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AuthorSu, Yuhan
AuthorLiwang, Minghui
AuthorGao, Zhibin
AuthorHuang, Lianfen
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
Available date2022-11-09T12:35:25Z
Publication Date2021-01-15
Publication NameIEEE Internet of Things Journal
Identifierhttp://dx.doi.org/10.1109/JIOT.2020.3008178
CitationSu, Y., Liwang, M., Gao, Z., Huang, L., Du, X., & Guizani, M. (2020). Optimal cooperative relaying and power control for IoUT networks with reinforcement learning. IEEE Internet of Things Journal, 8(2), 791-801.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099160070&origin=inward
URIhttp://hdl.handle.net/10576/35982
AbstractInternet of Underwater Things (IoUT) consists of numerous sensor nodes distributed in an underwater area for sensing, collecting, processing information, and sending related messages to the data processing center. However, the characteristics of the underwater environment will bring strict limitations on communication coverage and power scarcity to IoUT networks. Applying cooperative communications to IoUT networks can expand the communication range and alleviate power shortages. In this article, we investigate the cooperative communication problem in a power-limited cooperative IoUT system and propose a reinforcement learning-based underwater relay selection strategy. Specifically, we first determine the optimal transmit powers of the source node and the selected underwater relay to maximize the end-to-end signal-to-noise ratio of the system. Then, we formulate the underwater cooperative relaying process as a Markov process and apply reinforcement learning to obtain an effective underwater relay selection strategy. The simulation results show that the performance of the proposed scheme outperforms that of the equal transmit power settings under the same conditions. In addition, the proposed deep Q-network-based underwater relay selection strategy improves the communication efficiency compared with the Q-learning-based strategy, and the number of iterations needed for convergence can be effectively reduced.
SponsorThis work was supported in part by the National Natural Science Foundation of China under Grant 61871339, Grant 61971365, and Grant 61901403; in part by the Natural Science Foundation of Fujian Province under Grant 2019J05001 and Grant 2019J01046; and in part by the Key Laboratory of Digital Fujian on IoT Communication, Architecture and Safety Technology under Grant 2010499.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCooperative communications
Internet of Underwater Things (IoUT)
reinforcement learning
relay selection%%
TitleOptimal Cooperative Relaying and Power Control for IoUT Networks with Reinforcement Learning
TypeArticle
Issue Number2
Volume Number8
dc.accessType Abstract Only


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