New steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth, etc., sense. During adaptation, the weights undergo exponential relaxation toward their optimal solutions. T ime constants have been derived, and surprisingly they turn out to be proportional to the time constants that would have been obtained if the ste...

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, presents a new Normalized Least Mean Square (NLMS) algorithm, tailored for adaptive identification of invariant systems impulse responses with speech inputs. The proposed Square Root NLMS (SR-NLMS) algorithm is based on a specific normalization of the LMS adaptive filter input, by the Euclidean norm of the tap-input. In fact, we cancel the term involving the statistics of the input ...

The performance of multiuser adaptive minimum mean square error (MMSE) receiver combined with multiple antennas is investigated for wideband code division multiple access systems (W-CDMA) in multipath fading channels. In the adaptive implementation of the receiver, performance of various algorithms like least mean square (LMS), normalized least mean square (NLMS), recursive least square (RLS) a...

An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence a...

In this correspondence, a least mean squares (LMS)-based algorithm is devised for unbiased system identification in the presence of white input and output noise, assuming that the ratio of the noise powers is known. The proposed approach aims to minimize the mean square value of the equation-error function under a constant-norm constraint and is equivalent to minimizing a modified mean square e...