Sparse Channel Estimator via Block Approximate Zero Norm
Yang Hu · 2019
The progressively increasing data rates have induced the research and application of sparse channel model. To obtain the source signal via the signal got from the terminal equipment, we expect to improve the estimation performance such as accuracy by exploiting the sparsity of the channel impulse response. Our work focuses on exploiting training signal to detect and estimate the channel. However, the classical algorithms such as greedy algorithm like Orthogonal Matching Pursuit (OMP), Least Mean Square (LMS), and even the modified Least Mean Square with approximate-zero norm, l1norm or l2norm as cost function, are not satisfying in the efficiency and reliability as we expected, i.e., their performances are expected better in convergent speed and bias of convergent value in our study. To design superior channel estimator, we tried 3 new adaptive algorithms called LMS integrated with Block l1,0norm (BL10) and LMS integrated with Block l2,0norm (BL20). Simulation in this paper will demonstrate the superiority of the proposed methods when dealing with sparse system.