Blind symbol packing ratio estimation for faster-than-Nyquist signalling based on deep learning

Abstract

This Letter proposes a blind symbol packing ratio estimation for faster-than-Nyquist (FTN) signalling based on state-of-the-art deep learning technology. The symbol packing ratio (also named speeding parameter, time packing parameter etc.) is a vital parameter to obtain the real symbol rate and recover the origin symbols from the received symbols by calculating the intersymbol interference. To the best of the authors' knowledge, this is the first effective estimation approach for symbol packing ratio in FTN signalling and has shown its fast convergence and robustness to signal-to-noise ratio 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 the secret symbol packing ratio can be easily cracked.