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

In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is 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 sum rate...

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Bibliographic Details
Published inIEEE systems journal Vol. 13; no. 3; pp. 2551 - 2554
Main Authors Lee, Woongsup, Kim, Minhoe, Cho, Dong-Ho
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Channel capacity
Computational modeling
Computer simulation
Computing time
Control equipment
Deep learning
Device-to-device communication
interference
Iterative methods
Machine learning
Optimization
Power control
Production scheduling
Radio transmitters
Radios
Training
wireless communication
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Summary:In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is 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 sum 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 computation 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.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2018.2870483