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

Authors :
Siyuan Yang
Mondher Bouazizi
Tomoaki Ohtsuki
Yohei Shibata
Wataru Takabatake
Kenji Hoshino
Atsushi Nagate
Source :
Future Internet, Vol 15, Iss 1, p 34 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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.

Details

Language :
English
ISSN :
19995903
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Future Internet
Publication Type :
Academic Journal
Accession number :
edsdoj.655c987d84b4879bc428929d38e46e8
Document Type :
article
Full Text :
https://doi.org/10.3390/fi15010034