  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.
