A comparison of base station location optimization algorithms for dense HetNet deployments

Abstract

This final year project investigates and compares various base station location optimization algorithms for Dense Heterogeneous Network (HetNet) deployments. The increasing demand for high-speed and reliable connectivity has led to the deployment of HetNets, comprising diverse technologies and varying coverage areas. The primary objective of this research was to evaluate, compare, and analyze base station location optimization algorithms for Dense HetNet scenarios. A systematic literature review was conducted to gather information on diverse algorithms, followed by a comparative analysis based on parameters such as coverage, capacity and energy efficiency. A simulation environment was created to emulate various deployment scenarios, allowing for practical performance evaluation and validation. This project focused on a two-tier HetNet, with base station location optimization limited to the small cell tier. Three optimization algorithms were compared: Genetic Algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The outcomes of this study provide valuable insights for network planners, researchers, and industry practitioners involved in optimizing base station locations in Dense HetNet environments. The results show that SA outperforms GA and PSO in terms of network energy efficiency and sum rate.