We consider the problem of pilot-aided, uplink channel estimation in a
distributed massive multiple-input multiple-output (MIMO) architecture, in
which the access points are connected to a central processing unit via
fiber-optical fronthaul links, carrying a two-level-quantized version of the
received analog radio-frequency signal. We adapt to this architecture the
deep-learning-based channel-estimation algorithm recently proposed by Nguyen et
al. (2023), and explore its robustness to the additional signal distortions
(beyond 1-bit quantization) introduced in the considered architecture by the
automatic gain controllers (AGCs) and by the comparators. These components are
used at the access points to generate the two-level analog waveform from the
received signal. Via simulation results, we illustrate that the proposed
channel-estimation method outperforms significantly the Bussgang linear minimum
mean-square error channel estimator, and it is robust against the additional
impairments introduced by the AGCs and the comparators.

在分布式大规模多输入多输出 (MIMO) 架构中，我们考虑了试点辅助的上行信道估计问题，其中接入点通过光纤前传链路将接收到的模拟射频信号的两级量化版本连接到中央处理单元。我们在该架构中采用了由 Nguyen 等人 (2023) 最近提出的基于深度学习的信道估计算法，并探索该算法对接入点中的自动增益控制器 (AGCs) 和比较器引入的附加信号失真 (1 比特量化之外的失真) 的鲁棒性。这些组件用于从接收信号生成两级模拟波形。通过模拟结果，我们表明所提出的信道估计方法明显优于 Bussgang 线性最小均方误差信道估计器，并且对于由 AGCs 和比较器引入的附加损坏具有鲁棒性。