Jamming-resilient algorithm for underwater cognitive acoustic networks

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalInternational Journal of Distributed Sensor Networks
Volume13
Issue number8
DOIs
StatePublished - 1 Aug 2017

Keywords

  • anti-jamming
  • channel access
  • hidden Markov model
  • multi-armed bandit
  • Underwater cognitive acoustic network

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