An Intelligent Access Channel Algorithm Based on Distributed Double Q Learning
Source abstract
Abstract
Aiming at the problem of mutual interference between nodes and external malicious jamming in wireless communication networks, this paper proposes an intelligent communication anti-jamming algorithm based on distributed double Q-learning. First of all, the proposed algorithm anti-jamming problem is modeled as a time-frequency two-dimensional optimization problem. The decoupled relationship between subframe and channel is used. Each node uses two Q-learning. According to the automatically received feedback signal, users choose their subframe and channel to avoid malicious external jamming and mutual interference between users. It maximizes the sum of all user throughput. Simulation results show that the proposed algorithm can effectively shorten the convergence time and improve the performance of the system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Indoor and Outdoor Localization Technologies · Security in Wireless Sensor Networks · Cognitive Radio Networks and Spectrum Sensing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Sixty-Third Research Institute, National University of Defense Technology, Nanjing 210007, China
Funding
National Science Foundation of China
References (26)
Show source-provided references
1. 6G Wireless Communications Networks: A Comprehensive Survey10.1109/ACCESS.2021.3124812 ↗
2. Advanced space division multiplexing technologies for optical networks [Invited]10.1364/JOCN.9.0000C1 ↗
3. Maximizing Energy Efficiency with Channel Uncertainty under Mutual Interference10.1109/TWC.2022.3166445 ↗
4. Lin, J., Tian, B., Wu, J., and He, J. (2020, January 27–29). Spectrum Resource Trading and Radio Management Data Sharing Based on Blockchain. Proceedings of the 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China.10.1109/ICISCAE51034.2020.9236794 ↗
5. A Survey on Wireless Security: Technical Challenges, Recent Advances, and Future Trends10.1109/JPROC.2016.2558521 ↗
6. An Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks to Maximize Their Lifespan in an Operational Environment10.1109/ACCESS.2020.3020310 ↗
7. Optimization of Network Throughput of Joint Radar Communication System Using Stochastic Geometry10.3389/frsip.2022.835743 ↗
8. AIRIS:Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications10.23919/JCC.2021.07.013 ↗
9. Noels, N., and Moeneclaey, M. (2017, January 21–25). Performance of advanced telecommand frame synchronizer under pulsed jamming conditions. Proceedings of the IEEE International Conference on Communications (ICC), Paris, France.10.1109/ICC.2017.7996555 ↗
10. Hall, M., Silvennoinen, A., and Haggman, S. (2005, January 17–20). Effect of pulse jamming on IEEE 802.11 wireless LAN performance. Proceedings of the MILCOM 2005—2005 IEEE Military Communications Conference, Atlantic City, NJ, USA.
11. Artificial Intelligence for Cognitive Wireless Communications (Editorial)10.1109/MWC.2019.8752476 ↗
12. Pareto-Optimal Wireless Networks10.1109/COMST.2020.2965856 ↗
13. Zhou, Z. (2008, January 10–12). Machine Learning. Proceedings of the ACM SIGMOD International Conference on Management of Data, Vancouver, BC, Canada.
14. Sutton, R.S., and Barto, A.G. (2019). Reinforcement Learning, Electronic Industry Press.
15. Distributed Q-Learning for Aggregated Interference Control in Cognitive Radio Networks10.1109/TVT.2010.2043124 ↗
16. Collaborative Distributed Q-Learning for RACH Congestion Minimization in Cellular IoT Networks10.1109/LCOMM.2019.2896929 ↗
17. Aref, M.A., and Jayaweera, S.K. (2017, January 27–28). A novel cognitive anti-jamming stochastic game. Proceedings of the Cognitive Communications for Aerospace Applications Workshop (CCAA), Cleveland, OH, USA.10.1109/CCAAW.2017.8001605 ↗
18. Zhou, Q., Li, Y., Niu, Y., Qin, Z., Zhao, L., and Wang, J. (2020, January 11–14). “One Plus One Is Greater Than Two”: Defeating Intelligent Dynamic Jamming with Collaborative Multi-Agent Reinforcement Learning. Proceedings of the International Conference on Computer and Communications (ICCC), Chengdu, China.10.1109/ICCC51575.2020.9345127 ↗
19. Value-function reinforcement learning in Markov games10.1016/S1389-0417(01)00015-8 ↗
20. Aref, M.A., Jayaweera, S.K., and Machuzak, S. (2017, January 19–22). Multi-agent reinforcement learning based cognitive anti-jamming. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA.10.1109/WCNC.2017.7925694 ↗
21. Machuzak, S., and Jayaweera, S.K. (2016, January 27–29). Reinforcement learning based anti-jamming with wideband autonomous cognitive radios. Proceedings of the IEEE International Conference on Communications in China (ICCC), Chengdu, China.10.1109/ICCChina.2016.7636793 ↗
22. An SCMA-Based Decoupled Distributed Q-Learning Random Access Scheme for Machine-Type Communication10.1109/LWC.2021.3078476 ↗
23. Intelligent Anti-Jamming Communication for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach
24. Wang, J. (2018). Optimization Theory and Methods, Beijing University of Technology Press.
25. Vlassis, N. (2007). A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence, Morgan and Claypool Publishers.10.1007/978-3-031-01543-4 ↗
26. Zhong, A. (2021). Preamble Design and Collision Resolution in a Massive Access IoT System. Sensors, 21.10.3390/s21010250 ↗