{"ID":2826017,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20835","arxiv_id":"2512.20835","title":"QoS- and Physics-Aware Routing in Optical LEO Satellite Networks via Deep Reinforcement Learning","abstract":"Optical inter-satellite links (ISLs) are becoming the principal communication backbone in modern large-scale LEO constellations, offering multi-Gb/s capacity and near speed-of-light latency. However, the extreme sensitivity of optical beams to relative satellite motion, pointing jitter, and rapidly evolving geometry makes routing fundamentally more challenging than in RF-based systems. In particular, intra-plane and inter-plane ISLs exhibit markedly different stability and feasible range profiles, producing a dynamic, partially constrained connectivity structure that must be respected by any physically consistent routing strategy. This paper presents a lightweight geometry- and QoS-aware routing framework for optical LEO networks that incorporates class-dependent feasibility constraints derived from a jitter-aware Gaussian-beam model. These analytically computed thresholds are embedded directly into the time-varying ISL graph and enforced via feasible-action masking in a deep reinforcement learning (DRL) agent. The proposed method leverages local geometric progress, feasible-neighbor structure, and congestion indicators to select next-hop relays without requiring global recomputation. Simulation results on a Starlink-like constellation show that the learned paths are physically consistent, exploit intra-plane stability, adapt to jitter-limited inter-plane connectivity, and maintain robust end-to-end latency under dynamic topology evolution.","short_abstract":"Optical inter-satellite links (ISLs) are becoming the principal communication backbone in modern large-scale LEO constellations, offering multi-Gb/s capacity and near speed-of-light latency. However, the extreme sensitivity of optical beams to relative satellite motion, pointing jitter, and rapidly evolving geometry ma...","url_abs":"https://arxiv.org/abs/2512.20835","url_pdf":"https://arxiv.org/pdf/2512.20835v1","authors":"[\"Mohammad Taghi Dabiri\",\"Rula Ammuri\",\"Mazen Hasna\",\"Khalid Qaraqe\"]","published":"2025-12-23T23:22:38Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}