상세정보
학술대회
Convolutional Autoencoder for Compressive Symbol Detection
- 발행일
- 201810
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.986-988
- 협약과제
- 18HF1100, 압축센싱, 무전원 및 초고속체 전송 기반 무선통신 효율극대화 연구, 이우용
- 초록
- 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 제안 키워드
- Convolutional auto-encoder, Digital Communication, Encoder and Decoder, End to End(E2E), Fast speed, Simulation data, symbol detection, symbol rate