This paper proposes the use of evolutionary algorithms (EAs) to estimate the physical parameters of the generalized \(\alpha -\kappa -\mu \) 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 \(\alpha -\kappa -\mu \) 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 \(\alpha -\kappa -\mu \) 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.

本文提出使用进化算法（EA）来估计广义\（\ alpha-\ kappa-\ mu \）移动衰落信道模型的物理参数。参数的估计是一个基本步骤，可以使统计模型适应实际的实验数据。传统上用于估计\（\ alpha-\ kappa-\ mu \）参数的最大似然估计（MLE）方法通道使用非线性数值方法。在某些情况下，使用非线性数值方法可能会导致MLE做出物理上无法接受的估计，甚至无法获得结果。我们的建议是通过合并自适应方法，新的变异策略和足够的适应度函数来估计\（\ alpha-\ kappa-\ mu \）参数来创新现有EA 。实验结果表明，EA估计的参数（遗传算法，差分进化算法和具有自适应引导机制的差分进化算法）在物理上都是可以接受的。这些实验表明，EA优于MLE估计结果。