PPO-Based Dynamic Positioning of HAPS-BS in Wind-Disturbed Stratospheric Maritime Networks

TL;DR

This paper introduces a DRL-based framework using PPO for dynamic HAPS positioning in maritime networks, addressing wind disturbances to improve coverage and throughput.

Contribution

It develops a centralized DRL approach with PPO to optimize HAPS positioning under wind effects, a novel application in maritime wireless networks.

Findings

The proposed method improves coverage stability under wind disturbances.

Simulation results demonstrate enhanced system throughput.

The framework effectively mitigates wind-induced deviations.

Abstract

High-Altitude Platform Stations (HAPS) offer a promising solution for wide-area wireless coverage in maritime regions lacking terrestrial infrastructure. However, maintaining reliable performance is challenging due to dynamic ship mobility and atmospheric disturbances, particularly stratospheric wind effects on HAPS positioning. This paper proposes a deep reinforcement learning (DRL)-based framework for dynamic positioning of wind-disturbed HAPS-mounted base stations in maritime networks. A centralized DRL agent deployed on a coordinator HAPS controls multiple serving HAPS using radio measurements and network feedback, capturing realistic channel conditions and user mobility. A Proximal Policy Optimization (PPO) algorithm is employed to learn robust positioning policies that enhance coverage stability and system throughput under wind disturbances. Simulation results show that the proposed approach effectively mitigates wind-induced positioning deviations while ensuring reliable wide-area connectivity for maritime users.