Anti-Jamming Routing For Internet Of Satellites: A Reinforcement Learning Approach
Chen Han, Aijun Liu, Liangyu Huo, Haichao Wang, Xiaohu Liang
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The anti-jamming routing for the Internet of Satellites (IoS) has drawn increasing attentions due to the unknown interrupts, unexpected congestion and smart jamming. This paper investigates anti-jamming routing scheme for heterogeneous IoS, with the aim of minimizing anti-jamming routing cost. Firstly, to tackle the smart jamming which can automatically change jamming strategies according to the jamming effect, we formulate the routing anti-jamming problem as a hierarchical anti-jamming Stackelberg game. Secondly, we propose a deep reinforcement learning based routing algorithm (DRLR) to obtain an available routing path subset. Furthermore, based on this set, a fast response anti-jamming algorithm (FRA) is proposed to achieve fast and reliable anti-jamming routing. Finally, the simulations have shown that the proposed algorithm have lower routing cost and better anti-jamming performance than existing approaches.