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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
- 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