Enhancing 5G Base Station Placement in Outdoor 3D Environments Using a Quantum Genetic Algorithm

Abstract

The proliferation of fifth-generation (5G) base stations has introduced significant challenges related to suboptimal deployment and escalating construction expenses. To address these issues, this paper introduces an optimization methodology for 5G base station placement, specifically applied to a district in Kowloon, Hong Kong, which balances the competing objectives of signal coverage and deployment cost through a weighted approach. Our approach begins by simulating signal propagation using detailed three- dimensional (3D) maps combined with ray-tracing models. We incorporate demographic data to generate a realistic, random distribution of users along the map's street-level areas. We identify viable candidate sites for new base stations, considering both ground-level and rooftop installations, while imposing constraints on the tower heights. To mitigate construction expenditures, the model also evaluates the integration of pre-existing base station infrastructure. Furthermore, a novel dynamically adjusted quantum genetic algorithm (DAQGA) is proposed to determine the optimal base station configuration, using network coverage and construction cost as its primary objective functions. The efficacy of the resulting layout is assessed using a metric based on signal reception strength. The simulation results confirm that this optimization technique successfully pinpoints coverage blind spots in the planning zone, highlighting connectivity failures attributable to building-induced signal obstruction or locations outside the service range. An optimal trade-off between deployment objectives is achieved when the weights for coverage and cost are assigned values of 0.7 and 0.3, respectively. When benchmarked against four alternative algorithms, our proposed improved algorithm demonstrates superior performance in both convergence rate and solution stability.