Contribuições para a simulação de canais desvanecidos modelados com as distribuições -- e --

• Ano de defesa: 2021
• Autor(a) principal: Lemos, Carlos Paula
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: Canal com desvanecimento >; − − fading channel; Algoritmos evolutivos; Evolutionary algorithms; Estimador de máxima verossimilhança; Maximum likelihood estimation; Geradores aleatórios; Random generators; Estimação de parâmetros; Parameter estimation; Modelo de atenuação de Clarke e Gans; Clarke and Gans fading model; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOES; Algoritmos; Estimativa de parâmetros
• Link de acesso: https://repositorio.ufu.br/handle/123456789/31750; https://doi.org/10.14393/ufu.te.2021.6018

The statistical modeling of mobile radio signals requires the estimation of parameters that describe the probability distribution that hypothetically models this channel so that this probabilistic model guarantees a good adjustment to the experimental data. The maximum likelihood estimation (MLE) method that is traditionally used for estimating the parameters for the fading channels − − and − − use nonlinear numerical methods, and the solution, if found, maybe the optimal value, an approximation of the optimal value, or a local maximum. The purpose of this work is to innovate some of the existing evolutionary algorithms (EAs) incorporating an adaptive approach, a new mutation strategy, and an adequate aptitude function for the estimation of the fading parameters of the channels −− and − −. It is proposed to use the following EAs for the construction of the estimators: genetic algorithms, differential evolution algorithms, and differential evolution algorithms with an adaptive guiding mechanism. Experimental results are presented to confirm that parameters estimated by the proposed EAs are all physically acceptable. These experiments show that the EAs outperform MLE estimation results. Another contribution of this study is the − − and the − − fading simulator based on the Clarke and Gans fading model, which expands the generation range of current simulators, from integer multiples of 1 and 0.5 to integer multiples of 0.5 and 0.25 respectively.