The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive filters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnostic adaptive filters like the NLMS. In this paper, we address this problem by...

In this paper, we propose a novel leaky least mean square (leaky LMS, LLMS) algorithm which employs a p-norm-like constraint to force the solution to be sparse in the application of system identification. As an extension of the LMS algorithm which is the most widely-used adaptive filtering technique, the LLMS algorithm has been proposed for decades, due to the deteriorated performance of the st...

Most of 2DPCA-enhanced approaches improve face recognition rate while at the expense of computation load. In this paper, an approach is proposed to greatly improve face recognition rate with slightly increased computation load. In this approach, the 2DPCA is applied against a face image to extract important image features for selection. A weight is then assigned to each of selected image featur...

This paper investigates the nonlinear effects of the LeastMean Square (LMS) adaptive predictor. Traditional analysis of the adaptive filter ignores the statistical dependence among successive tap-input vectors and bounds the performance of the adaptive filter by that of the finite-length Wiener filter. It is shown that the nonlinear effects make it possible for an adaptive transversal predictio...

Underwater acoustic channel (UAC) is one of the most challenging communication channels in the world, owing to its complex multi-path and absorption as well as variable ambient noise. Although adaptive equalization could effectively eliminate the inter-symbol interference (ISI) with the help of training sequences, the convergence rate of equalization in sparse UAC decreased remarkably. Besides,...

In all books and papers on adaptive filtering, the input autocorrelation matrix Rxx is always considered positive definite and hence the theoretical Wiener–Hopf normal equations (Rxxh 1⁄4 rxd) have a unique solution h 1⁄4 hopt (‘‘there is only a single global optimum’’, [B. Widrow, S. Stearns, Adaptive Signal Processing, Prentice-Hall, 1985, p. 21]) due to the invertibility of Rxx (i.e., it is ...

Multicarrier code-division multiple access (MC-CDMA) combines multicarrier transmission with direct sequence spread spectrum. Recently, different approaches have been adopted which do not assume a perfectly known channel. In this paper, we examine the forward-link performance of decision-directed adaptive detection schemes, with and without explicit channel estimation, for MC-CDMA systems opera...

0 2 'A A ,." . . ..... ........ 01 -1 5 -1.0 0-5 0 0 5 1.0 1-5 Introduction: The LMS (least mean square) algorithm was developed by Widrow in 1959 and originally applied to a neural network known as Adaline.' Since then the LMS algorithm has been successfully applied to a number of practical applications such as adaptive filters in telecommunication, radio and audio applications due to its robu...

An algorithm for multi-input multi-output (MIMO) adaptive filtering is introduced that distributes the adaptive computation over a set of linearly connected computational modules. Each module has an input and an output and transmits data to and receives data from its nearest neighbor. A gradient-based algorithm for adapting the parameters in each module to minimize the global mean-squared error...

The LMS algorithm is one of the most successful adaptive filtering algorithms. It uses the instantaneous value of the square of the error signal as an estimate of the mean-square error (MSE). The LMS algorithm changes (adapts) the filter tap weights so that the error signal is minimized in the mean square sense. In Trigonometric LMS (TLMS) and Hyperbolic LMS (HLMS), two new versions of LMS algo...