Abstract
Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., zero-attracting LMS (ZA-LMS), reweighted zero-attracting LMS (RZA-LMS) and L p - norm sparse LMS (LP-LMS), have also been proposed. To take full advantage of channel sparsity, in this paper, we propose several improved adaptive sparse channel estimation methods using L p -norm normalized LMS (LP-NLMS) and L 0 -norm normalized LMS (L0-NLMS). Comparing with previous methods, effectiveness of the proposed methods is confirmed by computer simulations.
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