Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

Siyuan Yang (), Mondher Bouazizi, Tomoaki Ohtsuki, Yohei Shibata, Wataru Takabatake, Kenji Hoshino and Atsushi Nagate
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Siyuan Yang: Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan
Mondher Bouazizi: Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
Tomoaki Ohtsuki: Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
Yohei Shibata: SoftBank Corp. Technology Research Laboratory, Tokyo 135-0064, Japan
Wataru Takabatake: SoftBank Corp. Technology Research Laboratory, Tokyo 135-0064, Japan
Kenji Hoshino: SoftBank Corp. Technology Research Laboratory, Tokyo 135-0064, Japan
Atsushi Nagate: SoftBank Corp. Technology Research Laboratory, Tokyo 135-0064, Japan

Future Internet, 2023, vol. 15, issue 1, 1-19

Abstract: In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

Keywords: HAPS; antenna control; reinforcement learning; evolution algorithm (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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