Statistical tuning of Walfisch-Ikegami propagation model using Particle Swarm Optimization

Propagation models play a vital role in the characterization and design of wireless and mobile communications networks. However, if they are utilized in a different environment than the one they were formulated for, propagation path-loss models may produce unacceptable deviation in predictions. This paper proposes a statistical tuning technique based on Particle Swarm Optimization (PSO) to calibrate the COST-231-Walfisch-Ikegami path loss propagation model using collected received signal power measurements from a deployed 3G network. The parameters of the modified model (partial coefficients of regression) are estimated considering the roof height, distance between buildings, and road orientation angle with respect to an incident radio signal as random variables. Appropriateness and validity of the tuned propagation model was demonstrated through comparisons and statistical indicators.