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학술대회 Decoding of Polar Code by Using Deep Feed-Forward Neural Networks
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저자
서지훈, 이주열, 김근영
발행일
201803
출처
International Conference on Computing, Networking and Communications (ICNC) 2018 : Workshop, pp.238-242
DOI
https://dx.doi.org/10.1109/ICCNC.2018.8390279
협약과제
17ZF1100, 다점대다점 환경에서 이론적 한계도달을 위한 무선전송 기술 개발, 김근영
초록
With the success of image classification problems, deep learning is expanding its application areas. In this paper, we apply deep learning to decode a polar code. As an initial step for memoryless additive Gaussian noise channel, we consider a deep feed-forward neural network and investigate its decoding performances with respect to numerous configurations: the number of hidden layers, the number of nodes for each layer, and activation functions. Generally, the higher complex network yields a better performance. Comparing the performances of regular list decoding, we provide a guideline for the configuration parameters. Although the training of deep learning may require high computational complexity, it should be noted that the field application of trained networks can be accomplished at a low level complexity. Considering the level of performance and complexity, we believe that deep learning is a competitive decoding tool.
KSP 제안 키워드
Activation function, Application areas, Classification problems, Complex Networks(CN), Computational complexity, Configuration parameter, Feedforward neural networks, Hidden layer, Image classification, Initial step, List Decoding