Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143868
Title: Deep reinforcement learning for mobile 5g and beyond : fundamentals, applications, and challenges
Authors: Xiong, Zehui
Zhang, Yang
Niyato, Dusit
Deng, Ruilong
Wang, Ping
Wang, Li-Chun
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Xiong, 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.2019.2903655
Journal: IEEE Vehicular Technology Magazine 
Abstract: Future-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.
URI: https://hdl.handle.net/10356/143868
ISSN: 1556-6072
DOI: 10.1109/MVT.2019.2903655
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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