2016
DOI: 10.3390/a9030054
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Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments

Abstract: Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing ro… Show more

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Cited by 4 publications
(2 citation statements)
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“…However, the presence of the impulsive nature of noise in some communication channels gives rise to non-Gaussian characteristics. In general, impulsive noise occurs randomly in the form of a sharp increase in the magnitude [2].…”
Section: Introductionmentioning
confidence: 99%
“…However, the presence of the impulsive nature of noise in some communication channels gives rise to non-Gaussian characteristics. In general, impulsive noise occurs randomly in the form of a sharp increase in the magnitude [2].…”
Section: Introductionmentioning
confidence: 99%
“…Then Chen Ye introduced a higher term of error with better noise immunity, and proposed the L0‐norm least mean fourth algorithms [14]. For different application conditions, a variety of deformations are derived, such as the L0‐norm signed least mean square (LMS) algorithm for non‐Gaussian noise [15], L0‐norm normalised LMS algorithm for adapting to the input data power [16], L0‐norm exponentially forgetting window LMS algorithm for improving reconstruction accuracy [13] and L0‐norm zero attraction projection (L0‐ZAP) algorithm for speeding up the reconstruction [13].…”
Section: Introductionmentioning
confidence: 99%