The application of the least-mean-square (LMS) and recursive-least-square (RLS) algorithms to the estimation of symbol period is discussed. The algorithms are based on the measurements of time between two consecutive detected transitions in noisy waveforms. Two versions of the algorithm are developed, for white and colored measurement noise model. Conditions are derived that guarantee proper be...

Radio Frequency (RF) interference is inherent in all wireless systems and is one of the most significant design parameters of cellular and other mobile systems. In this paper, it is shown that how a non-linear adaptive Volterra filter (Polynomial filter where input and output signals are related through Volterra series) helps track the statistics of the input data and dynamics of a direct seque...

Wireless communication systems operating over time-varying fading channels require adaptive signal processing to equalize the channel variations at the receiver. In wireless applications, the received signal is typically affected by frequency-selective fading and channel equalization is required to mitigate the resulting inter symbol interference (ISI). In this paper an adaptive model has been ...

Intersymbol interference is an unwanted phenomenon that makes communication less reliable. However, an equalizer can reduce the bad influence of intersymbol interference. In general, my work is to compare different equalizers in various scenarios. In the first part of this paper, I explain the motivation of using equalization in detail, followed by introducing prerequisite knowledge of equaliza...

An adaptive multipath decorrelating multiuser receiver is considered for application in Rayleigh fading multipath channels with significant Doppler spread. Coherent diversity combining is performed using adaptively obtained channel estimates in a manner that minimizes the impact of estimation errors on data detection. The bit-error rate of the receiver is evaluated analytically, showing depende...

A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily u...

In this technical report we analyse the performance of diffusion strategies applied to the Least-Mean-Square adaptive filter. We configure a network of cooperative agents running adaptive filters and discuss their behaviour when compared with a non-cooperative agent which represents the average of the network. The analysis provides conditions under which diversity in the filter parameters is be...

Ensemble of networks has been proven to give better prediction result than a single network. Two commonly used way of determining the ensemble weights are simple average ensemble method and the generalized ensemble method. In the paper, we propose the weighted least square ensemble network. The major difference between this method and the other ensemble methods is that we do not assume that nei...

We present a new hierarchic kernel based modeling technique for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes the typical single-layer kernel based model in a hierarchical structure, such that the weights of a kernel model over each dimension are modeled over the adjacent dimension. We show that the impos...