Performance Analysis of Wireless Fading Channels: A Unified Approach

This thesis presents two major contributions. First, we consider a unified approach to model and simplify wireless fading channels or potentially fading scenarios by mixture distributions, namely using the mixture of Gaussian (MoG) and the mixture Gamma (MG) distributions. The approximation methodologies rely on maximum a posteriori and likelihood estimation techniques, such as the expectation-maximization and variational Bayes. Through the use of the mean-square error and the Kullback-Leibler divergence measures, we show that our models provide similar accuracy yet simpler representation than other existing models. In addition, we provide closed-form expressions or approximations for several performance metrics used in wireless communication systems, including the moment generating function, the raw moments, the amount of fading, the outage probability, and the average channel capacity. Second, through the use of the MoG and MG distributions, we provide a unifying and verstaile performance analysis over intricate generalized and composite fading channels in several contemporary wireless research topics, such as cognitive radio networks, cooperative- and diversity-based communications, and impulsive noise environments. The new approach and proposed distributions resolves intractable problems in many other fields, such as cognitive radio networks, cooperative networks, cascaded wireless applications and others.