@inproceedings{2adc676e81274db5916ef529113d91aa,
title = "Deep Reinforcement Learning Based Anti-Jamming Using Clear Channel Assessment Information in a Cognitive Radio Environment",
abstract = "Jamming as a type of denial of service attack has proved to be destructive to communication systems. This paper investigates and implements an anti-jamming scheme in a dynamic jamming environment. In our study, we utilize the clear channel assessment (CCA) information available in the MAC layer of a standard IEEE wireless device. Consequently, we eliminate the need for additional equipment to obtain the raw spectrum information. This contrast existing works which need a priori knowledge of the jamming patterns or employ raw spectrum information. The CCA information of all available spectrum channels is utilized as input states to train a double deep q-network (DDQN) agent online to mitigate the effects of jamming. Numerical results show that the proposed anti-jamming approach is effective in different jamming scenarios.",
keywords = "anti-jamming, cognitive radio, deep reinforcement learning, Jamming",
author = "Ali, {Abubakar S.} and Lunardi, {Willian T.} and Lina Bariah and Michael Baddeley and Lopez, {Martin Andreoni} and Giacalone, {Jean Pierre} and Sami Muhaidat",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th International Conference on Advanced Communication Technologies and Networking, CommNet 2022 ; Conference date: 12-12-2022 Through 14-12-2022",
year = "2022",
doi = "10.1109/CommNet56067.2022.9993858",
language = "British English",
series = "Proceedings - 2022 5th International Conference on Advanced Communication Technologies and Networking, CommNet 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "{El Bouanani}, Faissal and Fouad Ayoub",
booktitle = "Proceedings - 2022 5th International Conference on Advanced Communication Technologies and Networking, CommNet 2022",
address = "United States",
}