QoS- and Physics-Aware Routing in Optical LEO Satellite Networks via Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based routing framework for optical LEO satellite networks that considers physical and QoS constraints, improving path stability and latency in dynamic, jitter-affected environments.

Contribution

It proposes a novel, lightweight DRL routing method that incorporates geometry and QoS constraints directly into the decision process for optical satellite networks.

Findings

Paths are physically consistent and stable.

The method adapts to jitter and dynamic topology changes.

Robust end-to-end latency is maintained.

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.