  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.
