On Unlimited Sampling

Wednesday, February 7, 2018 - 4:30pm to Thursday, February 8, 2018 - 4:55pm

Event Calendar Category

LIDS & Stats Tea

Speaker Name

Ayush Bhandari

Affiliation

MIT Media Lab

Building and Room Number

LIDS Lounge

Abstract

Shannon's sampling theorem provides a link between the continuous and the discrete realms stating that bandlimited signals are uniquely determined by its values on a discrete set. This theorem is realized in practice using so called analog-to-digital converters (ADCs). A practical problem in realizing this theorem is that ADCs are limited in dynamic range. Whenever a signal exceeds some preset threshold, the ADC saturates, resulting in aliasing due to clipping.

The goal of this work is to analyze an alternative approach that does not suffer from these problems. Our work is based on recent developments in ADC design, which allow for ADCs that reset rather than to saturate, thus producing modulo samples. An open problem that remains is: Given such modulo samples of a bandlimited function as well as the dynamic range of the ADC, how can the original signal be recovered and what are the sufficient conditions that guarantee perfect recovery? In this talk, we provide a sufficiency condition that is complemented with a stable recovery algorithm.

Interestingly, the sampling density criterion does not depend on the ADC threshold.‚Äč

Numerical experiments that corroborate our theory indeed show that it is possible to perfectly recover function that takes values that are orders of magnitude higher than the ADC's threshold.