Jamming-resilient algorithm for underwater cognitive acoustic networks

Zixiang Wang, Fan Zhen, Senlin Zhang, Meiqin Liu and Qunfei Zhang

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 8, 1550147717726309

Abstract: Due to the limit spectrum resource in the underwater acoustic networks, underwater cognitive acoustic communication is a promising technique. The channel sharing mechanism in cognitive networks can improve the communication capacity efficiently. Jamming attack is a common deny of service attack in cognitive networks. In the underwater cognitive acoustic networks, the anti-jamming problem is quite different from cognitive radio networks. It calls for an effective anti-jamming strategy in the cognitive acoustic channel access. In this article, we propose an online learning anti-jamming algorithm called multi-armed bandit–based acoustic channel access algorithm to achieve the jamming-resilient cognitive acoustic communication. The imperfect channel sensing and the constraints of underwater acoustic communication are considered in the anti-jamming game. Under different kinds of jamming attacks, the channel utilization can be improved with our jamming-resilient approach.

Keywords: Underwater cognitive acoustic network; anti-jamming; hidden Markov model; multi-armed bandit; channel access (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:8:p:1550147717726309

DOI: 10.1177/1550147717726309

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