https://scholars.lib.ntu.edu.tw/handle/123456789/611220
標題: | Neural network-based equalizer by utilizing coding gain in advance | 作者: | AN-YEU(ANDY) WU | 關鍵字: | Channel coding; Channel fading; Convolutional neural network; Equalizer; Recurrent neural network | 公開日期: | 2019 | 來源出版物: | GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings | 摘要: | Recently, deep learning has been exploited in many fields with revolutionary breakthroughs. In the light of this, deep learning-assisted communication systems have also attracted much attention in recent years and have potential to break down the conventional design rule for communication systems. In this work, we propose two kinds of neural network-based equalizers to exploit different characteristics between convolutional neural networks and recurrent neural networks. The equalizer in conventional block-based design may destroy the code structure and degrade the capacity of coding gain for decoder. On the contrary, our proposed approach not only eliminates channel fading, but also exploits the code structure with utilization of coding gain in advance, which can effectively increase the overall utilization of coding gain with more than 1.5 dB gain. © 2019 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079275128&doi=10.1109%2fGlobalSIP45357.2019.8969437&partnerID=40&md5=a6bb6db7897a3cf52ac56ca1c363a0e0 https://scholars.lib.ntu.edu.tw/handle/123456789/611220 |
ISBN: | 9.78173E+12 | DOI: | 10.1109/GlobalSIP45357.2019.8969437 | SDG/關鍵字: | Channel coding; Codes (symbols); Convolution; Convolutional neural networks; Fading channels; Network coding; Recurrent neural networks; Block based design; Break down; Code structure; Coding gains; Conventional design; Equalizers |
顯示於: | 電機工程學系 |
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