  Machine learning, and more specifically deep learning, have shown remarkable
performance in sensing, communications, and inference. In this paper, we
consider the application of the deep unfolding technique in the problem of
signal reconstruction from its one-bit noisy measurements. Namely, we propose a
model-based machine learning method and unfold the iterations of an inference
optimization algorithm into the layers of a deep neural network for one-bit
signal recovery. The resulting network, which we refer to as DeepRec, can
efficiently handle the recovery of high-dimensional signals from acquired
one-bit noisy measurements. The proposed method results in an improvement in
accuracy and computational efficiency with respect to the original framework as
shown through numerical analysis.
