Asia-Pacific Conference on Communications
Iterative-Promoting Variable Step-Size LMS Algorithm based Adaptive Sparse Channel Estimation
Beiyi Liu, Guan Gui, Li Xu,
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Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity. For estimating sparse channels, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS) and reweighted L1-norm LMS (RL1-LMS) have been developed to exploit channel sparsity. However, these proposed algorithms may be difficult to make tradeoff between convergence speed and estimation performance with the invariable step-size. To solve this problem, we propose three sparse iterative-promoting variable step-size LMS (IP-VSS-LMS) channel estimation algorithms with sparse constraints, i.e. ZA, RZA, and RL1. The proposed algorithms are termed as ZA-IPVSS-LMS, RZA-IPVSS-LMS and RL1-IPVSS-LMS respectively. Simulation results are provided to confirm effectiveness of the proposed sparse channel estimation algorithms in different scenarios.