Deep Reinforcement Learning Based Resource Allocation for V2V Communications

In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous &q...

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Bibliographic Details
Published in:	IEEE transactions on vehicular technology Vol. 68; no. 4; pp. 3163 - 3173
Main Authors:	Ye, Hao, Li, Geoffrey Ye, Juang, Biing-Hwang Fred
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Deep learning Deep Reinforcement Learning Interference Optimization Reinforcement learning Reliability Resource Allocation Resource management Unicast V2V Communication Vehicle-to-infrastructure Vehicular ad hoc networks
ISSN:	0018-9545, 1939-9359
Online Access:	Get full text
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Abstract	In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous "agent," a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure communications.
AbstractList	In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous “agent,” a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure communications.
Author	Juang, Biing-Hwang Fred Ye, Hao Li, Geoffrey Ye
Author_xml	– sequence: 1 givenname: Hao orcidid: 0000-0003-1544-1768 surname: Ye fullname: Ye, Hao email: yehao@gatech.edu organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 2 givenname: Geoffrey Ye surname: Li fullname: Li, Geoffrey Ye email: liye@ece.gatech.edu organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 3 givenname: Biing-Hwang Fred surname: Juang fullname: Juang, Biing-Hwang Fred email: juang@ece.gatech.edu organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Snippet	In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning,...
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SubjectTerms	Deep learning Deep Reinforcement Learning Interference Optimization Reinforcement learning Reliability Resource Allocation Resource management Unicast V2V Communication Vehicle-to-infrastructure Vehicular ad hoc networks
Title	Deep Reinforcement Learning Based Resource Allocation for V2V Communications
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