Robust sparse channel estimation and equalization in impulsive noise using linear programming

Authors:
Xue Jiang;T. Kirubarajan;Wen-Jun Zeng
Affiliations:
Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada L8S 4L8;Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada L8S 4L8;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong
Venue:
Signal Processing
Year:
2013

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Abstract

In this paper, an algorithm for sparse channel estimation, called @?"1-regularized least-absolutes (@?"1-LA), and an algorithm for equalization, called linear least-absolutes (LLA), in non-Gaussian impulsive noise are proposed. The proposed approaches are based on the minimization of the absolute error function, rather than the squared error function. By replacing the standard modulus with the @?"1-modulus of complex numbers, the resulting optimization problem can be efficiently solved through linear programming. The selection of an appropriate regularization parameter is also addressed. Numerical results demonstrate that the proposed algorithms, compared with the classical methods, are more robust to impulsive noise and have a superior accuracy.