Deep Learning-based Transceiver Design for Pilotless Communication over Fading Channel with one-bit ADC and Oversampling

With the aim of addressing power consumption issues for terahertz band wireless communication, this work presents a deep learning-based solution for transceiver design with 1-bit quantization and oversampling at the receiver, and Faster-than-Nyquist transmission over fading channel. Specifically, by implementing the transceiver using a convolutional autoencoder, our work allows higher-order modulation transmission over one-bit fading channel without pilots. Transfer learning from previously trained blocks over simple noisy channel is used to minimize the probability of bit error and outperforms the convolutional autoencoder at low oversampling rates. The biterror-rate gain offered at 20dB SNR by the transfer learning is seen to be as high as half of one order at low oversampling rates and to saturate as oversampling rate increases. Furthermore, by allowing explicit phase synchronization, the autoencoder-based transceiver with partial channel matching is able to approach unquantized performance with 4dB gap in Rayleigh fading environment.