mlsanityData to 14 Oct 2026

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

arXiv 2605.05240 ↗First posted 3 May 2026eess.SP cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 3 May 2026.

Builds on

4 of 24 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2025Optimizing UAV Aerial Base Station Flights Using DRL-based Proximal Policy Optimizationcited by 3
  3. 2022AI-Aided Integrated Terrestrial and Non-Terrestrial 6G Solutions for Sustainable Maritime Networkingcited by 1
  4. 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networkscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Centralized PPO-Based DRL for Multi-UAV-BS Positioning and Trajectory Optimization in Disaster Response Networkscited by 0

Similar, not cited

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  1. 2025AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
  2. 2026Wind-Resilient Trajectory Optimization for UAV-BS Networks: TD3 for Continuous Service Availability ↗
  3. 2026Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids
  4. 2025Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations ↗
  5. 2026Deep Reinforcement Learning for Interference Suppression in RIS-Aided Space-Air-Ground Integrated Networks
  6. 2025Distributed Area Coverage with High Altitude Balloons Using Multi-Agent Reinforcement Learning
  7. 2026Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
  8. 2025Seasonal Station-Keeping of Short Duration High Altitude Balloons using Deep Reinforcement Learning