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Reinforcement Learning-Based Cloud-Aware HAPS Trajectory Optimization in Soft-Switching Hybrid FSO/RF Cooperative

Beibei Cui1,2, Shanyong Cai1,2, Liqian Wang1,2

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This study introduces a novel soft-switching method using rateless codes (RCs) and deep reinforcement learning (DRL) for hybrid free-space optical/radio-frequency (FSO/RF) communications. The RC-PPO approach enhances network throughput and trajectory smoothness for high-altitude platform stations (HAPS).

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deep reinforcement learning (DRL)hybrid FSO/RFproximal policy optimization (PPO)trajectory optimization

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Area of Science:

  • Satellite Communications
  • Optical Wireless Communications
  • Network Resilience

Background:

  • Free-space optical (FSO) links offer high capacity but suffer from cloud-induced availability issues.
  • Hybrid FSO/radio-frequency (RF) systems and trajectory optimization improve resilience but conventional hard-switching (HS) causes instability.
  • Existing methods struggle with frequent link transitions and unstable throughput in hybrid FSO/RF systems.

Purpose of the Study:

  • To develop a joint optimization framework for hybrid FSO/RF systems using soft-switching and deep reinforcement learning (DRL).
  • To enhance the stability and throughput of space-air-ground networks by optimizing high-altitude platform station (HAPS) trajectories.
  • To address challenges posed by sparse feedback from rateless codes (RCs) in DRL training.

Main Methods:

  • Proposed a joint optimization framework integrating soft-switching with RCs and DRL for HAPS trajectory optimization.
  • Developed a reward-shaped proximal policy optimization (PPO) agent to handle sparse RC feedback.
  • Utilized realistic ERA5 meteorological data for simulations to evaluate the proposed RC-PPO approach.

Main Results:

  • The RC-PPO method achieved higher throughput compared to the hard-switching proximal policy optimization (HS-PPO) baseline.
  • Simulations demonstrated smoother HAPS trajectories with the RC-PPO approach.
  • The proposed soft-switching mechanism enabled simultaneous transmission over FSO and RF links, improving data reception.

Conclusions:

  • The RC-PPO framework effectively enhances throughput and trajectory smoothness in hybrid FSO/RF space-air-ground systems.
  • Soft-switching with RCs and DRL-based trajectory optimization offers a more stable and efficient solution than traditional hard-switching.
  • This approach improves the overall resilience and performance of ubiquitous connectivity via HAPS.