Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143868
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dc.contributor.authorXiong, Zehuien_US
dc.contributor.authorZhang, Yangen_US
dc.contributor.authorNiyato, Dusiten_US
dc.contributor.authorDeng, Ruilongen_US
dc.contributor.authorWang, Pingen_US
dc.contributor.authorWang, Li-Chunen_US
dc.date.accessioned2020-09-28T06:07:21Z-
dc.date.available2020-09-28T06:07:21Z-
dc.date.issued2019-
dc.identifier.citationXiong, Z., Zhang, Y., Niyato, D., Deng, R., Wang, P., & Wang, L.-C. (2019). Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges. IEEE Vehicular Technology Magazine, 14(2), 44–52. doi:10.1109/mvt.2019.2903655en_US
dc.identifier.issn1556-6072en_US
dc.identifier.urihttps://hdl.handle.net/10356/143868-
dc.description.abstractFuture-generation wireless networks (5G and beyond) must accommodate surging growth in mobile data traffic and support an increasingly high density of mobile users involving a variety of services and applications. Meanwhile, the networks become increasingly dense, heterogeneous, decentralized, and ad hoc in nature, and they encompass numerous and diverse network entities. Consequently, different objectives, such as high throughput and low latency, need to be achieved in terms of service, and resource allocation must be designed and optimized accordingly. However, considering the dynamics and uncertainty that inherently exist in wireless network environments, conventional approaches for service and resource management that require complete and perfect knowledge of the systems are inefficient or even inapplicable. Inspired by the success of machine learning in solving complicated control and decision-making problems, in this article we focus on deep reinforcement- learning (DRL)-based approaches that allow network entities to learn and build knowledge about the networks and thus make optimal decisions locally and independently. We first overview fundamental concepts of DRL and then review related works that use DRL to address various issues in 5G networks. Finally, we present an application of DRL for 5G network slicing optimization. The numerical results demonstrate that the proposed approach achieves superior performance compared with baseline solutions.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Vehicular Technology Magazineen_US
dc.rights© 2019 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/MVT.2019.2903655.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDeep reinforcement learning for mobile 5g and beyond : fundamentals, applications, and challengesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.identifier.doi10.1109/MVT.2019.2903655-
dc.description.versionAccepted versionen_US
dc.identifier.issue2en_US
dc.identifier.volume14en_US
dc.identifier.spage44en_US
dc.identifier.epage52en_US
dc.subject.keywords5G Mobile Communicationen_US
dc.subject.keywordsDeep Reinforcement Learningen_US
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item.grantfulltextopen-
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