Co-Evolutionary Dynamic Cell optimization Algorithm for HAPS Mobile Communications

High-Altitude Platform Stations (HAPSs) are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Single-cell frequency reuse using multiple cells can increase capacity to cover a wide area. Thus far, we have proposed a cell configuration optimization method based on a genetic algorithm (GA). We clarified the optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution scenario. Whereas user distributions differ depending on location. Thus, cell configuration optimization is required for a non-uniform user distribution. Each cell needs different antenna parameters for non-uniform user distributions, resulting in an increase in the number of parameters compared with a uniform user distribution, making the optimization difficult even by GA in some cases. To address this problem, we propose a co-evolutionary dynamic cell optimization algorithm. Co-evolution is one of the divide-and-conquer methods. The proposed method divides multiple cells into several groups to decrease the number of parameters to optimize at a time, and each group is optimized in order. The simulation results show that the cell configuration with the proposed method can increase the sum of the square root of throughput for non-uniform user distributions compared to that for uniform user distributions. Furthermore, the proposed method with three sub-areas can improve the sum of the square root of throughput while reducing the number of combinations performed compared to the method without subarea division for a nine-cell scenario.