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

For rapid on-orbit forwarding of high-resolution remote sensing (RS) images, the combination of very low-Earth orbit (VLEO) and optical intersatellite links (OISLs) has recently emerged as a focus of nonterrestrial 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 article 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 multihop on the routing performance, we perform dynamic group scheduling for OISL. Additionally, taking advantage of the fact that RS 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 multiagent 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.