TY - GEN
T1 - Two-dimensional anti-jamming communication based on deep reinforcement learning
AU - Han, Guoan
AU - Xiao, Liang
AU - Poor, H. Vincent
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - 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.
AB - 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.
KW - Cognitive radio networks
KW - deep Q-networks
KW - deep reinforcement learning
KW - game theory
KW - jamming
UR - http://www.scopus.com/inward/record.url?scp=85023743877&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023743877&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952524
DO - 10.1109/ICASSP.2017.7952524
M3 - Conference contribution
AN - SCOPUS:85023743877
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2087
EP - 2091
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -