Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163822
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dc.contributor.authorLi, Fengen_US
dc.contributor.authorShen, Bowenen_US
dc.contributor.authorGuo, Jialeen_US
dc.contributor.authorLam, Kwok-Yanen_US
dc.contributor.authorWei, Guiyien_US
dc.contributor.authorWang, Lien_US
dc.date.accessioned2022-12-19T05:22:52Z-
dc.date.available2022-12-19T05:22:52Z-
dc.date.issued2022-
dc.identifier.citationLi, F., Shen, B., Guo, J., Lam, K., Wei, G. & Wang, L. (2022). Dynamic spectrum access for Internet-of-Things based on federated deep reinforcement learning. IEEE Transactions On Vehicular Technology, 71(7), 7952-7956. https://dx.doi.org/10.1109/TVT.2022.3166535en_US
dc.identifier.issn0018-9545en_US
dc.identifier.urihttps://hdl.handle.net/10356/163822-
dc.description.abstractThe explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Vehicular Technologyen_US
dc.rights© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TVT.2022.3166535.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleDynamic spectrum access for Internet-of-Things based on federated deep reinforcement learningen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TVT.2022.3166535-
dc.description.versionSubmitted/Accepted versionen_US
dc.identifier.scopus2-s2.0-85128692697-
dc.identifier.issue7en_US
dc.identifier.volume71en_US
dc.identifier.spage7952en_US
dc.identifier.epage7956en_US
dc.subject.keywordsInternet of Thingsen_US
dc.subject.keywordsCollaborative Worken_US
dc.description.acknowledgementThis work was supported in part by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and in part by the Fundamental Research Funds for the Central Universities under Grant 3132021335.en_US
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