Adaptive Meta-Heuristic Based Okumura-Hata Pathloss Model for LTE at 2300 MHz using Auto-Correlation Chaotic Particle Swarm Optimization

Abstract

An accurate pathloss prediction is essential for efficient wireless network design and optimization. However, existing empirical models such as Okumura-Hata often yield inaccurate results when applied to environments different from where it was originally developed. This study presents an Adaptive Meta-Heuristic based Okumura-Hata pathloss model using an Auto-Correlation Chaotic Particle Swarm Optimization (ACPSO) algorithm for Long Term Evolution (LTE) networks operating at 2300 MHz in Ibadan metropolis, Nigeria. Drive test measurements were conducted in three different locations of the metropolis. The developed ACPSO algorithm integrates adaptive time-varying auto-correlation and chaotic processes to improve the convergence speed and minimize prediction errors. Simulation and performance evaluations using MATLAB R2023b were carried out, and the results were compared with the conventional Okumura and existing Auto-Regression Particle Swarm Optimization (ARPSO) based models using Root Mean Squared Error (RMSE) and Mean Absolute Deviation (MAD) metrics. Findings show that the developed ACPSO-Okumura-Hata model achieved the closest agreement with the measured data. The developed model provides a reliable framework for pathloss estimation and serves as a valuable tool for improving LTE network planning and optimization in Nigerian and similar terrains.