Anti-Jamming Routing For Internet of Satellites: a Reinforcement Learning Approach
Chen Han, Aijun Liu, Liangyu Huo, Haichao Wang, Xiaohu Liang
Abstract
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 antijamming routing. Finally, the simulations have shown that the proposed algorithm have lower routing cost and better antijamming performance than existing approaches.
BibTeX
@inproceedings{icassp2020_antijammingrouti,
title = {Anti-Jamming Routing For Internet of Satellites: a Reinforcement Learning Approach},
author = {Chen Han and Aijun Liu and Liangyu Huo and Haichao Wang and Xiaohu Liang},
booktitle = {ICASSP 2020},
year = {2020}
}