Group Merging Particle Swarm Optimization Algorithm for Rural Base Station Deployment

Abstract

The deployment of 6G base stations has brought convenience to the lives of people, but effective implementation in rural areas still faces significant obstacles, making the requirements for base station deployment more demanding. This paper first models the rural base station deployment environment and introduces three objective functions: the effective area of base station deployment, signal security, and uniformity. A weighted multi-objective function is also constructed. Additionally, a Group Merging Particle Swarm Optimization Algorithm (GMPSO) is proposed, incorporating the concept of splitting and perturbation terms into the particle swarm optimization to enhance the optimization capability of the algorithm. Experiments were conducted in both fixed and random environments, comparing various PSO variants from recent years. The impact of different numbers of base stations and clustering centers was also analyzed. The results indicate that GMPSO exhibits significant advantages in base station layout, achieving over 90% coverage and success rates even with fewer base stations, demonstrating the practicality of GMPSO.