We present a new analytical framework on the uplink data detection for massive multiple-input multiple-output systems with 1-bit analog-to-digital converters (ADCs). We first characterize the expected values of the soft-estimated symbols (after the linear receiver and prior to the data detection), which are affected by the 1-bit quantization during both the channel estimation and the uplink data transmission. In our analysis, we consider conventional receivers such as maximum ratio combining (MRC), zero forcing, and minimum mean squared error (MMSE), with multiple user equipments (UEs) and correlated Rayleigh fading. Additionally, we design a linear minimum mean dispersion (LMMD) receiver tailored for the data detection with 1-bit ADCs, which exploits the expected values of the soft-estimated symbols previously derived. Then, we propose a joint data detection (JD) strategy that exploits the interdependence among the soft-estimated symbols of the interfering UEs, along with its low-complexity variant. These strategies are compared with the robust maximum likelihood data detection with 1-bit ADCs. Numerical results examining the symbol error rate show that MMSE exhibits a considerable performance gain over MRC, whereas the proposed LMMD receiver significantly outperforms all the conventional receivers. Lastly, the proposed JD and its low-complexity variant provide a significant boost in comparison with the single-UE data detection.

翻译：我们提出了一种新的分析框架，用于研究配备1位模数转换器的大规模多输入多输出系统的上行数据检测问题。我们首先刻画了软估计符号（在线性接收机之后、数据检测之前）的期望值，这些值受到信道估计和上行数据传输过程中1位量化的影响。在分析中，我们考虑了最大比合并、迫零和最小均方误差等传统接收机，并涉及多个用户设备和相关的瑞利衰落场景。此外，我们设计了一种专门针对1位ADC数据检测的线性最小均值散度接收机，该接收机利用了先前推导出的软估计符号期望值。然后，我们提出了一种联合数据检测策略，利用干扰用户软估计符号之间的相互依赖关系，并给出了其低复杂度变体。这些策略与基于1位ADC的鲁棒最大似然数据检测进行了比较。考察符号错误率的数值结果表明，MMSE相比MRC具有显著的性能增益，而所提出的LMMD接收机明显优于所有传统接收机。最后，与单用户数据检测相比，所提出的JD及其低复杂度变体提供了显著的性能提升。