A topology design method for satellite networks based on deep reinforcement learning

Yuning Zheng; Yifeng Lyu; Ying Wang; Xiufeng Sui; Liyue Zhu; Shubin Xu

doi:10.1117/12.2682444

20 June 2023 A topology design method for satellite networks based on deep reinforcement learning

Yuning Zheng, Yifeng Lyu, Ying Wang, Xiufeng Sui, Liyue Zhu, Shubin Xu

Proceedings Volume 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023); 127151A (2023) https://doi.org/10.1117/12.2682444
Event: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 2023, Dalian, China

Abstract

Recently, Low Earth Orbit (LEO) satellite constellations with low-latency and high-bandwidth attract extensive research. However, most available studies focused on the field of satellite network routing algorithms, ignoring the impact of topology on the efficiency of inter-satellite networking and the quality of inter-satellite communication. In this paper, we propose a satellite network topology design method based on deep reinforcement learning (DRL), with the goal of reducing the latency of the entire satellite network. To achieve this goal, we first model the satellite network communication scene and formulate the topology optimization problem as a Markov decision process (MDP). Then, we further propose the idea of backbone-point satellites and use DRL to optimize the topology structure. Finally, we conduct extensive experiments on different performances of satellite topology, and we conclude that the network topology constructed in this way can provide lower latency communications than the motif and +Grid topologies, optimized by 8.48% and 42.86% respectively.

Citation Download Citation

Yuning Zheng, Yifeng Lyu, Ying Wang, Xiufeng Sui, Liyue Zhu, and Shubin Xu "A topology design method for satellite networks based on deep reinforcement learning", Proc. SPIE 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 127151A (20 June 2023); https://doi.org/10.1117/12.2682444

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KEYWORDS

Satellites

Satellite communications

Design and modelling

Network architectures

Connectors

Education and training

Performance modeling

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