Al-Driven Optimization of Cellular Network Coverage and Capacity: Tower Placement and Dynamic Parameter Tuning

Abstract

The rapid growth of wireless communication has become vital to modern

society, connecting people and devices across diverse applications. However, en- suring robust coverage and sufficient capacity in cellular networks especially in

densely populated urban areas remains a significant challenge due to complex en- vironments and increasing user demands. This study aims to enhance network

performance by optimizing key operational settings and strategically expanding

infrastructure. We employ intelligent algorithms to fine tune base station param- eters using Bayesian Optimization (BO) and determine optimal locations for new

towers using a Genetic Algorithm (GA), balancing signal quality, interference,

and practical constraints. The results demonstrate that these data-driven meth- ods significantly improve coverage and capacity, offering a promising approach for

efficient and adaptive network management.