Ambient Backscatter: A Novel Method to Defend Jamming Attacks for Wireless Networks

Van Huynh, Nguyen; Nguyen, Diep N.; Thai Hoang, Dinh; Dutkiewicz, Eryk; Mueck, Markus

Abstract

This letter introduces a novel idea to defend jamming attacks for wireless communications. In particular, when the jammer attacks the channel, the transmitter can leverage the jamming signals to transmit data by using ambient backscatter technique or harvest energy from the jamming signals to support its operation. To deal with the uncertainty of the jammer, we propose a reinforcement learning-based algorithm that allows the transmitter to obtain the optimal operation policy through real-time interaction processes with the attacker. The simulation results show the effectiveness of ambient backscatter in combating jammers, i.e., it enables the transmitter to transmit data even under the jamming attacks. We observe that the more power the jammer uses to attack the channel, the better performance the network can achieve.

Citation

Van Huynh, N., Nguyen, D. N., Thai Hoang, D., Dutkiewicz, E., & Mueck, M. (2020). Ambient Backscatter: A Novel Method to Defend Jamming Attacks for Wireless Networks. IEEE Wireless Communications Letters, 9(2), 175-178. https://doi.org/10.1109/lwc.2019.2947417

• Journal Article Type: Article
• Online Publication Date: Oct 16, 2019
• Publication Date: 2020-02
• Deposit Date: Mar 29, 2023
• Journal: IEEE Wireless Communications Letters
• Print ISSN: 2162-2337
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 2
• Pages: 175-178
• DOI: https://doi.org/10.1109/lwc.2019.2947417
• Keywords: Anti-jamming, ambient backscatter, RF energy harvesting, reinforcement learning, Q-learning, MDP