한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제23권8호
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  • Pages.975-980
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  • 2019
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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다중 사용자 다중 안테나 네트워크를 위한 심화 학습기반 사용자 스케쥴링

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)
  • 투고 : 2019.06.18
  • 심사 : 2019.07.25
  • 발행 : 2019.08.31
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초록

본 논문에서는 차세대 이동통신 시스템의 핵심 요소 기술 중의 하나로 각광 받고 있는 다중 사용자 다중 안테나 네트워크에서 사용자 선택을 위한 심화 학습 기반 스케쥴링 기법을 제안한다. 제안된 신경망을 학습시키기 위하여 기존의 최적 방식을 통해서 90,000 데이터 샘플을 확보하였으며, 추가적인 10,000 데이터 샘플을 이용하여 최종 학습된 신경망의 과최적화 여부를 확인하였다. 제안된 신경망 기반의 스케쥴링 알고리즘은 초기 학습 시에는 상당한 복잡도와 학습 시간이 필요하지만, 일단 학습이 완료된 이후에는 추가적인 복잡도가 유발되지 않는 장점이 있다. 반면에, 기존의 최적 방식은 매 스케쥴링마다 동일한 복잡도의 계산이 지속적으로 요구된다. 다양한 컴퓨터 시뮬레이션 결과에 따르면, 제안된 심화 학습 기반의 스케쥴링 기법은 10dB 보다 낮은 SNR에서는 기존 최적 알고리즘의 약 88~96%에 이르는 평균 전송 속도의 합을 얻을 수 있으며, 10dB 이상의 SNR에서는 최적의 평균 전송 속도의 합을 얻을 수 있다.

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.

키워드

참고문헌

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