Gaussian Mixture Parameter Estimation for Cognitive Radio and Network Surveillance Applications

Abstract

Abstract: Cognitive radio is being heralded as an important, new frontier in radio communication; one that promises to overcome spectrum-congestion hotspots. In the cognitive radio approach, different groups of radio users may operate in the same spectrum region. The radio systems intelligently and dynamically change their transmission schemes during operation to co-exist. Therefore, a fundamental issue in cognitive radio is being able to assess and track users on a wireless channel. This problem occurs when the cognitive radio system is searching for friendly data or usable channels. The received and preprocessed signal typically turns out to be a Gaussian mixture random variable with common component variance. The task then is to quickly estimate and continue tracking the parameters of this mixture random variable from samples of data. Two efficient and easily-implemented methods for estimating all these parameters are developed, using a novel approach of fitting the cumulative distribution function (cdf) to the estimated cdf, and nonlinear, unconstrained optimization. The algorithms are demonstrated through simulation experiments. The algorithms presented here compare favourably with the general EM algorithm and are better suited for cognitive radio and wireless network surveillance applications