K-Means Clustering-Aided Dynamic Multi-Cell Optimization Algorithm for HAPS

High Altitude Platform Station (HAPS), functioning as a flying base station (BS) positioned in the stratosphere, has gradually captured more interest in the field of mobile communications. HAPS holds the potential to deliver extensive coverage areas and resilient networks in the face of disasters. Using beamforming techniques, a HAPS can configure multiple cells within its coverage area to serve a large user population. However, the position of HAPS and the distribution of user equipment (UE) changes with time, resulting in changes in the relative position between HAPS and UEs. Consequently, the maximum throughput of UE decreases and some UEs can suffer network outages. A previously proposed method treats this as an optimization problem and tries to maximize throughput based on an objective function. However, the computational complexity is still too high for real-time control. To help beams respond faster to position changes, some AI-based methods are proposed but they cannot guarantee that low throughput UEs are well minimized. In this paper, we propose a K-means clustering-aided particle swarm optimization (PSO) algorithm that can adjust the cell configuration to minimize the number of low throughput UEs. Taking advantage of K-means clustering, this method redefines the search range for each parameter to help PSO quickly converge to the optimum. By running simulations using realistic UE distributions in some cities, we demonstrate the superiority of our proposed method in terms of computational complexity and ability to reduce low throughput UEs.