전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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딥러닝을 활용한 무선 전송 및 접속 기술 동향

Research Trends on Wireless Transmission and Access Technologies Using Deep Learning

  • 발행 : 2018.10.01
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초록

Deep learning is a promising solution to a number of complex problems based on its inherent capability to approximate almost all types of functions without the demand for handcrafted feature extraction. New wireless transmission and access schemes based on deep learning are being increasingly proposed as substitutes for existing approaches, providing a lower complexity and better performance gain. Among such schemes, a communications system is viewed as an end-to-end autoencoder. The learning process applied in autoencoders can automatically deal with some nonlinear or unknown properties in communications systems. Deep learning can also be used to optimize each processing block for required tasks such as channel decoding, signal detection, and multiple access. On top of recent related research trends, we suggest appropriate research approaches for communications systems to adopt deep learning.

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과제정보

연구 과제번호 : 다점대다점 환경에서 이론적 한계 도달을 위한 무선전송기술 개발

연구 과제 주관 기관 : 한국전자통신연구원

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