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Least mean square (LMS) type adaptive algorithms have attracted muchattention due to their low computational complexity. In the scenarios of sparsechannel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS)and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channelsparsity. However, these proposed algorithms may hard to make tradeoff betweenconvergence speed and estimation performance with only one step-size. To solvethis problem, we propose three sparse iterative-promoting variable step-sizeLMS (IP-VSS-LMS) algorithms with sparse constraints, i.e. ZA, RZA and RL1.These proposed algorithms are termed as ZA-IPVSS-LMS, RZA-IPVSS-LMS andRL1-IPVSS-LMS respectively. Simulation results are provided to confirmeffectiveness of the proposed sparse channel estimation algorithms.

Iterative-Promoting Variable Step-size Least Mean Square Algorithm For Adaptive Sparse Channel Estimation