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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| Vydáno v: | IEEE transactions on vehicular technology Ročník 68; číslo 4; s. 3163 - 3173 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0018-9545, 1939-9359 |
| On-line přístup: | Získat plný 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. |
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| 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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| CODEN | ITVTAB |
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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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