Authors
Shuqi Wang, Huizi Guo, Weijie Deng
Published in
Sensors (Basel, Switzerland). Volume 26. Issue 20. Oct 19, 2026. Epub Oct 19, 2026.
Abstract
In sixth-generation (6G) space-air-ground integrated networks, risk-neutral reinforcement-learning-based routing methods for low Earth orbit (LEO) satellite networks remain susceptible to severe tail latency. To address this issue, this paper proposes a novel Entropic Value-at-Risk (EVaR)-inspired routing algorithm with risk regularization for low-return actions. The routing process in an LEO satellite network is formulated as an event-driven partially observable Markov decision process. Within a multi-agent soft actor-critic framework, an EVaR-inspired state-level risk proxy for low-return actions is introduced and incorporated into the actor loss as a risk regularizer. This design enables the joint optimization of expected routing returns and the mitigation of low-return action risk. Simulation results obtained in a simplified LEO network using Iridium orbital parameters demonstrate that, compared with the multi-agent soft actor-critic algorithm, the proposed algorithm reduces the average end-to-end delay and the 99th-percentile end-to-end delay by 19.2% and 32.9%, respectively. It also reduces the average and maximum queueing delays by 79.4% and 57.0%, respectively, with approximately no packet loss observed during the simulations. These results demonstrate that the proposed algorithm enables efficient and reliable routing in LEO satellite networks under high-load conditions.
PMID:
42944490
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.
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