A robust adaptive weighted constant modulus algorithm for blind equalization of wireless communications systems under impulsive noise environment

Abstract A robust adaptive weighted constant modulus algorithm is proposed for blind equalization of wireless communication systems under impulsive noise environment. The influence of the impulsive noise is analyzed based on numerical analysis method. Then an adaptive weighted constant modulus algorithm is constructed to adaptively suppress impulsive noise. Theoretical analysis is provided to illustrate that the proposed algorithm has a robust equalization performance since the impulsive noise is adaptively suppressed. Moreover, the proposed algorithm has stable and quick convergence due to avoidance of large misadjuntment and adoption of large step size. Simulation results are presented to show the robust equalization performance and the fast convergence speed of the proposed algorithm under both impulsive noise and Gaussian noise environments.

[1]  Yanyan Wei,et al.  Blind equalization of short burst signals based on twin support vector regressor and data-reusing method , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[2]  D.L. Jones,et al.  A normalized constant-modulus algorithm , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[3]  Mohammad Ataei,et al.  Joint blind equalization and detection in chaotic communication systems using simulation-based methods , 2015 .

[4]  Zhi Ding,et al.  Global convergence of fractionally spaced Godard (CMA) adaptive equalizers , 1996, IEEE Trans. Signal Process..

[5]  Rong Chen,et al.  Blind turbo equalization in Gaussian and impulsive noise , 2001, IEEE Trans. Veh. Technol..

[6]  S. Manioudakis Regularised analytical constant modulus algorithm for space–time coded systems , 2006 .

[7]  Tao Jiang,et al.  Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation , 2016, Signal Process..

[8]  Masanori Hamamura,et al.  Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation , 2015 .

[9]  Marcelo A. C. Fernandes Linear programming applied to blind signal equalization , 2015 .

[10]  C. L. Nikias,et al.  Signal processing with fractional lower order moments: stable processes and their applications , 1993, Proc. IEEE.

[11]  Prasant Kumar Pattnaik,et al.  Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization , 2014, Expert Syst. Appl..

[12]  Yassine Himeur,et al.  An adaptive recursive noise compensator for impulsive noise mitigation over OFDM power line communication , 2016 .

[13]  Namyong Kim,et al.  Blind signal processing for impulsive noise channels , 2012, Journal of Communications and Networks.

[14]  Pierre Comon,et al.  Optimal Step-Size Constant Modulus Algorithm , 2008, IEEE Transactions on Communications.

[15]  D. Middleton,et al.  Man-Made Noise in Urban Environments and Transportation Systems: Models and Measurements , 1973, IEEE Trans. Commun..

[16]  Jinming Li,et al.  A robust constant modulus algorithm in alpha-stable noise environments , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[17]  Mohan Avinash,et al.  Low complexity adaptation for SISO channel shortening equalizers , 2012 .

[18]  Yanyan Wang,et al.  Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation , 2016 .

[19]  B. Lin,et al.  Adaptive blind equalization in impulsive noise environments , 2010, 2010 3rd International Congress on Image and Signal Processing.

[20]  Diego B. Haddad,et al.  Affine Projection Subband Adaptive Filter With Low Computational Complexity , 2016, IEEE Transactions on Circuits and Systems II: Express Briefs.

[21]  Esam Abdel-Raheem,et al.  Computationally efficient methods for blind decision feedback equalization of QAM signals , 2008 .

[22]  Ivan V. Bajic,et al.  Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering , 2015, IEEE Signal Processing Letters.

[23]  D. Godard,et al.  Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems , 1980, IEEE Trans. Commun..

[24]  Chrysostomos L. Nikias,et al.  Maximum likelihood localization of sources in noise modeled as a stable process , 1995, IEEE Trans. Signal Process..

[25]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[26]  G. R. Wilson,et al.  Nonlinear and non-Gaussian ocean noise , 1986 .

[27]  Lee M. Garth,et al.  A dynamic convergence analysis of blind equalization algorithms , 2001, IEEE Trans. Commun..