In this paper we provide a thorough ser( symbol error rate) analysis of two well known adaptive algorithms for equalization based on a novel least squares reference model that allows to treat the equalizer problem equivalently as system identification problem. An adaptive algorithm is a procedure for adjusting the parameters of an adaptive filter to minimize a cost function chosen for the task ...

In this paper we present a general formalism for the establishment of the family of selective partial update affine projection algorithms (SPU-APA). The SPU-APA, the SPU regularized APA (SPU-R-APA), the SPU partial rank algorithm (SPU-PRA), the SPU binormalized data reusing least mean squares (SPU-BNDR-LMS), and the SPU normalized LMS with orthogonal correction factors (SPU-NLMS-OCF) algorithms...

Least mean square (LMS)-based adaptive algorithms have attracted much attention due to their low computational complexity and reliable recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods have been proposed based on different sparse penalties, such as l1-norm LMS or zeroattracting LMS (ZA-LMS), reweighted ZA-LMS, and lp-norm LMS. However, th...

Existing systolic architectures for the LMS algorithm with delayed coeficient adaptation have large adaptation delay and hence degraded convergence behaviour. This paper presents a systolic architecture with minimal adaptation delay and input/output latency, thereby improving the convergence behaviour to near that of the original LMS algorithm. T h e architecture is synthesized by using a numbe...

This paper introduces a framework based on the LMS algorithm for sequential deconvolution of images acquired by a pushbroom hyperspectral imaging system. Considering a sequential model of image blurring phenomenon, we derive a zero-attracting LMS (ZA-LMS) algorithm for 2D image deconvolution. Its transient behavior is analyzed in the mean and mean-square sense. For hyperspectral images, a spect...

ABSTRACT In this paper, it was proved to be useful to use M[(1-1)-L’] FX-LMS algorism for Active Acoustic Shielding (AAS) window to enlarge the window size. The AAS is a system that can attenuate the sound passing though an open window. The AAS system is composed of many AAS cells set in an array. Each AAS cell consists of approximately colocated a microphone and a speaker. However, a size of e...

This paper develops adaptive step-size blind LMS algorithms and adaptive forgetting factor blind RLS algorithms for code-aided suppression of multiple access interference (MAI) and narrowband interference (NBI) in DS/CDMA systems. These algorithms optimally adapt both the step size (forgetting factor) and the weight vector of the blind linear multiuser detector using the received measurements. ...

In order to improve the sparsity exploitation performance of norm constraint least mean square (LMS) algorithms, a novel adaptive algorithm is proposed by introducing a variable p-norm-like constraint into the cost function of the LMS algorithm, which exerts a zero attraction to the weight updating iterations. The parameter p of the p-norm-like constraint is adjusted iteratively along the negat...

This paper deals with the problem of Adaptive Noise Cancellation (ANC) for the speech signal corrupted with an additive white Gaussian noise. After explaining the least Mean Square (LMS)-based adaptive filter and Kalman filter, it examine the hybrid Kalman-based LMS (KNLMS) technique for adaptation of the ANC. The proposed technique suggests a way to normalize LMS algorithm using Kalman filter....

We present analytical results, and details of implementation for a novel adaptive filter incorporating an approximate natural gradient tap-update algorithm, termed the simplified signed sparse LMS algorithm (SSSLMS). Each tap-update equation includes a term proportional to the tap-value, so that larger taps adapt more quickly than for a corresponding Least Mean Square (LMS) update. Results indi...