Channel Parameter Estimation in Millimeter-Wave Propagation Environments Using Genetic Algorithm

Abstract

This paper explores the suitability of the natureinspired Genetic Algorithm (GA) for estimating propagation channel parameters in an indoor millimeter-wave environment at 60 GHz. Our work is based on real propagation channel measurements and the goal is two-fold: i) to estimate physically plausible parameters, and ii) to provide improvements in terms of the goodness-of-fit when compared to traditional methods such as Nonlinear Least-Squares (NLS). To better contextualize the use of the GA within the meta-heuristic family of algorithms, a more recent meta-heuristic approach, named Hybrid Grey Wolf and Whale Optimization Algorithm (HGW-WOA), is also exercised. We adopt popular small-scale and shadowed fading models which accurately characterize these mm-wave links. A total of 72 fading scenarios are investigated. The goodness-of-fit of these models, using different parameter estimation methods, is assessed through the Akaike Information Criterion. Our investigation has shown that the GA overwhelmingly outperformed the NLS. Similarly, the GA performed better than the HGW-WOA in the majority of scenarios. Thus, we demonstrate that the GA is a promising technique for the robust estimation of fading parameters.

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“…By combining different strategies at different algorithm stages, they improved BWO's performance, successfully applied to engineering design problems, and tested on the CEC benchmark dataset. Gomes et al [49] proposed a hybrid algorithm and compared it with GA. They applied metaheuristic algorithms to channel parameter estimation and successfully demonstrated GA's advantages over the hybrid algorithm in channel parameter estimation.…”