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학술지 Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters
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저자
백명선, 곽상운, 정준영, 김흥묵, 최동준
발행일
201909
출처
IEEE Transactions on Broadcasting, v.65 no.3, pp.636-642
ISSN
0018-9316
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TBC.2019.2891051
협약과제
18ZR1600, 동일 채널에서의 기계 학습 기반 다중 RF 신호 송수신 기술 개발, 최동준
초록
In this paper, simple methodologies of deep learning application to conventional multiple-input multiple-output (MIMO) communication systems are presented. The deep learning technologies with deep neural network (DNN) structure, emerging technologies in various engineering areas, have been actively investigated in the field of communication engineering as well. In the physical layer of conventional communication systems, there are practical challenges of application of DNN: calculating complex number in DNN and designing proper DNN structure for a specific communication system model. This paper proposes and verifies simple solutions for the difficulty. First, we apply a basic DNN structure for signal detection of one-tap MIMO channel. Second, convolutional neural network (CNN) and recurrent neural network (RNN) structures are presented for MIMO system with multipath fading channel. Our DNN structure for one-tap MIMO channel can achieve the optimal maximum likelihood detection performance, and furthermore, our CNN and RNN structures for multipath fading channel can detect the transmitted signal properly.
키워드
CNN, communication systems, Deep learning, DNN, MIMO, RNN, signal detection
KSP 제안 키워드
Communication system, Complex Number, Convolution neural network(CNN), Deep learning application, Deep neural network(DNN), Emerging technology, Learning-based, MIMO channel, MIMO system, MIMO transmitters, Multipath fading channel