The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

The least mean square (LMS) algorithm is the most popular adaptive ltering method due to its simplicity and predictable behavior. The convergence properties of the LMS algorithm can be improved by updating the lter coe cients in the transform domain. This paper presents a new transform domain LMS equalizer, called Discrete Cosine Transform Domain Parallel LMS (DCTPLMS) equalizer, which updates ...

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...

In this paper an adaptation technique using both genetic algorithm (GA) and least mean square algorithm (LMS) is proposed. The key control parameters tap-length and tap-weight are updated using the GA and the LMS algorithm respectively according to the value of mean square error (MSE). The proposed algorithm results optimal solutions very fast by reducing initialization and derivative problems....

The Global Positioning System (GPS) signals are very weak signal over wireless channels, so they are vulnerable to in-band interferences. Therefore, even a low-power interference can easily spoof GPS receivers. Among the variety of GPS signal interference, spoofing is considered as the most dangerous intentional interference. The spoofing effects can mitigate with an appropriate strategy in the...