This paper investigates full-duplex (FD) multi-user multiple-input multiple-output (MU-MIMO) system design with coarse quantization. We first analyze the impact of self-interference (SI) on quantization in FD single-input single-output systems. The analysis elucidates that the minimum required number of analog-to-digital converter (ADC) bits is logarithmically proportional to the ratio of total received power to the received power of desired signals. Motivated by this, we design a FD MIMO beamforming method that effectively manages the SI. Dividing a spectral efficiency maximization beamforming problem into two sub-problems for alternating optimization, we address the first by optimizing the precoder: obtaining a generalized eigenvalue problem from the first-order optimality condition, where the principal eigenvector is the optimal stationary solution, and adopting a power iteration method to identify this eigenvector. Subsequently, a quantization-aware minimum mean square error combiner is computed for the derived precoder. Through numerical studies, we observe that the proposed beamformer reduces the minimum required number of ADC bits for achieving higher spectral efficiency than that of half-duplex (HD) systems, compared to FD benchmarks. The overall analysis shows that, unlike with quantized HD systems, more than 6 bits are required for the ADC to fully realize the potential of the quantized FD system.

翻译：本文研究了采用粗量化的全双工（FD）多用户多输入多输出（MU-MIMO）系统设计。首先，我们分析了全双工单输入单输出系统中自干扰（SI）对量化的影响。分析表明，模数转换器（ADC）所需的最小比特数与总接收功率与期望信号接收功率之比呈对数关系。基于此，我们设计了一种能有效管理自干扰的全双工MIMO波束赋形方法。将频谱效率最大化波束赋形问题分解为两个子问题以交替优化：首先通过优化预编码器解决第一个子问题——从一阶最优性条件中导出广义特征值问题，其中主特征向量即为最优驻点解，并采用幂迭代法识别该特征向量；随后，针对导出的预编码器计算量化感知最小均方误差合并器。数值研究表明，与全双工基准方案相比，所提波束赋形器在实现高于半双工（HD）系统的频谱效率时，可降低ADC所需的最小比特数。整体分析表明，与量化半双工系统不同，ADC需要超过6个比特才能完全发挥量化全双工系统的潜力。