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Article

Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

by 1 and 2,*
1
School of Electronic and Electrical Engineering, Hankyong National University, Anseong 17579, Korea
2
School of Electronic and Electrical Engineering, Institute of Information Telecommunication Convergence (IITC), Hankyong National University, Anseong 17579, Korea
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1606; https://doi.org/10.3390/electronics9101606
Received: 5 August 2020 / Revised: 19 September 2020 / Accepted: 24 September 2020 / Published: 1 October 2020
(This article belongs to the Special Issue Toward a New Era of Radio Access Technologies for 5G and Beyond)
The proximity-based device-to-device (D2D) communication allows for internet of things, public safety, and data offloading services. Because of these advantages, D2D communication has been applied to wireless communication networks. In wireless networks using D2D communication, there are challenging problems of the data rate shortage and coverage limitation due to co-channel interference in the proximity communication. To resolve the problems, transmit power control schemes that are based on deep learning have been presented in network-assisted D2D communication systems. The power control schemes have focused on enhancing spectral efficiency and energy efficiency in the presence of interference. However, the data-rate fairness performance may be a key performance metric in D2D communications, because devices in proximity can expect fair quality of service in the system. Hence, in this paper, a transmit power control scheme using a deep-learning algorithm based on convolutional neural network (CNN) is proposed to consider the data-rate fairness performance in network-assisted D2D communication systems, where the wireless channels are modelled by path loss and Nakagami fading. In the proposed scheme, the batch normalization (BN) scheme is introduced in order to further enhance the spectral efficiency of the conventional deep-learning transmit power control scheme. In addition, a loss function for the deep-learning optimization is defined in order to consider both the data-rate fairness and spectral efficiency. Through simulation, we show that the proposed scheme can achieve extremely high fairness performance while improving the spectral efficiency of the conventional schemes. It is also shown that the improvement in the fairness and spectral efficiency is achieved for different Nakagami fading conditions and sizes of area containing the devices. View Full-Text
Keywords: deep learning; transmit power control; spectral efficiency; index of fairness deep learning; transmit power control; spectral efficiency; index of fairness
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MDPI and ACS Style

Kim, D.; Lee, I.-H. Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems. Electronics 2020, 9, 1606. https://doi.org/10.3390/electronics9101606

AMA Style

Kim D, Lee I-H. Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems. Electronics. 2020; 9(10):1606. https://doi.org/10.3390/electronics9101606

Chicago/Turabian Style

Kim, Donghyeon; Lee, In-Ho. 2020. "Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems" Electronics 9, no. 10: 1606. https://doi.org/10.3390/electronics9101606

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