Improved Sampling Model for the Nyquist Folding Receiver

Abstract

The Nyquist Folding Receiver is an architecture that uses Compressed Sensing to convert analog Radio Frequency signals into digital signals. Analog-to-Digital Converter architectures that implement Compressed Sensing are collectively known as Analog-to-Information. Sparse bandlimited analog signals with frequency bands above the Nyquist frequency of a traditional Analog-to-Digital Converter can be recovered by Analog-to-Information. While most Analog-to-Information architectures use Gaussian distributed random variables for Compressed Sensing measurements, the Nyquist Folding Receiver uses structured Compressed Sensing measurements that are incoherent with general sparsifying bases like the Fourier Transform. The quality of the structured Compressed Sensing measurement model sets a baseline for how effective Compressed Sensing recovery algorithms will be. Current measurement models for the Nyquist Folding Receiver don't accurately capture all aspects of the architecture. The proposed structured Compressed Sensing measurement model for the Nyquist Folding Receiver should help improve Compressed Sensing signal reconstruction accuracy and push Analog-to-Information architectures towards broader adoption within commercial Radio Frequency communication devices.