Statistical inference subject to nonnegativity constraints is a frequently occurring problem in signal processing. The nonnegative least-mean-square (NNLMS) algorithm was derived to address such problems in an online way. This algorithm builds on a fixed-point iteration strategy driven by the Karush-Kuhn-Tucker conditions. It was shown to provide low variance estimates, but it however suffers f...

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...

An improvement of Signal-to-Interference Ratio (SIR) at the base station of a wireless communication system can be obtained using a suitable choice of an array antenna. In this paper we consider Concentric Circular Antenna Array (CCAA) which can be used for adaptive antenna systems. A new adaptive algorithm, called least mean square-least mean Square (LLMS) algorithm, has been proposed for diff...

The Normalized Least Mean Square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear ltering. It possesses many advantages over the LMS algorithm, including having a faster convergence and providing for an automatic time-varying choice of the LMS step-size parameter which aaects the stability , steady-state mean square error (MSE) and convergence speed of...

This paper proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The propos...

A low complexity wavelet packet transform-based least mean square (LMS) adaptive beamformer is presented in this paper. This beamformer uses wavelet packet transform as the preprocessing, reduces the signal dimension in wavelet packet domain for low complexity and denoising, and employs least mean square algorithm to implement adaptive beamformer. Theoretical analysis and simulations demonstrat...

In this paper, a novel data–aided stochastic gradient algorithm for adjustment of the widely linear (WL) minimum mean–squared error (MMSE) filter for multiple access interference (MAI) suppression for direct–sequence code–division multiple access (DS–CDMA) is introduced and analyzed. We give analytical expressions for the steady–state signal–to–interference– plus–noise ratio (SINR) of the propo...

{In almost all analyses of the least-mean-square (LMS) adaptive lter, it is assumed that the lter coeecients are statistically independent of the input data currently in lter memory, an assumption that is incorrect for shift-input data. In this paper, we present a method for deriving a set of linear update equations that can be used to predict the exact statistical behavior of a nite-impulse-re...

Sparse adaptive filtering has gained much attention due to its wide applicability in the field of signal processing. Among the main algorithm families, sparse norm constraint adaptive filters develop rapidly in recent years. However, when applied for system identification, most priori work in sparse norm constraint adaptive filtering suffers from the difficulty of adaptability to the sparsity o...

The least mean square (LMS) algorithm is the most popular adaptive ltering method due to its simplicity and predictable behavior. The convergence properties of the LMS algorithm can be improved by updating the lter coe cients in the transform domain. This paper presents a new transform domain LMS equalizer, called Discrete Cosine Transform Domain Parallel LMS (DCTPLMS) equalizer, which updates ...