Two-dimensional antijamming mobile communication based on reinforcement learning

Liang Xiao, Donghua Jiang, Dongjin Xu, Hongzi Zhu, Yanyong Zhang, H. Vincent Poor

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

By using smart radio devices, a jammer can dynamically change its jamming policy based on opposing security mechanisms; it can even induce the mobile device to enter a specific communication mode and then launch the jamming policy accordingly. On the other hand, mobile devices can exploit spread spectrum and user mobility to address both jamming and interference. In this paper, a two-dimensional (2-D) antijamming mobile communication scheme is proposed in which a mobile device leaves a heavily jammed/interfered-with frequency or area. It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework. More specifically, a hotbooting deep Q-network based 2-D mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate learning in dynamic situations. Several real-world scenarios are simulated to evaluate the proposed method. These simulation results show that our proposed scheme can improve both the signal-to-interference-plus-noise ratio of the signals and the utility of the mobile devices against cooperative jamming compared with benchmark schemes.

Original languageEnglish (US)
Article number8412128
Pages (from-to)9499-9512
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Volume67
Issue number10
DOIs
StatePublished - Oct 2018

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Mobile devices
  • deep Q-network
  • game theory
  • jamming
  • reinforcement learning

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