Deep-learning-based Signal Detection for Faster-than-Nyquist Transmission

Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this letter, we develop a signal detection architecture based on deep learning (DL) for FTN system, which employs the sliding window and works without any iteration. To the best of our knowledge, this is the first attempt to apply a six-layer deep neural network (DNN) for FTN signal detection. As demonstrated by simulation results, our proposed DL-based detection can achieve a near-optimal bit error rate (BER) performance and shows the potential in high order modulations. In addition, the proposed DL-based detection has a robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to be a powerful tool for FTN signal detection.