Deep Learning-based Transceiver with One-bit ADC Over Fading Channel

To tackle the power consumption challenges in terahertz band wireless communication, this study proposes a deep learning-driven approach for transceiver design that utilizes one-bit quantization and oversampling at the receiver. The solution also involves implementing Faster-than-Nyquist (FTN) transmission on a fading channel. Our approach employs a convolutional autoencoder (AE) to enable the transmission of higher-order modulation over a one-bit fading channel while utilizing pilots. By exploiting the AE transceiver, it is evident that performance in quantized communication has significantly improved for QPSK, 16-QAM, and 64-QAM modulation levels, approaching the theoretical lower bound for the corresponding modulation over additive white Gaussian noise (AWGN) channel. Furthermore, the study has explored how to use the robust error-correcting capabilities of the AE transceiver to boost spectral efficiency by increasing FTN rates without dire Bit-error-rate sacrifice.