Convolutional Autoencoder for Compressive Symbol Detection
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 autoencod...
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Published in | 2018 International Conference on Information and Communication Technology Convergence (ICTC) pp. 986 - 988 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.10.2018
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.1109/ICTC.2018.8539605 |