Wireless Communications Interference Avoidance Based on Fast Reinforcement Learning

LI Fang; XIONG Jun; ZHAO Xiaodi; ZHAO Haitao; WEI Jibo; SU Man

doi:10.11999/JEIT210965

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3842-3849

LI Fang, XIONG Jun, ZHAO Xiaodi, ZHAO Haitao, WEI Jibo, SU Man. Wireless Communications Interference Avoidance Based on Fast Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3842-3849. doi: 10.11999/JEIT210965

Citation:

LI Fang, XIONG Jun, ZHAO Xiaodi, ZHAO Haitao, WEI Jibo, SU Man. Wireless Communications Interference Avoidance Based on Fast Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3842-3849. doi: 10.11999/JEIT210965

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Wireless Communications Interference Avoidance Based on Fast Reinforcement Learning

doi: 10.11999/JEIT210965

1.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
2.
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
3.
Beijing Institute of Tracking and Telecommunication Technology, Beijing 100049, China

Funds: The National Natural Science Foundation of China (U19B2024, 61601480 )

Received Date: 2021-09-10
Accepted Date: 2021-12-14
Rev Recd Date: 2021-12-12

Available Online: 2021-12-25

Publish Date: 2022-11-14

Abstract

Abstract

In this article, the unknown and dynamic interference in the wireless communication environment is studied. Jointly considering communication channel access and transmit power control, a fast reinforcement learning strategy is proposed to ensure reliable communication at the transceivers. The interference avoidance problem is firstly modeled as a Markov decision process to lower the transmission power of the system and reduce the number of channel switching while ensuring the communication quality. Subsequently, a Win or Learn Fast Policy Hill-Climbing (WoLF-PHC) learning method is proposed to avoid rapidly interference. Simulation results show that the anti-interference performance and convergence speed of the proposed WoLF-PHC algorithm are superior to the traditional random selection method and Q learning algorithm under different interference situations.
- Interference avoidance,
- Win or Learn Fast Policy Hill-Climbing (WoLF-PHC),
- Q learning,
- Markov decision process

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References(17)

References

[1]	ZOU Yulong, ZHU Jia, WANG Xianbin, et al. A survey on wireless security: Technical challenges, recent advances, and future trends[J]. Proceedings of the IEEE, 2016, 104(9): 1727–1765. doi: 10.1109/JPROC.2016.2558521
[2]	GROVER K, LIM A, and YANG Qing. Jamming and anti-jamming techniques in wireless networks: A survey[J]. International Journal of Ad Hoc and Ubiquitous Computing, 2014, 17(4): 197–215. doi: 10.1504/IJAHUC.2014.066419
[3]	LI Fang, XIONG Jun, ZHAO Xiaodi, et al. An improved FCME algorithm based on differential spectrum envelope for interference detection in satellite communication systems[C]. The 5th International Conference on Computer and Communication Systems (ICCCS), Shanghai, China, 2020: 874–879.
[4]	SUTTON R S and BARTO A G. Reinforcement Learning: An Introduction[M]. Cambridge: MIT Press, 1998: 216–224.
[5]	KONG Lijun, XU Yuhua, ZHANG Yuli, et al. A reinforcement learning approach for dynamic spectrum anti-jamming in fading environment[C]. The IEEE 18th International Conference on Communication Technology (ICCT), Chongqing, China, 2018: 51–58.
[6]	SLIMENI F, CHTOUROU Z, SCHEERS B, et al. Cooperative Q-learning based channel selection for cognitive radio networks[J]. Wireless Networks, 2019, 25(7): 4161–4171. doi: 10.1007/s11276-018-1737-9
[7]	WANG Beibei, WU Yongle, LIU K J R, et al. An anti-jamming stochastic game for cognitive radio networks[J]. IEEE Journal on Selected Areas in Communications, 2011, 29(4): 877–889. doi: 10.1109/JSAC.2011.110418
[8]	GWON Y, DASTANGOO S, FOSSA C, et al. Competing mobile network game: Embracing antijamming and jamming strategies with reinforcement learning[C]. 2013 IEEE Conference on Communications and Network Security (CNS), National Harbor, USA, 2013: 28–36.
[9]	SLIMENI F, SCHEERS B, CHTOUROU Z, et al. Jamming mitigation in cognitive radio networks using a modified Q-learning algorithm[C]. 2015 IEEE International Conference on Military Communications and Information Systems (ICMCIS), Cracow, Poland, 2015: 1–7.
[10]	MPITZIOPOULOS A, GAVALAS D, KONSTANTOPOULOS C, et al. A survey on jamming attacks and countermeasures in WSNs[J]. IEEE Communications Surveys & Tutorials, 2009, 11(4): 42–56. doi: 10.1109/SURV.2009.090404
[11]	ZHANG Yuli, XU Yuhua, XU Yitao, et al. A multi-leader one-follower stackelberg game approach for cooperative anti-jamming: No Pains, No Gains[J]. IEEE Communications Letters, 2018, 22(8): 1680–1683. doi: 10.1109/LCOMM.2018.2843374
[12]	HANAWAL M K, ABDEL-RAHMAN M J, and KRUNZ M. Joint adaptation of frequency hopping and transmission rate for anti-jamming wireless systems[J]. IEEE Transactions on Mobile Computing, 2016, 15(9): 2247–2259. doi: 10.1109/TMC.2015.2492556
[13]	HANAWAL M K, ABDEL-RAHMAN M J, and KRUNZ M. Game theoretic anti-jamming dynamic frequency hopping and rate adaptation in wireless systems[C]. The 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Hammamet, Tunisia, 2014: 247–254.
[14]	LI Yangyang, XU Yitao, WANG Ximing, et al. Power and frequency selection optimization in anti-jamming communication: A deep reinforcement learning approach[C]. The IEEE 5th International Conference on Computer and Communications (ICCC), Chengdu, China, 2019: 815–820.
[15]	XIAO Liang, JIANG Donghua, XU Dongjin, et al. Two-dimensional antijamming mobile communication based on reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018, 67(10): 9499–9512. doi: 10.1109/TVT.2018.2856854
[16]	PEI Xufang, WANG Ximing, YAO Junnan, et al. Joint time-frequency anti-jamming communications: A reinforcement learning approach[C]. The 11th International Conference on Wireless Communications and Signal Processing (WCSP), Xi'an, China, 2019: 1–6.
[17]	BOWLING M and VELOSO M. Multiagent learning using a variable learning rate[J]. Artificial Intelligence, 2002, 136(2): 215–250. doi: 10.1016/S0004-3702(02)00121-2