Time-Efficient Neural-Network-Based Dynamic Area Optimization Algorithm for High-Altitude Platform Station Mobile Communications

Abstract

There is a growing interest in high-altitude platform stations (HAPSs) as potential telecommunication infrastructures in the stratosphere, providing direct communication services to ground-based smartphones. Enhanced coverage and capacity can be realized in HAPSs by adopting multicell configurations. To improve the communication quality, previous studies have investigated methods based on search algorithms, such as genetic algorithms (GAs), which dynamically optimize antenna parameters. However, these methods face hurdles in swiftly adapting to sudden distribution shifts from natural disasters or major events due to their high computational requirements. Moreover, they do not utilize the previous optimization results, which require calculations each time. This study introduces a novel optimization approach based on a neural network (NN) model that is trained on GA solutions. The simple model is easy to implement and allows for instantaneous adaptation to unexpected distribution changes. However, the NN faces the difficulty of capturing the dependencies among neighboring cells. To address the problem, a classifier chain (CC), which chains multiple classifiers to learn output relationships, is integrated into the NN. However, the performance of the CC depends on the output sequence. Therefore, we employ an ensemble approach to integrate the CCs with different sequences and select the best solution. The results of simulations based on distributions in Japan indicate that the proposed method achieves a total throughput whose cumulative distribution function (CDF) is close to that obtained by the GA solutions. In addition, the results show that the proposed method is more time-efficient than GA in terms of the total time required to optimize each user distribution.