Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication

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In this paper, a means of transmit power control for underlaid device-to-device (D2D) comm proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted stun rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved in an iterative manner which possibly requires long c imitation time, in our proposed scheme the transmit power can be determined with a relatively low computation time. Through simulations, we confirm that the proposed scheme achieves a sufficiently high WSR with a sufficiently low computation time.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-09
Language
English
Article Type
Article
Citation

IEEE SYSTEMS JOURNAL, v.13, no.3, pp.2551 - 2554

ISSN
1932-8184
DOI
10.1109/JSYST.2018.2870483
URI
http://hdl.handle.net/10203/267481
Appears in Collection
EE-Journal Papers(저널논문)
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