Reinforcement learning based dynamic distributed routing scheme for mega LEO satellite networks |
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| Institution: | 1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China;2. School of Astronautics, Beihang University, Beijing 100191, China;3. School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Camperdown 2006, Australia;4. X-Laboratory, The Second Academy of China Aerospace Science and Industry Corporation, Beijing 100854, China |
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| Abstract: | Recently, mega Low Earth Orbit (LEO) Satellite Network (LSN) systems have gained more and more attention due to low latency, broadband communications and global coverage for ground users. One of the primary challenges for LSN systems with inter-satellite links is the routing strategy calculation and maintenance, due to LSN constellation scale and dynamic network topology feature. In order to seek an efficient routing strategy, a Q-learning-based dynamic distributed Routing scheme for LSNs (QRLSN) is proposed in this paper. To achieve low end-to-end delay and low network traffic overhead load in LSNs, QRLSN adopts a multi-objective optimization method to find the optimal next hop for forwarding data packets. Experimental results demonstrate that the proposed scheme can effectively discover the initial routing strategy and provide long-term Quality of Service (QoS) optimization during the routing maintenance process. In addition, comparison results demonstrate that QRLSN is superior to the virtual-topology-based shortest path routing algorithm. |
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| Keywords: | LEO satellite networks Mega constellation Multi-objective optimization Routing algorithm Reinforcement learning |
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