A differential evolution algorithm for estimating mobile channel parameters α−η−μ. (15th April 2021) - Lemos, Carlos Paula; Veiga, Antônio Cláudio Paschoarelli; Fasolo, Sandro Adriano
Abstract: 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 α − η − μ 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 α − η − μ fading simulator, which is another contribution of this paper. This proposed α − η − μ fading simulator is based on the Clarke and Gans fading model and expands the generation range of current simulators, from μ integer multiples of 0.5, to μ integer multiples of 0.25. Highlights: A Differential Evolution algorithm to estimate the α η μ channel parameters is proposed. A novel α η μ fading simulator that can create data for the experiments is developed. Analysis proves the validity of the proposed Differential Evolution algorithm. Experiments show that this algorithm outperforms the maximum-likelihood estimator.