Fast-Fading Modeling in Wireless Industrial Communications

Wireless channel properties in industrial environments are significantly impacted by heavy machinery, leading to complex multipath propagation and strong blockage effects. Conventional empirical models employed in factory settings are constrained by their limited flexibility and applicability to diverse industrial conditions. In this study, this limitation is tackled in a twofold way. First, machine learning algorithms, including linear regression and a Multi-Layer Perceptron, are employed to capture the complex relationships between fast-fading effects and key features of the industrial layout. Second, a flexible empirical formula is proposed to model fast-fading phenomena with enhanced adaptability, providing a comprehensive solution for diverse industrial contexts. The results align with previous studies and provide some trends in fast-fading sensitivity to different industrial features. The machine learning model demonstrates superior accuracy compared to the empirical formula, which nevertheless still achieves reasonable performance despite its simplicity.