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. This article proposes the use of differential evolution (DE) algorithms for estimating parameters of the $\alpha -\eta -\mu$ fading channel, and to compare these to the traditional method of moments (MM) and maximum likelihood estimation (MLE) method. These traditional parameter estimation methods use nonlinear numerical methods, and the solution, if found, may be the optimal value, an approximation of the optimal value, or a local maximum. The authors demonstrate through comparative experiments using the MM and the MLE method that the DE algorithm for the proposed estimation demands a lower run time. In addition, it presents the error performance measured by the mean square error (MSE), near or above, as well as high robustness measured by the statistical analysis. Essentially, this algorithm always finds acceptable physical estimations with a good goodness of fit to experimental data. This estimating DE algorithm along with its proposed fitness function are original contributions of this paper. The received signal samples, used in the experiments of this paper, were randomly generated by the $\alpha -\eta -\mu$ fading simulator, which is another contribution of this paper. This proposed $\alpha -\eta -\mu$ fading simulator is based on the Clarke and Gans fading model and expands the generation range of current simulators, from $\mu$ integer multiples of 0.5, to $\mu$ integer multiples of 0.25.

移动无线电信号的统计建模需要估计描述假设地对此信道进行建模的概率分布的参数估计，因此该概率模型可确保对实验数据进行良好的调整。本文提出了使用差分进化（DE）算法来估算目标参数的方法。$\alpha -\eta -\mu$衰落信道，并将其与传统的矩量法（MM）和最大似然估计（MLE）方法进行比较。这些传统的参数估计方法使用非线性数值方法，并且如果找到解决方案，则可以是最佳值，最佳值的近似值或局部最大值。作者通过使用MM和MLE方法进行的比较实验证明，所提出的估计的DE算法所需的运行时间较短。此外，它还提供了通过均方误差（MSE）测得的接近或高于均方根误差的性能，以及通过统计分析测得的高鲁棒性。从本质上讲，该算法始终会找到可接受的物理估计值，并且非常适合实验数据。该估计DE算法及其提出的适应度函数是本文的原始贡献。本文实验中使用的接收信号样本是由$\alpha -\eta -\mu$衰落模拟器，这是本文的另一贡献。这个提议$\alpha -\eta -\mu$ 衰落模拟器基于Clarke和Gans衰落模型，并扩展了当前模拟器的生成范围，从 $\mu$ 0.5的整数倍，以 $\mu$ 0.25的整数倍。