Deep Reinforcement Learning Based Resource Allocation for V2V Communications

2019/02/04 by Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang +1 · 44 citations
Engineering · Computer Science · #Vehicular Ad Hoc Networks (VANETs) #Advanced Data and IoT Technologies #Software-Defined Networks and 5G

paper · pdf · doi:10.1109/tvt.2019.2897134

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.

Citations

• ImageNet classification with deep convolutional neural networks

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