Deep Signal Recovery with One-Bit Quantization
This work addresses signal recovery in sensing and communications, but it is incremental as it builds on existing deep unfolding techniques for a specific quantization scenario.
The paper tackles the problem of reconstructing high-dimensional signals from noisy one-bit measurements by proposing DeepRec, a model-based deep unfolding method, which improves both accuracy and computational efficiency compared to the original framework as demonstrated through numerical analysis.
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.