Anti-Jamming Routing For Internet of Satellites: a Reinforcement Learning Approach

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.

[1]  Ramón Martínez Rodríguez-Osorio,et al.  Survey of Inter-Satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View , 2016, IEEE Communications Surveys & Tutorials.

[2]  K. J. Ray Liu,et al.  An anti-jamming stochastic game for cognitive radio networks , 2011, IEEE Journal on Selected Areas in Communications.

[3]  Adriano Camps,et al.  Benefits of Using Mobile Ad-Hoc Network Protocols in Federated Satellite Systems for Polar Satellite Missions , 2018, IEEE Access.

[4]  Nei Kato,et al.  State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems , 2017, IEEE Communications Surveys & Tutorials.

[5]  Zhu Han,et al.  Game Theory in Wireless and Communication Networks: Theory, Models, and Applications , 2011 .

[6]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[7]  Nei Kato,et al.  The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective , 2017, IEEE Wireless Communications.

[8]  Yuanqing Xia,et al.  Security Research on Wireless Networked Control Systems Subject to Jamming Attacks , 2019, IEEE Transactions on Cybernetics.

[9]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[10]  Chen Han,et al.  Cross-Layer Anti-Jamming Scheme: A Hierarchical Learning Approach , 2018, IEEE Access.

[11]  Siliang Wu,et al.  GNSS Jamming Mitigation Using Adaptive-Partitioned Subspace Projection Technique , 2019, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Lajos Hanzo,et al.  A Survey on Wireless Security: Technical Challenges, Recent Advances, and Future Trends , 2015, Proceedings of the IEEE.

[13]  Nei Kato,et al.  Space-Air-Ground Integrated Network: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[14]  Alec Radford,et al.  Proximal Policy Optimization Algorithms , 2017, ArXiv.