迭代促进变步长最小均方算法用于自适应稀疏信道估计
Iterative-Promoting Variable Step-size Least Mean Square Algorithm For Adaptive Sparse Channel Estimation
AI总结：
本文提出三种基于稀疏约束的迭代促进变步长LMS算法，以在稀疏信道估计中平衡收敛速度与估计性能。
摘要：
最小均方（LMS）类型自适应算法因其低计算复杂度而受到广泛关注。在稀疏信道估计场景中，零吸引LMS（ZA-LMS）、加权零吸引LMS（RZA-LMS）和加权L1范数LMS（RL1-LMS）已被提出以利用信道稀疏性。然而，仅使用一个步长的这些算法在收敛速度和估计性能之间难以取得平衡。为解决此问题，本文提出三种具有稀疏约束的稀疏迭代促进变步长LMS（IP-VSS-LMS）算法，即ZA、RZA和RL1。这些算法分别称为ZA-IPVSS-LMS、RZA-IPVSS-LMS和RL1-IPVSS-LMS。仿真结果用于验证所提稀疏信道估计算法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
