Estimation of $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel parameters using evolutionary algorithms

Carlos Paula Lemos (), Antônio Cláudio Paschoarelli Veiga and Sandro Adriano Fasolo
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Carlos Paula Lemos: Ciência e Tecnologia do Triângulo Mineiro
Antônio Cláudio Paschoarelli Veiga: Universidade Federal de Uberlândia
Sandro Adriano Fasolo: Universidade Federal de São João del Rei

Telecommunication Systems: Modelling, Analysis, Design and Management, 2021, vol. 77, issue 1, No 11, 189-211

Abstract: Abstract This paper proposes the use of evolutionary algorithms (EAs) to estimate the physical parameters of the generalized $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel model. The estimation of parameters is a fundamental step that allows for the statistical model to adjust to the real experimental data. The The maximum likelihood estimation (MLE) method that is traditionally used for estimating parameters of the $$\alpha -\kappa -\mu $$ α - κ - μ channel uses nonlinear numerical methods. In some cases, the use of nonlinear numerical methods may lead the MLE to make physically unacceptable estimations, or even to not be able to obtain a result. Our proposal is to innovate the existing EAs by incorporating an adaptive approach, a new mutation strategy and an adequate fitness function for the estimation of $$\alpha -\kappa -\mu $$ α - κ - μ parameters. Experimental results are presented to confirm that parameters estimated by the EAs (genetic algorithms, differential evolution algorithms, and differential evolution algorithms with an adaptive guiding mechanism) are all physically acceptable. These experiments show that the EAs outperform MLE estimation results.

Keywords: $$\alpha -\kappa -\mu $$ α - κ - μ distribuition; Parameter estimation; Evolutionary algorithm; Genetic algorithm; Differential evolution; Maximum likelihood estimation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11235-020-00743-0

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