Online unsupervised deep unfolding for massive MIMO channel estimation

Massive 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 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 letter, we propose to add flexibility to physical channel models by unfolding the channel estimation algorithms as neural networks. This leads to a neural network structure that can be trained online when initialized with an imperfect model, realizing automatic system calibration. The method is applied to both single path and multipath 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.