Neural Network Aided TeraHertz Backhaul Communications Using One-Bit ADCs

TeraHertz (THz) communication is considered as a promising technology to satisfy the ever-increasing demand for high-rate services in the next generation of wireless communication systems. However, the high-speed high-resolution analog-to-digital converters (ADCs) of THz systems are power hungry and complex to implement. Low-resolution ADCs are considered as an efficient solution to reduce the cost and complexity of THz systems. Especially, one-bit ADCs are of particular interest because they require only a comparator and an automatic gain control is not needed anymore. In this paper, we investigate a point-to-point THz backhaul communication system wherein one-bit ADCs with temporal oversampling are deployed at the receiver side. The bit-error-rate (BER) performance of the proposed system is evaluated through an end-to-end link level simulation. Specifically, we propose a convolutional neural network (CNN) based receiver which considers not only the nonlinearity caused by the one-bit ADCs but also the correlation between the received samples due to the oversampling. Our results demonstrate that the proposed CNN-based receiver can considerably improve the BER performance especially at moderate signal-to-noise ratio (SNR) values. The idea of far-field digital dithering is also proposed to maintain the BER performance at high-SNR regime.