Broadband signal transmission over frequencyselective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation methods is least mean square (LMS) algorithm. However, LMS-based method is often degraded by random scaling of input training signal. To improve the estimation performance, in this paper we apply the standard l...

An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical...

The aim of this paper is to implement various adaptive noise cancellers (ANC) for speech enhancement based on gradient descent approach, namely the least-mean square (LMS) algorithm and then enhanced to variable step size strategy. In practical application of the LMS algorithm, a key parameter is the step size. As is well known, if the step size is large, the convergence rate of the LMS algorit...

In this paper, e cient pipelined architectures for the implementation of the Transform Domain LMS algorithm, are presented. Pipelining of the TD-LMS algorithm is achieved by introducing an amount of time delay into the original adaptive scheme. The adaptation delay introduced to the TD-LMS algorithm allows for the development of pipelined architectures. By retiming the delays existing in the er...

In many identi cation and tracking problems, an accurate estimate of the measurement noise variance is available. A partially adaptive LMS-type algorithm is developed which can exploit this information while maintaining the simplicity and robustness of LMS. This noise constrained LMS (NCLMS) algorithm is a type of variable step-size LMS algorithm, which is derived by adding constraints to the m...

In this paper we discuss how to improve the behavior of the Variable Step-Size LMS (VSSLMS) algorithm that was proposed in [6]. In many practical applications from the field of system identification, an estimation of the noise variance is available. Therefore we introduce a modified VS-LMS algorithm that exploits this informations in order to provide a faster convergence speed. In this paper th...

The paper proposes a new adaptive VS LMS algorithm, obtained by combining LMS algorithms with different step sizes without calculating their weighting coefficients. As a criterion for choosing the VS LMS algorithm step size, we take the ratio between the weighting coefficients' bias and variance. Identification of an unknown system in nonstationary noisy environment is performed and simulations...

The Normalized Least Mean Square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear FIR ltering. It provides an automatic choice for the LMS step-size parameter which aaects the stability, convergence speed and steady-state performance of the algorithm. In this paper, we generalize the NLMS algorithm by deriving a class of Nonlinear Normalized LMS-type (...

The statistical efficiency of a learning algorithm applied to the adaptation of a given set of variable weights is defined as the ratio of the quality of the converged solution to the amount of data used in training the weights. Statistical efficiency is computed by averaging over an ensemble of learning experiences. A high quality solution is very close to optimal, while a low quality solution...

In a distributed network environment, the diffusion-least mean squares (LMS) algorithm gives faster convergence than the original LMS algorithm. It has also been observed that, the diffusion-LMS generally outperforms other distributed LMS algorithms like spatial LMS and incremental LMS. However, both the original LMS and diffusion-LMS are not applicable in non-linear environments where data may...