We present new insightful results on the uplink data detection for massive multiple-input multiple-output systems with 1-bit analog-to-digital converters. The expected values of the soft-estimated symbols (i.e., after the linear combining and prior to the data detection) have been recently characterized for multiple user equipments (UEs) and maximum ratio combining (MRC) receiver at the base station. In this paper, we first provide a numerical evaluation of the expected value of the soft-estimated symbols with zero-forcing (ZF) and minimum mean squared error (MMSE) receivers for a multi-UE setting with correlated Rayleigh fading. Then, we propose a joint data detection (JD) strategy, which exploits the interdependence among the soft-estimated symbols of the interfering UEs, along with its low-complexity variant. These strategies are compared with a naive approach that adapts the maximum-likelihood data detection to the 1-bit quantization. Numerical results show that ZF and MMSE provide considerable gains over MRC in terms of symbol error rate. Moreover, the proposed JD and its low-complexity variant provide a significant boost in comparison with the single-UE data detection.

翻译：本文针对采用1比特模数转换器的大规模多输入多输出系统上行数据检测问题，提出了具有深刻洞察的新成果。对于多个用户设备及基站采用最大比合并接收机的情形，软估计符号（即线性合并后、数据检测前的符号）的期望值近期已有表征。首先，在多用户相关瑞利衰落场景下，本文给出了采用迫零和最小均方误差接收机时软估计符号期望值的数值评估。随后，我们提出了一种联合数据检测策略，该策略利用干扰用户软估计符号间的相互依赖性，并进一步提出了其低复杂度变体。将这些策略与适应1比特量化的最大似然数据检测的朴素方法进行了比较。数值结果表明，在符号错误率方面，ZF和MMSE比MRC具有显著优势。此外，与单用户数据检测相比，所提出的JD及其低复杂度变体带来了显著性能提升。