Downlink NOMA for Short-Packet Internet-of-Things Communications with Low-Resolution ADCs

In this paper, we propose precoding design to maximize the sum achievable rate in downlink non-orthogonal multiple access (NOMA)-aided short-packet Internet-of-Things (IoT) communications, wherein each IoT device is equipped with low-resolution analog-to-digital converters (ADCs). Due to intertwined effects caused from low-resolution ADCs, short-packets, and NOMA, it is challenging to find efficient precoding vectors. To resolve the difficulties, we first linearize quantization distortion by adopting an additive quantization noise model. Thereafter, we approximate non-smooth functions by using a LogSumExp technique. With the transformed problem, we derive a first-order optimality condition and propose a novel precoding algorithm which identifies an efficient local optimal solution with low complexity. Based on the proposed algorithm, we also investigate an efficient NOMA decoding ordering method for the considered system. Via simulations, we demonstrate that the proposed method outperforms other baseline methods.