On the Achievable Rate of TeraHertz Backhaul Systems with Low-Resolution ADCs using Neural Networks

TeraHertz (THz) communication, with its ultra-high bandwidth, is considered as a promising technology for the next generation wireless communication systems. To reduce the implementation cost and power consumption of THz systems, low-resolution analog-to-digital converters (ADCs) are proposed as a practical solution. In this paper, the achievable information rate of a THz backhaul communication link with low resolution ADCs is investigated in the form of generalized mutual information (GMI). Specifically, two approaches are proposed for GMI performance evaluation: analytical approach using uniform quantization noise model and end-to-end link simulation using a dense neural network (DNN) soft demapper. Our results demonstrate that the GMI performance as high as 4.9 bits/symbol can be achieved by using a 64-point quadrature amplitude modulation (QAM) with two-bit resolution ADCs and up-sampling ratio of four at the transmitter. We also investigate geometric constellation shaping (GCS) in low resolution ADC systems using a NN-aided trainable constellation and end-to-end learning of the proposed system.