This paper proposes variable step-size-norm constraint normalized least mean square (-
NLMS) algorithms for sparse channel identification. The mean square deviation of the-NLMS …

The well-known reweighted zero-attracting least mean square algorithm (RZA-LMS) has
been effective for the estimation of sparse system channels. However, the RZA-LMS …

As one of the recently proposed algorithms for sparse system identification, l_0 norm
constraint Least Mean Square (l_0-LMS) algorithm modifies the cost function of the …

In this study, the authors propose an l0‐norm penalised shrinkage linear least mean
squares (l0‐SH‐LMS) algorithm and an l0‐norm penalised shrinkage widely linear least …

In this paper, we propose a type of sparsity-aware set-membership normalized least mean
square (SM-NLMS) algorithm for sparse channel estimation and echo cancelation. The …

A sparse set‐membership normalised least mean square (SM‐NLMS) algorithm with a
correntropy penalty is proposed and its performance is investigated for estimating a sparse …

Recently, sparsity‐aware least mean square (LMS) algorithms have been proposed to
improve the performance of the standard LMS algorithm for various sparse signals, such as …

An improved norm‐constrained set‐membership normalized least mean square (INCSM‐
NLMS) algorithm is proposed for adaptive sparse channel estimation (ASCE). The proposed …

Wireless communication systems often require accurate channel state information (CSI) at
the receiver side. Typically, the CSI can be obtained from channel impulse response (CIR) …

The reweighted zero-attracting least mean square (RZA-LMS) algorithm has good
performance in sparse system identification. However, the convergence performance of the …