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http://dx.doi.org/10.6109/jkiice.2019.23.8.975

Deep Learning Based User Scheduling For Multi-User and Multi-Antenna Networks  

Ban, Tae-Won (Department of Information and Communication Engineering, Gyeongsang National University)
Lee, Woongsup (Department of Information and Communication Engineering, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.8, 2019 , pp. 975-980 More about this Journal
Abstract
In this paper, we propose a deep learning-based scheduling scheme for user selection in multi-user multi-antenna networks which is considered one of key technologies for the next generation mobile communication systems. We obtained 90,000 data samples from the conventional optimal scheme to train the proposed neural network and verified the trained neural network to check if the trained neural network is over-fitted. Although the proposed neural network-based scheduling algorithm requires considerable complexity and time for training in the initial stage, it does not cause any extra complexity once it has been trained successfully. On the other hand, the conventional optimal scheme continuously requires the same complexity of computations for every scheduling. According to extensive computer-simulations, the proposed deep learning-based scheduling algorithm yields about 88~96% average sum-rates of the conventional scheme for SNRs lower than 10dB, while it can achieve optimal average sum-rates for SNRs higher than 10dB.
Keywords
Multi-user; multi-antenna; beamforming; scheduling; deep learning;
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