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Robust sparse channel estimation and equalization in impulsive noise using linear programming
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DOI：10.1016/j.sigpro.2012.11.030.png)
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Abstract
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In this paper, an algorithm for sparse channel estimation, called l(1)-regularized least-absolutes (l(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 l(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. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Sparse channel estimation
l(1)-Regularization
Linear programming
Least absolute deviation
Equalization
Impulsive noise
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