Modeling and Analysis of Wireless Channels via the Mixture of Gaussian Distribution

TL;DR

This paper introduces a unified Gaussian mixture model for wireless channel characterization, demonstrating high accuracy and low complexity through simulations and analytical performance metrics.

Contribution

It proposes a novel Gaussian mixture approach for modeling wireless channels, with parameter estimation via EM algorithm and closed-form expressions for key performance metrics.

Findings

Accurately models multipath and composite fading channels

Achieves high accuracy with low computational complexity

Provides analytical expressions for various wireless performance metrics

Abstract

Considerable efforts have been devoted to statistical modeling and the characterization of channels in a range of statistical models for fading channels. In this paper, we consider a unified approach to model wireless channels by the mixture of Gaussian (MoG) distribution. Simulations provided have shown the new probability density function to accurately characterize multipath fading as well as composite fading channels. We utilize the well known expectation-maximization algorithm to estimate the parameters of the MoG model and further utilize the Kullback-Leibler divergence and the mean square error criteria to demonstrate that our model provides both high accuracy and low computational complexity, in comparison with existing results. Additionally, we provide closed form expressions for several performance metrics used in wireless communication systems, including the moment generating function, the raw moments, the amount of fading, the outage probability, the average channel capacity, and the probability of energy detection for cognitive radio. Numerical Analysis and Monte-Carlo simulations are presented to corroborate the analytical results and to provide detailed performance comparisons with the other models in the literature.