In this paper, Least Mean Square (LMS) adaptive noise reduction algorithm is proposed to enhance the speech signal from the noisy speech. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is seen that the p...

The smart antennas are broadly used in wireless communication. least mean square (LMS) algorithm is a procedure that concerned controlling the antenna pattern to accommodate specified requirements such as steering beam toward desired signal, addition placing deep nulls direction of unwanted signals. conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady stat...

The wireless network highly affected by the interference in the spectrum which reduces the throughput of the network. In order to reduce the problem going for the beamforming technology. In this paper we suggest different training sequence algorithms like Recursive Least Squares (RLS) and Least Mean Squares (LMS) are analysed and compared. The simulation is based on the single user networks whi...

For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during station...

In this paper, a technique to identify the filter bank coefficients of Wavelets db4 and coif5 using adaptive filter NLMS algorithm is presented. Filter bank coefficients of the wavelet are treated as the weight vector of adaptive filter, changes with each iteration and approach to the desired value after little iteration. When we compare the two adaptive algorithms viz. Least Mean Square (LMS) ...

This paper proposes distributed adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks. We present sparsity-aware conventional and modified distributed CG algorithms using l1 and log-sum penalty functions. The proposed sparsity-aware diffusion distributed CG algorithms have an improved performance in terms of mean...

This article present a new method of accuracy verification of calibrated one-port vector network analyzers based on the least mean square algorithm. The method is of particular interest for significantly limited frequency ranges as well as for cost-effective S-parameter measurement systems where the use of conventional ripple test may be impractical and/or relatively expensive. Experimental stu...

This paper presents distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks. Both incre-mental and diffusion adaptive solutions are all considered. The distributed conventional (CG) and modified CG (MCG) algorithms have an improved performance in terms of mean square error as compared with least-mean square (LMS)-based algorithms and a performance th...

The normalized least mean square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear filtering. It possesses many advantages over the LMS algorithm, including having a faster convergence and providing for an automatic time-varying choice of the LMS stepsize parameter that affects the stability, steady-state mean square error (MSE), and convergence speed o...

The least-mean-square (LMS) algorithm is an adaptation scheme widely used in practice due to its simplicity. In some applications the involved signals are continuous-time. Then, usually either a fully analog implementation of the LMS algorithm is applied or the input data are sampled by analog-to-digital (AD) converters to be processed digitally. A purely digital realization is most often the p...