Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong, NC; Hoang, DT; Gong, S; Niyato, D; Wang, P; Liang, YC; Kim, DI

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong, NC Hoang, DT

Gong, S Niyato, D Wang, P Liang, YC Kim, DI

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Publication Type:: Journal Article
Citation:: IEEE Communications Surveys and Tutorials, 2019, 21 (4), pp. 3133 - 3174
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Luong, NC	en_US
dc.contributor.author	Hoang, DT https://orcid.org/0000-0002-9528-0863	en_US
dc.contributor.author	Gong, S	en_US
dc.contributor.author	Niyato, D	en_US
dc.contributor.author	Wang, P	en_US
dc.contributor.author	Liang, YC	en_US
dc.contributor.author	Kim, DI	en_US
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	IEEE Communications Surveys and Tutorials, 2019, 21 (4), pp. 3133 - 3174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138762
dc.description.abstract	© 1998-2012 IEEE. This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.	en_US
dc.relation.ispartof	IEEE Communications Surveys and Tutorials	en_US
dc.relation.isbasedon	10.1109/COMST.2019.2916583	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Applications of Deep Reinforcement Learning in Communications and Networking: A Survey	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	21	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1005 Communications Technologies	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-10-31T00:00:00+1100
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

© 1998-2012 IEEE. This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

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http://hdl.handle.net/10453/138762