We investigate full-duplex (FD) multi-user multiple input single-output systems with coarse quantization, aiming to characterize the impact of employing low-resolution analog-to-digital converters (ADCs) on self-interference (SI) and to develop a quantization- and SI-aware beamforming method that alleviates quantization-induced performance degradation in the FD systems. We first present an analysis on the perantenna signal-to-quantization noise ratio for conventional linear beamformers to provide the desired range of the number of analog-to-digital converter (ADC) bits, providing system insights for reliable FD operation in regard to the ADC resolution and beamforming strategy. Motivated by the insights, we then propose an SI-aware beamforming method that mitigates residual SI and quantization distortion. The resulting spectral efficiency (SE) maximization problem is decomposed into two tractable subproblems solved via alternating optimization: precoder and combiner design. The precoder optimization is formulated as a generalized eigenvalue problem, where the dominant eigenvector yields the best stationary solution through power iteration, while the combiner is derived as a quantization-aware minimum meansquared error (MMSE) filter. Numerical studies show that the number of required ADC bits with the proposed beamforming falls within the derived theoretical range while achieving the highest SE compared to benchmarks.

翻译：本文研究采用粗量化的全双工多用户多输入单输出系统，旨在分析低分辨率模数转换器对自干扰的影响，并开发一种兼顾量化效应与自干扰的波束成形方法，以缓解全双工系统中由量化引起的性能退化。首先，针对传统线性波束成形器进行每天线信号与量化噪声比分析，以确定模数转换器比特数的合理范围，从而为模数转换器分辨率与波束成形策略的选择提供可靠全双工操作的系统性见解。基于该见解，我们进一步提出一种自干扰感知的波束成形方法，用于抑制残余自干扰与量化失真。由此产生的频谱效率最大化问题被分解为两个可通过交替优化求解的子问题：预编码器与合并器设计。预编码器优化被构建为广义特征值问题，其中通过幂迭代得到的主特征向量可产生最佳稳态解；而合并器则被推导为量化感知的最小均方误差滤波器。数值研究表明，采用所提波束成形方法所需的模数转换器比特数落在理论推导范围内，且相较于基准方法实现了最高的频谱效率。