Two-dimensional anti-jamming communication based on deep reinforcement learning

Guoan Han, Liang Xiao, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Scopus citations

Abstract

In this paper, a two-dimensional anti-jamming communication scheme for cognitive radio networks is developed, in which a secondary user (SU) exploits both spread spectrum and user mobility to address jamming attacks, while not interfering with primary users. By applying a deep Q-network algorithm, this scheme determines whether to recommend that the SU leave an area of heavy jamming and chooses a frequency hopping pattern to defeat smart jammers. Without knowing the jamming model and the radio channel model, the SU derives an optimal anti-jamming communication policy using Q-learning in a proposed dynamic game, and applies a deep convolution neural network to accelerate the learning speed with a large number of frequency channels. The proposed scheme can increase the signal-to-interference-plus-noise ratio and improve the utility of the SU against cooperative jamming, compared with a Q-learning-only based benchmark system.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2087-2091
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Cognitive radio networks
  • deep Q-networks
  • deep reinforcement learning
  • game theory
  • jamming

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