Wireless communications have become an essential part of our daily life. Despite the fact that wireless networks are simple to set up and deploy, the channel conditionsin wireless networks are susceptible to fading and attenuation, thereby reducing the transmission efficiency and reliability. The influence of unstable wireless channels, fading and attenuation is a significant limitation of wireless networks. The persistent and exponential increase in the usage of wireless communication services demands for enhanced reliability, transmission range and efficiency. This can be achieved byexploring and proposing different propagation techniques and models. This thesis evaluates a network model which makes use of an adaptive modulation coding scheme in the presence of an Amplify-and-Forward relaying environment. We deploy adaptive modulation technique in combination with Amplify-and-Forward relaying transmission mechanism to select the best suitable transmission path and obtain better transmission efficiency for a wireless communication system. The network model thus designed comprises of two links, one considering the relay transmission path that travels from the source-to-relay-to-destination, and the other link considering the direct transmission path traveling from the source to the destination, while both the links undergo Nakagami-m fading. In addition, the system ensures better performance in different conditions by making use of adaptive modulation and coding scheme and deploying distinct modulation and coding schemes based on the condition of the communication channel. The performance of this system model is analysed through mathematical analysis and the results are validated and depicted through simulation in MATLAB under different conditions. We have thereby derived the closed-form expressions for the Signal to Noise Ratio (SNR), Outage Probability, and the Packet Error Rate (PER) for the considered network model in the Nakagami-m fading environment. Different fading parameters that affect the system performance are considered and varied while obtaining the required results through MATLAB simulations. The system model proposed significantly reduces the outage probability and packet error rate and aids in achieving better system performance and reliability.

The generalized gamma (GG) distribution is an important model that has proven to be very flexible in practice for modeling data from several areas. This model has important sub-models, such as the Weibull, gamma, lognormal, Nakagami-m distributions, among others. In this work, our main objective is to develop different estimation procedures for the unknown parameters of the generalized gamma distribution and related models (Nakagami-m and gamma), considering both classical and Bayesian approaches. Under the Bayesian approach, we provide in a simple way necessary and sufficient conditions to check whether or not objective priors lead proper posterior distributions for the Nakagami, gamma, and GG distributions. As a result, one can easily check if the obtained posterior is proper or improper directly looking at the behavior of the improper prior. These theorems are applied to different objective priors such as Jeffreyss rule, Jeffreys prior, maximal data information prior and reference priors. Simulation studies were conducted to investigate the performance of the Bayes estimators. Moreover, maximum a posteriori (MAP) estimators for the Nakagami and gamma distribution that have simple closed-form expressions are proposed Numerical results demonstrate that the MAP estimators outperform the existing estimation procedures and produce almost unbiased estimates for the fading parameter even for a small sample size. Finally, a new lifetime distribution that is expressed as a two-component mixture of the GG distribution is presented. / A distribuição gama Generalizada (GG) possui um papel fundamental para modelar dados em diversas áreas. Tal distribuição possui como casos particulares importantes distribuições, tais como, Weibull, Gama, lognormal, Nakagami-m, dentre outras. Nesta tese, tem-se como objetivo principal, considerando as abordagens clássica e Bayesiana, desenvolver diferentes procedimentos de estimação para os parâmetros da distribuição gama generalizada e de alguns dos seus casos particulares dentre eles as distribuições Nakagami-m e Gama. Do ponto de vista Bayesiano, iremos propor de forma simples, condições suficientes e necessárias para verificar se diferentes distribuições a priori não-informativas impróprias conduzem a distribuições posteriori próprias. Tais resultados são apresentados para as distribuições Nakagami-m, gama e gama generalizada. Assim, com a criação de novas prioris não-informativas, para tais modelos, futuros pesquisadores poderão utilizar nossos resultados para verificar se as distribuições a posteriori obtidas são impróprias ou não. Aplicações dos teoremas propostos são apresentados em diferentes prioris objetivas, tais como, a regra de Jeffreys, priori Jeffreys, priori maximal data information e prioris de referência. Iremos também realizar estudos de simulação para investigar a influência destas prioris nas estimativas a posteriori. Além disso, são propostos estimadores de máxima a posteriori em forma fechada para as distribuições Nakagami-m e Gama. Por meio de estudos de simulação verificamos que tais estimadores superam os procedimentos de estimação existentes e produzem estimativas quase não-viciadas para os parâmetros de interesse. Por fim, apresentamos uma nova distribuição obtida considerando um modelo de mistura de distribuições gama generalizada.