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Cited 24 time in webofscience Cited 29 time in scopus
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An Autonomous Transmission Scheme Using Dueling DQN for D2D Communication Networks

Authors
Ban, Tae-Won
Issue Date
Dec-2020
Publisher
Institute of Electrical and Electronics Engineers
Keywords
Autonomous transmission; device-to-device (D2D); dueling deep reinforcement learning (DRL); transmission scheme
Citation
IEEE Transactions on Vehicular Technology, v.69, no.12, pp 16348 - 16352
Pages
5
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Vehicular Technology
Volume
69
Number
12
Start Page
16348
End Page
16352
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/5882
DOI
10.1109/TVT.2020.3041458
ISSN
0018-9545
1939-9359
Abstract
In this paper, we investigate device-to-device (D2D) communication networks which are one of the key technologies for next-generation mobile communication networks and many other applications such as unmanned aerial vehicles (UAVs), vehicle-to-vehicle (V2V), and Internet of things (IoT). The overlay D2D communication networks that are considered in our study use dedicated radio resources separate from what cellular networks use and there exists co-channel interference in D2D networks without cross-channel interference between two networks. We propose a new transmission scheme for overlay D2D networks that uses a dueling deep reinforcement learning (DRL) architecture. The DRL is especially effective in environments where actions do not affect subsequent states as in wireless communication networks. The main contribution of this paper is that the proposed architecture is designed to utilize only information that each D2D devices can easily obtain by measuring channels. The proposed scheme thus enables D2D devices to train their own neural networks and to decide autonomously whether to transmit data without any intervention from infrastructures. The performance of the proposed scheme is analyzed in terms of average sum-rates and is compared to three baseline schemes. Simulation results show that the proposed scheme can achieve almost optimal sum-rates with low signal-to-noise (SNR) values without any intervention from infrastructure.
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Ban, Tae Won
IT공과대학 (AI정보공학과)
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