Deep Reinforcement Learning Based Anti-Jamming Using Clear Channel Assessment Information in a Cognitive Radio Environment

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...

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Bibliographic Details
Published in2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet) pp. 1 - 6
Main Authors Ali, Abubakar S., Lunardi, Willian T., Bariah, Lina, Baddeley, Michael, Lopez, Martin Andreoni, Giacalone, Jean-Pierre, Muhaidat, Sami
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.12.2022
Subjects
anti-jamming
cognitive radio
Computer architecture
Decision making
Deep learning
deep reinforcement learning
Jamming
Neural networks
Reinforcement learning
Simulation
Training
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Summary: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.
ISSN:2771-7402
DOI:10.1109/CommNet56067.2022.9993858