Empirical evolution of a propagation model for low altitude platforms

This paper aims at optimizing empirically a propagation model for Low Altitude Platforms that evolves from the Okumura propagation model. Optimization exploits predicted results with the Okumura model, firstly, through a comparative evaluation against results reported in academic literature and, secondly, using a machine learning approach. The optimization parameters exploited are those that have been reported to affect the deployment of LAP as an aerial station: altitude, coverage, propagation path loss, Received Signal Strength, frequency, and transmission power. The evolved result is a propagation model for various altitudes and over different terrains that has been optimized using a Neural Network.