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http://dx.doi.org/10.6109/jkiice.2019.23.3.326

Performance Verification of Deep Learning based Transmit Power Control  

Lee, Woongsup (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Kim, Seong Hwan (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Ryu, Jongyeol (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Ban, Tae-Won (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.3, 2019 , pp. 326-332 More about this Journal
Abstract
Recently, the deep learning technology has gained lots of attention which leads to its application to various fields. Especially, there are recent attempts to overcome the limit of wireless communications systems through the use of the deep learning. In this paper, we have verified the performance of deep learning based transmit power control scheme. Unlike previous transmit power control schemes where the optimal transmit power is derived by solving the optimization problem explicitly, in the deep learning based transmit power control, the general solver for the optimization problem is derived through the deep neural network (DNN). Especially, by using the spectral efficiency as the loss function of DNN, the training can be performed without needing labels. Through simulation based on Tensorflow, we confirm that the transmit power control based on deep learning can achieve the optimal performance while reducing the computational complexity by 1/200.
Keywords
Deep learning; Transmit power control; Wireless communication systems; Optimization; Verification;
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Times Cited By KSCI : 2  (Citation Analysis)
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