Utilizing low-resolution analog-to-digital converters (ADCs) in uplink massive multiple-input multiple-output (MIMO) systems is a practical solution to decrease power consumption. The performance gap between the low and high-resolution systems is small at low signal-to-noise ratio (SNR) regimes. However, at high SNR and with high modulation orders, the achievable rate saturates after a finite SNR value due to the stochastic resonance (SR) phenomenon. This paper proposes a novel pseudo-random quantization (PRQ) scheme by modifying the quantization thresholds that can help compensate for the effects of SR and makes communication with high-order modulation schemes such as $1024$-QAM in one-bit quantized uplink massive MIMO systems possible. Moreover, modified linear detectors for non-zero threshold quantization are derived, and a two-stage uplink detector for single-carrier (SC) multi-user systems is proposed. The first stage is an iterative method called Boxed Newton Detector (BND) that utilizes Newton's Method to maximize the log-likelihood with box constraints. The second stage, Nearest Codeword Detector (NCD), exploits the first stage solution and creates a small set of most likely candidates based on sign constraints to increase detection performance. The proposed two-stage method with PRQ outperforms the state-of-the-art detectors from the literature with comparable complexity while supporting high-order modulation schemes.

翻译：在上行大规模多输入多输出（MIMO）系统中使用低分辨率模数转换器（ADC）是降低功耗的实用方案。在低信噪比（SNR）区间内，低分辨率系统与高分辨率系统之间的性能差距较小。然而，在高SNR及高阶调制条件下，由于随机共振（SR）现象，可达速率在有限SNR值后趋于饱和。本文通过修改量化阈值提出了一种新颖的伪随机量化（PRQ）方案，该方案有助于补偿SR效应，使得在单比特量化的上行大规模MIMO系统中实现如$1024$-QAM等高阶调制方案成为可能。此外，推导了适用于非零阈值量化的改进线性检测器，并针对单载波（SC）多用户系统提出了一种两级上行检测器。第一级是一种名为箱式牛顿检测器（BND）的迭代方法，利用牛顿法在箱式约束下最大化对数似然函数。第二级即最近码字检测器（NCD），利用第一级的解并基于符号约束生成一个包含最可能候选的小集合，以提高检测性能。所提出的结合PRQ的两级方法在支持高阶调制方案的同时，以相当的复杂度优于文献中现有最先进的检测器。