Reliable Low-Latency Routing for VLEO Satellite Optical Network: A Multi-agent Reinforcement Learning Approach

For rapid on-orbit forwarding of high-resolution remote sensing images, the combination of very low Earth orbit (VLEO) and optical inter-satellite links (OISL) has recently emerged as a focus of non-terrestrial networks (NTNs). However, at the height of VLEO, not only does the attenuation factor of the optical signal-to-noise ratio grow exponentially, but the service with more than ten times bandwidth expansion, which adds a significant burden to the buffer queue. To address the above reliability and latency challenges, this paper proposes a routing framework based on the characteristics of the VLEO scenario. Specifically, the OISL path in VLEO is modelled first. In order to reduce the severe impact of multi-hop on the routing performance, we perform dynamic group scheduling for OISL. Additionally, taking advantage of the fact that remote sensing missions can be scheduled in advance, we adopt a “route first, then establish links” approach to specifically plan service paths. Considering the coordination of a single service and overall network performance, this problem is solved using a Q-value decomposed multi-agent reinforcement learning method. Simulation results demonstrate that our scheme maintains excellent reliability and latency performance under VLEO scenarios with varying heights, network scales, and traffic loads.