# Deep Signal Recovery with One-Bit Quantization

arXiv: https://arxiv.org/abs/1812.00797 · HTML: https://mlsanity.com/paper/1812.00797/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization](https://mlsanity.com/paper/2202.13157/) (2022)
- [LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers](https://mlsanity.com/paper/2102.02993/) (2021)
- [Generalized Linear Models with 1-Bit Measurements: Asymptotics of the Maximum Likelihood Estimator](https://mlsanity.com/paper/2501.04937/) (2025)
- [Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems](https://mlsanity.com/paper/2103.00125/) (2021)
- [Convex Quantization Preserves Logconcavity](https://mlsanity.com/paper/2206.05598/) (2022)
- [One-bit Compressed Sensing using Generative Models](https://mlsanity.com/paper/2502.12762/) (2025)
- [Signal Reconstruction from Quantized Noisy Samples of the Discrete Fourier Transform](https://mlsanity.com/paper/2201.03114/) (2022)
- [Interference Cancellation GAN Framework for Dynamic Channels](https://mlsanity.com/paper/2208.08019/) (2022)

## Abstract (arXiv)

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.