Quantization plays a critical role in digital signal processing systems. Quantizers are typically designed to obtain an accurate digital representation of the input signal, operating independently of the system task, and are commonly implemented using serial scalar analog-to-digital converters (ADCs). This talk is concerned with hardware-limited task-based quantization, where a system utilizing a serial scalar ADC is designed to provide a suitable representation in order to allow the recovery of a parameter vector underlying the input signal. We propose hardware-limited task-based quantization systems for a fixed and finite quantization resolution, and characterize their achievable distortion. Our results illustrate the benefits of properly taking into account the underlying task in the design of the quantization scheme.
Nir Shlezinger is postdoctoral researcher in the Signal Acquisition Modeling and Processing Lab in the Technion, Israel Institute of Technology. He completed his Ph.D .in Electrical Engineering at Ben-Gurion University, Israel, in 2017. His research interests include information theory and signal processing for communications.