5G network planning in connecting urban areas for trains service using a genetic algorithm

Abstract

The adoption of 5G for Railways (5G-R) is expanding, particularly in high-speed trains, due to the benefits offered by 5G technology. High-speed trains must provide seamless connectivity and Quality of Service (QoS) to ensure passengers have a satisfactory experience throughout their journey. Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference. In particular, when a user with a mobile phone is a passenger in a high speed train traversing between urban centres, the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service. The utilization of macro, pico, and femto cells may optimize the utilization of 5G resources. In this paper, a Genetic Algorithm (GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented. The network is divided into three cell types, macro, pico, and femto cells—and the optimization process is designed to achieve a balance between key objectives: providing comprehensive coverage, minimizing interference, and maximizing energy efficiency. The study focuses on environments with high user density, such as high-speed trains, where reliable and high-quality connectivity is critical. Through simulations, the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated. The algorithm is compared with the Particle Swarm Optimisation (PSO) and the Simulated Annealing (SA) methods and interesting insights emerged. The GA offers a strong balance between coverage and efficiency, achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels. Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.