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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Bibliographic Details
Published in2018 International Conference on Information and Communication Technology Convergence (ICTC) pp. 986 - 988
Main Authors Park, Jae-Hyuck, Kim, Yookyung
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Binary phase shift keying
Compressive Symbol Detection
Convolution
Convolutional Autoencoder
Data models
Decoding
Graphics processing units
Sensors
Training
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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.
DOI:10.1109/ICTC.2018.8539605