  Least mean square (LMS) type adaptive algorithms have attracted much
attention due to their low computational complexity. In the scenarios of sparse
channel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS)
and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channel
sparsity. However, these proposed algorithms may hard to make tradeoff between
convergence speed and estimation performance with only one step-size. To solve
this problem, we propose three sparse iterative-promoting variable step-size
LMS (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 and
RL1-IPVSS-LMS respectively. Simulation results are provided to confirm
effectiveness of the proposed sparse channel estimation algorithms.
