  Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth
utilization at the expense of additional intersymbol interference (ISI). In
this paper, we apply state-of-the-art deep learning (DL) technology into
receiver design for FTN signaling and propose two DL-based new architectures.
Firstly, we propose an FTN signal detection based on DL and connect it with the
successive interference cancellation (SIC) to replace traditional detection
algorithms. Simulation results show that this architecture can achieve
near-optimal performance in both uncoded and coded scenarios. Additionally, we
propose a DL-based joint signal detection and decoding for FTN signaling to
replace the complete baseband part in traditional FTN receivers. The
performance of this new architecture has also been illustrated by simulation
results. Finally, both the proposed DL-based receiver architecture has the
robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to
be a powerful tool for the FTN receiver design.
