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학술대회 Deep Learning-based Signal Detection Technique for FTN Signaling-based Emergency Alert Communication System
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저자
백명선, 박원주, 이용태
발행일
202108
출처
International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) 2021, pp.1-3
DOI
https://dx.doi.org/10.1109/BMSB53066.2021.9547144
협약과제
21HR4200, 위험 상황 초기 인지를 위한 ICT 기반의 범죄 위험도 예측 및 대응 기술 개발, 이용태
초록
In this paper, for spectrally efficient transmission of emergency alert signal, a faster-than-Nyquist signal is considered. FTN signaling is a useful communication technique for high spectral efficiency. However, since the use of a faster symbol rate than Nyquist rate destroys orthogonality between symbols, ISI is generated inevitably. To reduce the ISI effect, interference cancellation and signal detection process is required for the FTN receiver. Generally, interference cancellation techniques based on the trellis algorithm are adopted for ISI reduction. The complexity of the trellis algorithms is highly increased according to the increase of the number of states. And the increase of interference symbols augments the number of states exponentially. This paper investigates the interference cancellation technique based on deep learning technology for FTN communication systems. To reduce the continuous interference between adjacent signals, LSTM algorithm-based RNN is applied. The simulation results show that the proposed deep learning-based signal detector can provide similar performance to the trellis-based BCJR algorithm.
키워드
deep learning, FTN signaling, interference cancellation, LSTM, RNN, signal detection
KSP 제안 키워드
BCJR algorithm, Communication system, FTN Signaling, High spectral efficiency, Interference Cancellation Technique, Learning-based, Number of states, Nyquist rate, Signal detector, Spectral efficiency(SE), Spectrally efficient