Deep Learning based Blind Symbol Packing Ratio Estimation for Faster-than-Nyquist Signaling

This letter proposes a blind symbol packing rartio estimation for faster-than-Nyquist (FTN) signaling based on state-of-the-art deep learning (DL) technology. The symbol packing rartio is a vital parameter to obtain the real symbol rate and recover the origin symbols from the received symbols by cal...

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Bibliographic Details
Published in:arXiv.org 2019-12
Main Authors: Song, Peiyang, Gong, Fengkui, Li, Qiang
Format: Article
Language:English
Subjects:
Adaptive control
Computer simulation
Deep learning
Machine learning
Robustness (mathematics)
Signal to noise ratio
Signaling
State of the art
Symbols
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Summary:This letter proposes a blind symbol packing rartio estimation for faster-than-Nyquist (FTN) signaling based on state-of-the-art deep learning (DL) technology. The symbol packing rartio is a vital parameter to obtain the real symbol rate and recover the origin symbols from the received symbols by calculating the intersymbol interference (ISI). To the best of our knowledge, this is the first effective estimation approach for symbol packing rartio in FTN signaling and has shown its fast convergence and robustness to signal-to-noise ratio (SNR) by numerical simulations. Benefiting from the proposed blind estimation, the packing-ratio-based adaptive FTN transmission without dedicate channel or control frame becomes available. Also, the secure FTN communications based on secret symbol packing rartio can be easily cracked.
ISSN:2331-8422
DOI:10.48550/arxiv.1907.05606