2022/12/08 by Arian Eamaz, Eamaz, Arian, Farhang Yeganegi +5
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Electrical engineering #Image and Signal Denoising Methods #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2301.03467
openalex publication_date 2022/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One-bit quantization with time-varying sampling thresholds has recently found significant utilization potential in statistical signal processing applications due to its relatively low power consumption and low implementation cost. In addition to such advantages, an attractive feature of one-bit analog-to-digital converters (ADCs) is their superior sampling rates as compared to their conventional multi-bit counterparts. This characteristic endows one-bit signal processing frameworks with what we refer to as sample abundance. On the other hand, many signal recovery and optimization problems are formulated as (possibly non-convex) quadratic programs with linear feasibility constraints in the one-bit sampling regime. We demonstrate, with a particular focus on the nuclear norm minimization, that the sample abundance paradigm allows for the transformation of such quadratic problems to merely a linear feasibility problem by forming a large-scale overdetermined linear system; thus removing the need for costly optimization constraints and objectives. To make this achievable, we propose enhanced randomized Kaczmarz algorithms to tackle these highly overdetermined feasibility problems. Several numerical results are presented to illustrate the effectiveness of the proposed methodologies.