Ye Cai Guo
Keyword(s):  
Frequency Domain ◽  
Mean Square Error ◽  
Time Domain ◽  
Blind Equalization ◽  
Mean Square ◽  
Constant Modulus ◽  
Equalization Algorithm ◽  
Adaptive Equalizers ◽  
Simulation Results ◽  
The Time Domain

In order to overcome the defects of the high computational loads and selecting the threshold of mean square error (MSE) for time domain decision-directed constant modulus blind equalization algorithm (DD+CMA), a frequency domain parallel decision multi-modulus blind equalization algorithm based on frequency domain MMA(FMMA) and frequency domain LMS (FLMS) algorithm is proposed. The proposed algorithm is composed of the FMMA and FLMS, and the FMMA and FLMS run automatically in soft switching parallel manner. In running process, it is not necessary to selecting the threshold of the MSE. Moreover, the computational loads can be reduced by circular convolution in the frequency domain signals instead of linear one of the time domain signals. Simulation results show that performance of the proposed algorithm outperforms the FLMS and the FMMA algorithm.

Pseudo Two-Hop Distributed Consensus Algorithm under Directed Topologies

2012 ◽  
Vol 591-593 ◽  
pp. 1599-1602 ◽  
Author(s):  
Huan Xin Peng ◽  
Wen Kai Wang
Keyword(s):  
Convergence Rate ◽  
Spectral Radius ◽  
Consensus Algorithm ◽  
Distributed Consensus ◽  
Convergence Conditions ◽  
Communication Topology ◽  
The Matrix ◽  
Convergence Performance ◽  
Simulation Results ◽  
Directed Topologies

In this paper, we propose the pseudo two-hop distributed consensus algorithm under directed communication topologies. The convergence performance of the pseudo two-hop distributed consensus algorithm under directed communication topologies are analyzed, and the convergence conditions for the pseudo two-hop distributed consensus algorithm are given. In particular, the convergence rate is determined by the spectral radius of the matrix depend on the communication topology. Finally, simulation results are provided to verify these analytical results.

New Concurrent Fractionally-Spaced Blind Equalization

2010 ◽  
Vol 108-111 ◽  
pp. 363-368 ◽  
Author(s):  
Wei Rao ◽  
Ye Cai Guo ◽  
Min Chen ◽  
Wen Qun Tan ◽  
Jian Bing Liu ◽  
...  
Keyword(s):  
Steady State ◽  
Convergence Rate ◽  
Mean Square Error ◽  
Blind Equalization ◽  
Mean Square ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Underwater Acoustic ◽  
Underwater Acoustic Channels ◽  
Simulation Results

The paper proposes a concurrent constant modulus algorithm (CMA) and decision-directed (DD) scheme for fractionally-spaced blind equalization. The proposed algorithm makes full use of the advantages of CMA and DD algorithm. A novel rule to control the adjustment of DD’s tap weights vector is proposed which avoids the hard switch between CMA and DD in practice. Simulations with underwater acoustic channels are used to compare the proposed algorithm with the famous CMA. And the simulation results show that the proposed algorithm has faster convergence rate and lower steady state mean square error.

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