  The need to recover high-dimensional signals from their noisy low-resolution
quantized measurements is widely encountered in communications and sensing. In
this paper, we focus on the extreme case of one-bit quantizers, and propose a
deep detector entitled LoRD-Net for recovering information symbols from one-bit
measurements. Our method is a model-aware data-driven architecture based on
deep unfolding of first-order optimization iterations. LoRD-Net has a
task-based architecture dedicated to recovering the underlying signal of
interest from the one-bit noisy measurements without requiring prior knowledge
of the channel matrix through which the one-bit measurements are obtained. The
proposed deep detector has much fewer parameters compared to black-box deep
networks due to the incorporation of domain-knowledge in the design of its
architecture, allowing it to operate in a data-driven fashion while benefiting
from the flexibility, versatility, and reliability of model-based optimization
methods. LoRD-Net operates in a blind fashion, which requires addressing both
the non-linear nature of the data-acquisition system as well as identifying a
proper optimization objective for signal recovery. Accordingly, we propose a
two-stage training method for LoRD-Net, in which the first stage is dedicated
to identifying the proper form of the optimization process to unfold, while the
latter trains the resulting model in an end-to-end manner. We numerically
evaluate the proposed receiver architecture for one-bit signal recovery in
wireless communications and demonstrate that the proposed hybrid methodology
outperforms both data-driven and model-based state-of-the-art methods, while
utilizing small datasets, on the order of merely $\sim 500$ samples, for
training.
