Smart Multi-Objective Unmanned Aerial Vehicles as Base Stations Placement in 6G Cellular Telecommunication Networks Using NSGA-II Optimisation Algorithm

Abstract

The deployment of unmanned aerial vehicles (UAVs) as aerial base stations in cellular networks presents a dynamic solution to meet the demands of high and fluctuating traffic patterns. Efficient placement of UAVs is crucial to harness their benefits and adapt intelligently to environmental changes. This paper introduces a multi-objective optimisation model aimed at maximising user coverage and minimising overlap among drone-based base stations in 6G networks. To address this optimisation issue, the Nondominated Sorting Genetic Algorithm II (NSGA-II) is deployed, enabling the identification of Pareto optimal solutions that strike a balance between conflicting objectives. Through simulations conducted under various scenarios, the proposed model demonstrated significant improvements in user coverage and reduction of overlap among base stations compared to existing techniques. The findings reveal the effectiveness of the proposed model in balancing the objectives of coverage and overlap, resulting in an enhanced 6G network design. The method achieves an average coverage probability of 98.39% and an average overlap improvement percentage (OIP) of 92.39%, validated through 50 experimental runs. These results underscore the robustness and superiority of the proposed NSGA-II-based strategy in optimising DBS placement, contributing to the advancement of 6G cellular networks.