The inherent feature of the Least Mean Squares (LMS) algorithm is the step size, and it requires careful adjustment. Small step size, required for small excess mean square error, results in slow convergence. Large step size, needed for fast adaptation, may result in loss of stability. Therefore, many modifications of the LMS algorithm, where the step size changes during the adaptation process d...

This paper describes a new variant of the least-mean-squares (LMS) algorithm, with low computational complexity, for updating an adaptive lter. The reduction in complexity is obtained by using values of the input data and the output error, quantized to the nearest power of two, to compute the gradient. This eliminates the need for multipliers or shifters in the algorithm's update section. The q...

Performance analysis of l0 norm constrained Recursive least Squares (RLS) algorithm is attempted in this paper. Though the performance pretty attractive compared to its various alternatives, no thorough study of theoretical analysis has been performed. Like the popular l0 Least Mean Squares (LMS) algorithm, in l0 RLS, a l0 norm penalty is added to provide zero tap attractions on the instantaneo...

Regression models are used in many areas of signal processing, e.g., spectral analysis and speech LPC, where block processing methods have typically been used to estimate the unknown coefficients. Iterative methods for adaptive estimation fall into two categories: the least-mean-square (LMS) algorithm and the recursive-least-squares (RLS) algorithm. The LMS algorithm offers low complexity and s...

The digital Fourier transform (DFT) and the adaptive least mean square (LMS) algorithm have existed for some time. This paper establishes a connection between them. The result is the “LMS spectrum analyzer,” a new means for the calculation of the DFT. The method uses a set of N periodic complex phasors whose frequencies are equally spaced from dc to the sampling frequency. The phasors are weigh...

An adaptive minimum bit error rate (MBER) linear multiuser detector (MUD) is proposed for DS-CDMA systems. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS) style adaptive algorithm is developed for training linear MUDs. Computer simulation results show that this adaptive MBER linear MUD outperforms two e...

This paper generalizes the proportionate-type adaptive algorithm to graph signal processing and proposes two recovery algorithms. The gain matrix of proportionate leads faster convergence than least mean squares (LMS) algorithm. In this paper, is obtained in a closed-form by minimizing gradient mean-square deviation (GMSD). first LMS (Pt-GLMS) which simply uses recursion process accelerates Pt-...

In this paper an efficient incomplete factorization preconditioner is proposed for the Least Mean Squares (LMS) adaptive filter. The proposed preconditioner is approximated from a priori knowledge of the factors of input correlation matrix with an incomplete strategy, motivated by the sparsity patter of the upper triangular factor in the QRD-RLS algorithm. The convergence properties of IPLMS al...

The convergence rate of the Least Mean Squares (LMS) algorithm is poor whenever the adaptive lter input auto-correlation matrix is ill-conditioned. In this paper we propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspaces corresponding to clusters of eigenvalues of the auto-correlation matrix of the input to the ad...

Third generation partnership project (3GPP) long term evolution (LTE) uses single carrier frequency division multiple access (SC-FDMA) in uplink transmission and multiple input multiple output orthogonal frequency division multiple access (MIMOOFDM) scheme for the downlink. A variable step size based least mean squares (LMS) algorithm is formulated for a single carrier frequency division multip...