  This paper considers uplink massive MIMO systems with 1-bit analog-to-digital
converters (ADCs) and develops a deep-learning based channel estimation
framework. In this framework, the prior channel estimation observations and
deep neural network models are leveraged to learn the non-trivial mapping from
quantized received measurements to channels. For that, we derive the sufficient
length and structure of the pilot sequence to guarantee the existence of this
mapping function. This leads to the interesting, and
\textit{counter-intuitive}, observation that when more antennas are employed by
the massive MIMO base station, our proposed deep learning approach achieves
better channel estimation performance, for the same pilot sequence length.
Equivalently, for the same channel estimation performance, this means that when
more antennas are employed, fewer pilots are required. This observation is also
analytically proved for some special channel models. Simulation results confirm
our observations and show that more antennas lead to better channel estimation
both in terms of the normalized mean squared error and the achievable
signal-to-noise ratio per antenna.
