A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 10
Issue 7
IEEE/CAA Journal of Automatica Sinica
| Citation: | C. F. Wang, Z. C. Bi, and Y. P. Wan, “Secure underwater distributed antenna systems: A multi-agent reinforcement learning approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1622–1624, Jul. 2023. doi: 10.1109/JAS.2023.123366
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Z. Wang, S. Zhou, and Z. Wang, “Underwater distributed antenna systems: Design opportunities and challenges,” IEEE Commun. Mag., vol. 56, no. 10, pp. 178–185, 2018. doi: 10.1109/MCOM.2017.1601071
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M. Xu, Y. Fan, and L. Liu, “Multi-party secret key generation over underwater acoustic channels,” IEEE Wireless Commun. Lett., vol. 9, no. 7, pp. 1075–1079, 2020.
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Y. Huang, P. Xiao, S. Zhou, and Z. Shi, “A half-duplex self-protection jamming approach for improving secrecy of block transmissions in underwater acoustic channels,” IEEE Sensors J., vol. 16, no. 11, pp. 4100–4109, 2015.
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Z. Peng, X. Han, and Y. Ye, “Enhancing underwater sensor network security with coordinated communications,” in Proc. IEEE Int. Conf. Commun., 2021, pp. 1–6.
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C. Wang and Z. Wang, “Signal alignment for secure underwater coordinated multipoint transmissions,” IEEE Trans. Signal Process., vol. 64, no. 23, pp. 6360–6374, 2016. doi: 10.1109/TSP.2016.2601281
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Y. Yang, B. Li, S. Zhang, W. Zhao, and H. Zhang, “Cooperative proactive eavesdropping based on deep reinforcement learning,” IEEE Wireless Commun. Lett., vol. 10, no. 9, pp. 1857–1861, 2021. doi: 10.1109/LWC.2021.3084213
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Y. Zhang, Z. Mou, F. Gao, J. Jiang, R. Ding, and Z. Han, “UAV-enabled secure communications by multi-agent deep reinforcement learning,” IEEE Trans. Veh. Technol., vol. 69, no. 10, pp. 11599–11611, 2020. doi: 10.1109/TVT.2020.3014788
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H. Sharma, I. Budhiraja, N. Kumar, and R. K. Tekchandani, “Secrecy rate maximization for THz-enabled femto edge users using deep reinforcement learning in 6G,” in Proc. IEEE Int. Conf. Comput. Commun., 2022, pp. 1–6.
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J. Yan, Y. Meng, X. Yang, X. Luo, and X. Guan, “Privacy-preserving localization for underwater sensor networks via deep reinforcement learning,” IEEE Trans. Inf. Forensics Security, vol. 16, pp. 1880–1895, 2021. doi: 10.1109/TIFS.2020.3045320
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L. Xiao, D. Jiang, Y. Chen, W. Su, and Y. Tang, “Reinforcement learning-based relay mobility and power allocation for underwater sensor networks against jamming,” IEEE J. Ocean. Eng., vol. 45, no. 3, pp. 1148–1156, 2020. doi: 10.1109/JOE.2019.2910938
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R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch, “Multi-agent actor-critic for mixed cooperative-competitive environments,” in Proc. Conf. Neural Inf. Process. Syst., 2017, pp. 6382–6393.
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X. Ye, Y. Yu, and L. Fu, “Deep reinforcement learning based MAC protocol for underwater acoustic networks,” IEEE Trans. Mobile Comput., vol. 21. no. 5, pp. 1625–1638. 2022.
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