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http://dx.doi.org/10.6109/jkiice.2021.25.5.685

Performance Analysis of Deep Learning Based Transmit Power Control Using SINR Information Feedback in NOMA Systems  

Kim, Donghyeon (School of Electronic and Electrical Engineering, Hankyong National University)
Lee, In-Ho (School of Electronic and Electrical Engineering, Hankyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.5, 2021 , pp. 685-690 More about this Journal
Abstract
In this paper, we propose a deep learning-based transmit power control scheme to maximize the sum-rates while satisfying the minimum data-rate in downlink non-orthogonal multiple access (NOMA) systems. In downlink NOMA, we consider the co-channel interference that occurs from a base station other than the cell where the user is located, and the user feeds back the signal-to-interference plus noise power ratio (SINR) information instead of channel state information to reduce system feedback overhead. Therefore, the base station controls transmit power using only SINR information. The use of implicit SINR information has the advantage of decreasing the information dimension, but has disadvantage of reducing the data-rate. In this paper, we resolve this problem with deep learning-based training methods and show that the performance of training can be improved if the dimension of deep learning inputs is effectively reduced. Through simulation, we verify that the proposed deep learning-based power control scheme improves the sum-rate while satisfying the minimum data-rate.
Keywords
Deep learning; Transmit power control; Non-orthogonal multiple access; Channel feedback;
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