Stable sparse channel estimation algorithm under non-Gaussian noise environments

Broadband frequency-selective fading channels usually exhibit the inherent sparse structure distribution in spread time-domain. By exploiting the sparsity, adaptive sparse channel estimation (ASCE) algorithms, e.g., least mean square with reweighted L1-norm constraint (LMS-RL1) algorithm, can bring a considerable performance gain under the assumption of additive white Gaussian noise (AWGN). In the scenarios of real wireless communication systems, however, channel estimation performance is often deteriorated by the unexpected non-Gaussian mixture noises which usually include AWGN and impulsive noises. To design stable communication systems, we propose sign LMS-RL1 (SLMS-RL1) channel estimation algorithm to remove the non-Gaussian noises and to exploit channel sparsity simultaneously. In addition, the regularization parameter (REPA) selection for SLMS-RL1 algorithm is proposed via Monte Carlo method. Simulation results are provided to corroborate our studies.