Abstract
Sparse channels exist in many broadband wireless communication systems. To exploit the channel sparsity, invariable step-size zero-attracting normalized least mean square (ISS-ZA-NLMS) algorithm was applied in adaptive sparse channel estimation (ASCE). However, ISS-ZA-NLMS cannot achieve a good trade-off between the convergence rate, the computational cost, and the performance. In this paper, we propose a variable step-size ZA-NLMS (VSS-ZA-NLMS) algorithm to improve the ASCE. The performance of the proposed method is theoretically analyzed and verified by numerical simulations in terms of mean square deviation (MSD) and bit error rate (BER) metrics.
Highlights
Broadband transmission is one of the key techniques in wireless communication systems [1,2,3]
Inspired by least absolute shrinkage and selection operator (LASSO) algorithm [8], an l1-norm sparse constraint function can be used to take the advantage of channel sparsity in adaptive sparse channel estimation (ASCE); zero-attracting ISS-NLMS
A ZAVSS-NLMS filtering algorithm was proposed for channel estimation
Summary
Broadband transmission is one of the key techniques in wireless communication systems [1,2,3]. Conventional normalized least mean square (ISS-NLMS) algorithm using invariable step size was considered as one of the effective methods for channel estimation due to its easy implementation [4]. Inspired by least absolute shrinkage and selection operator (LASSO) algorithm [8], an l1-norm sparse constraint function can be used to take the advantage of channel sparsity in adaptive sparse channel estimation (ASCE); zero-attracting ISS-NLMS. Different from ISS-NLMS [4], variable step-size NLMS (VSS-NLMS) was first proposed to improve the estimation performance [11] without sacrificing the convergence speed. We propose a zero-attracting VSS-NLMS (ZA-VSS-NLMS) algorithm for sparse channel estimation. To derive the adaptive step size, different from the traditional VSS-NLMS algorithm in [11], two practical problems are considered: sparse channel model and tractable independent assumptions [12].
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