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Conference Paper Convolutional Autoencoder for Compressive Symbol Detection
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Authors
JaeHyuck Park, Yookyung Kim
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.986-988
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539605
Abstract
This paper deals with a problem of digital communication which samples a signal compressively below its symbol rate and restores the original message symbols. We propose a method to optimize the sensing and restoration modules by designing them as the encoder and decoder of a convolutional autoencoder. The network learns end-to-end through simulation data. Our architecture has an efficient mechanism to solve this compressive symbol detection problem and it shows strong performance and fast speed empirically.
KSP Keywords
Convolutional auto-encoder, Digital Communication, Encoder and Decoder, End to End(E2E), Fast speed, Simulation data, symbol detection, symbol rate