符号LMS的正则化参数选择方法及其加权L1-Norm约束算法
Regularization Parameter Selection Method for Sign LMS with Reweighted L1-Norm Constriant Algorithm
摘要：
宽带频率选择性衰落信道通常具有固有的稀疏性。通过利用稀疏性，自适应稀疏信道估计（ASCE）算法，例如，在加性高斯白噪声（AWGN）假设下，最小均方加权L1范数约束（LMS-RL 1）算法可以带来可观的性能增益。然而，在无线系统的实际场景中，信道估计性能经常会因意外的非高斯混合噪声（包括AWGN和脉冲噪声）而恶化。为了设计稳定的通信系统，提出了一种符号LMS-RL 1（SLMS-RL 1）算法，该算法能够在去除脉冲噪声的同时利用信道稀疏性。众所周知，SLMS-RL 1的正则化参数（REPA）选择是一个非常具有挑战性的问题。在最坏的情况下，不适当的REPA甚至可能导致SLMS-RL 1算法意外的不稳定收敛。本文提出了一种基于Monte Carlo的选择方法来选择合适的REPA，使SLMS-RL 1能够实现两个目标：稳定收敛以及使用稀疏信息。仿真结果证实了我们的研究。
英文摘要：
Broadband frequency-selective fading channels usually have the inherent sparse nature. By exploiting the sparsity, adaptive sparse channel estimation (ASCE) algorithms, e.g., least mean square with reweighted L1-norm constraint (LMS-RL1) algorithm, could bring a considerable performance gain under assumption of additive white Gaussian noise (AWGN). In practical scenario of wireless systems, however, channel estimation performance is often deteriorated by unexpected non-Gaussian mixture noises which include AWGN and impulsive noises. To design stable communication systems, sign LMS-RL1 (SLMS-RL1) algorithm is proposed to remove the impulsive noise and to exploit channel sparsity simultaneously. It is well known that regularization parameter (REPA) selection of SLMS-RL1 is a very challenging issue. In the worst case, inappropriate REPA may even result in unexpected instable convergence of SLMS-RL1 algorithm. In this paper, Monte Carlo based selection method is proposed to select suitable REPA so that SLMS-RL1 can achieve two goals: stable convergence as well as usage sparsity information. Simulation results are provided to corroborate our studies.
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。
