Iris: Toward Intelligent Reliable Routing for Software-Defined Satellite Networks
Wei, Wenting Fu, Liying Gu, Huaxi Lu, Xueyu Liu, Lei Mumtaz, Shahid Guizani, Mohsen
Wei, Wenting
Fu, Liying
Gu, Huaxi
Lu, Xueyu
Liu, Lei
Mumtaz, Shahid
Guizani, Mohsen
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Department
Machine Learning
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Journal article
Date
2025
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Language
English
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Abstract
Satellite networks have long been regarded as a vital component of space communication systems, which provide integrated satellite-terrestrial broadband access in seamless coverage and cost-effective manner. The inter-satellite routing design for low earth orbit (LEO) satellite constellations is critical for achieving low-latency and high-reliability communication in the space communication systems. However, the inherent dynamic nature of LEO satellites, coupled with the variability in inter-satellite connectivity, imposes significant challenges for routing efficiency and network dependability. Existing routing schemes cannot handle such topological fluctuations due to their insensitivity to real-time network changes, thus suffering from performance degradations in highly dynamic space environments. This paper presents Iris, an intelligent reliable routing scheme for inter-satellite communication, aiming at increasing efficiency and reliability of the packet transmission process. Specifically, we propose a comprehensive deep reinforcement learning (DRL) framework that learns a policy to select routing paths automatically under the emerging software-defined satellite networking (SDSN) architecture. To strengthen fault-tolerance in fluctuating environments, we train an agent in an incremental manner by gradually increasing scenario complexity. Simulation results indicate that our solution significantly outperforms baselines and exhibits advances in adaptability and reliability, especially under dynamic environments with frequent topology changes. © 1972-2012 IEEE.
Citation
W. Wei et al., “Iris: Towards Intelligent Reliable Routing for Software Defined Satellite Networks,” IEEE Transactions on Communications, 2024, doi: 10.1109/TCOMM.2024.3429166.
Source
IEEE Transactions on Communications
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Keywords
deep reinforcement learning, routing algorithm, Software-defined satellite networking
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Publisher
IEEE