A Deep Reinforcement Learning Based D2D Relay Selection and Power Level Allocation in mmWave Vehicular Networks

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5G millimeter wave (mmWave) communication is an efficient technique for low delay and high data rate transmission in vehicular networks. Due to the high path loss in 5G mmWave band, 5G base stations need to be densely deployed, which may result in great deployment expenditures. In this letter, we jointly consider a relay selection problem in multihop 5G mmWave device to device (D2D) transmissions and a power level allocation problem of mmWave D2D links. We propose a centralized hierarchical deep reinforcement learning based method to find an optimal solution for the problem. The proposed method does not rely on the information of links, and it tries to find an optimal solution based on the information of vehicles. Simulation results show that the convergence of the proposed method, and the transmission delay performance of proposed method is better than a link-quality-prediction based method, and close to a link-quality-known method.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-03
Language
English
Article Type
Article
Citation

IEEE COMMUNICATIONS LETTERS, v.9, no.3, pp.416 - 419

ISSN
2162-2337
DOI
10.1109/LWC.2019.2958814
URI
http://hdl.handle.net/10203/273848
Appears in Collection
RIMS Journal Papers
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