A new reweighted l1-norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. Since standard LMS algorithm does not take into account the sparsity information about the channel impulse response (CIR), sparsity-aware modifications of the LMS algorithm aim at outperforming the standard LMS by introducing a penalty term to the standard LM...

A new algorithm, the binormalized data-reusing least mean squares (LMS) algorithm is presented. The new algorithm has been found to converge faster than other LMS-like algorithms, such as the Normalized LMS algorithm and several data-reusing LMS algorithms, when the input signal is highly correlated. The computational complexity of this new algorithm is only slightly higher than a recently prop...

Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneously distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defi...

This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time-varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the prop...

Abstract. Adaptive inverse control of linear system with fixed learning rate least mean square (LMS) algorithm is improved by varying the learning rate. This variable learning rate LMS algorithm is proved to be convergent by using Lyapunov method. It has better performance especially when there is noise in command input signal. And it is simpler than the Variable Step-size Normalized LMS algori...

This paper introduces a new variable step-size LMS (NVSS LMS) algorithm in an adaptive channel equalizer. This variant of VSS LMS algorithm is based on the weighting coefficinets bias/varance trade-off. We will show here that the adaptive equalizer with the NVSS LMS algorithm has favorable performance. Simulation results are provided to support the proposed implementation of the NVSS LMS.

In this paper, we propose two novel p-norm penalty least mean square (lp-LMS) algorithms as supplements of the conventional lp-LMS algorithm established for sparse adaptive filtering recently. A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error, w...

It is well known that DC offset degrades the performance of analog adaptive filters. The effects of DC offset on LMS derivatives such as sign-data LMS, sign-error LMS and sign-sign LMS have been studied to much extent but that on MLMS, VSSLMS and NLMS algorithms have remained relatively ignored. The present paper reports the effects of dc offset on LMS algorithm and its four variations Sign LMS...

In this work, a new class of stochastic gradient algorithm is developed based on q-calculus. Unlike the existing q-LMS algorithm, the proposed approach fully utilizes the concept of q-calculus by incorporating time-varying q parameter. The proposed enhanced q-LMS (Eq-LMS) algorithm utilizes a novel, parameterless concept of error-correlation energy and normalization of signal to ensure high con...