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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Published in | IEEE transactions on vehicular technology Vol. 68; no. 4; pp. 3163 - 3173 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
ISSN | 0018-9545 1939-9359 |
DOI | 10.1109/TVT.2019.2897134 |
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Summary: | 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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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0018-9545 1939-9359 |
DOI: | 10.1109/TVT.2019.2897134 |