Robust channel estimation is required for coherent demodulation in multipath fading\nwireless communication systems which are often deteriorated by non-Gaussian noises. Our research\nis motivated by the fact that classical sparse least mean square error (LMS) algorithms are very\nsensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent\nsparsity information of wireless channels. This paper proposes a sign function based sparse adaptive\nfiltering algorithm for developing robust channel estimation techniques. Specifically, sign function\nbased least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described\nby a symmetric �±-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms\nare proposed by introducing several effective sparse-promoting functions into the standard SLMS\nalgorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they\noutperform the standard SLMS algorithm for robust sparse channel estimation, which can be also\nverified by simulation results.
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