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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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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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2019.2897134