Unlimited Sampling of Bandpass Signals: Computational Demodulation via Undersampling

TL;DR

This paper introduces a novel approach for sampling and recovering high dynamic range bandpass signals using modulo sampling, enabling sub-Nyquist rates and practical hardware implementation despite aliasing challenges.

Contribution

It provides new sampling theorems for bandpass signals from undersampled modulo samples and demonstrates hardware feasibility through experiments.

Findings

Successful recovery of bandpass signals at sub-Nyquist rates.

Validation of theoretical results with hardware experiments.

Effective handling of high dynamic range signals without clipping.

Abstract

Bandpass signals are an important sub-class of bandlimited signals that naturally arise in a number of application areas but their high-frequency content poses an acquisition challenge. Consequently, "Bandpass Sampling Theory" has been investigated and applied in the literature. In this paper, we consider the problem of modulo sampling of bandpass signals with the main goal of sampling and recovery of high dynamic range inputs. Our work is inspired by the Unlimited Sensing Framework (USF). In the USF, the modulo operation folds high dynamic range inputs into low dynamic range, modulo samples. This fundamentally avoids signal clipping. Given that the output of the modulo nonlinearity is non-bandlimited, bandpass sampling conditions never hold true. Yet, we show that bandpass signals can be recovered from a modulo representation despite the inevitable aliasing. Our main contribution includes proof of sampling theorems for recovery of bandpass signals from an undersampled representation, reaching sub-Nyquist sampling rates. On the recovery front, by considering both time-and frequency-domain perspectives, we provide a holistic view of the modulo bandpass sampling problem. On the hardware front, we include ideal, non-ideal and generalized modulo folding architectures that arise in the hardware implementation of modulo analog-to-digital converters. Numerical simulations corroborate our theoretical results. Bridging the theory-practice gap, we validate our results using hardware experiments, thus demonstrating the practical effectiveness of our methods.