AI-Driven Path Loss Optimization in 4G Networks Through PSO Algorithm

This paper investigates signal attenuation in the mobile user interface of a 4G cellular network and proposes an AI-driven strategy to enhance path loss prediction. Employing the Particle Swarm Optimization (PSO) algorithm coupled with AI techniques, it analyzes and forecasts path losses. Measurements were conducted at 1800 MHz and 2100 MHz frequencies in a suburban area of Tebessa. The objective was to minimize the Root Mean Square Error (RMSE) between theoretical and observed path losses by optimizing parameters for the COST231 and Ericsson models using AI-supported PSO. The optimized models, benefiting from the synergy between AI and PSO, exhibited superior performance compared to unoptimized empirical models.