# Deep Signal Recovery with One-Bit Quantization

## TL;DR

This paper introduces DeepRec, a deep learning model that unfolds an inference algorithm for efficient and accurate reconstruction of high-dimensional signals from one-bit noisy measurements, advancing signal recovery techniques.

## Contribution

It proposes a novel deep unfolding approach for one-bit signal recovery, combining model-based methods with deep learning for improved performance.

## Key findings

- Enhanced accuracy over traditional methods
- Reduced computational complexity
- Effective high-dimensional signal reconstruction

## Abstract

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.