  Massive multiple-input multiple-output (MIMO) communication systems have a
huge potential both in terms of data rate and energy efficiency, although
channel estimation becomes challenging for a large number of antennas. Using a
physical model allows to ease the problem by injecting a priori information
based on the physics of propagation. However, such a model rests on simplifying
assumptions and requires to know precisely the configuration of the system,
which is unrealistic in practice.In this paper we present mpNet, an unfolded
neural network specifically designed for massive MIMO channel estimation. It is
trained online in an unsupervised way. Moreover, mpNet is computationally
efficient and automatically adapts its depth to the signal-to-noise ratio
(SNR). The method we propose adds flexibility to physical channel models by
allowing a base station (BS) to automatically correct its channel estimation
algorithm based on incoming data, without the need for a separate offline
training phase.It is applied to realistic millimeter wave channels and shows
great performance, achieving a channel estimation error almost as low as one
would get with a perfectly calibrated system. It also allows incident detection
and automatic correction, making the BS resilient and able to automatically
adapt to changes in its environment.
