Reconciling Compressive Sampling Systems for Spectrally-sparse Continuous-time Signals

Abstract

The Random Demodulator (RD) and the Modulated Wideband Converter (MWC) are two recently proposed compressed sensing (CS) techniques for the acquisition of continuous-time spectrally-sparse signals. They extend the standard CS paradigm from sampling discrete, finite dimensional signals to sampling continuous and possibly infinite dimensional ones, and thus establish the ability to capture these signals at sub-Nyquist sampling rates. The RD and the MWC have remarkably similar structures (similar block diagrams), but their reconstruction algorithms and signal models strongly differ. To date, few results exist that compare these systems, and owing to the potential impacts they could have on spectral estimation in applications like electromagnetic scanning and cognitive radio, we more fully investigate their relationship in this paper. We show that the RD and the MWC are both based on the general concept of random filtering, but employ significantly different sampling functions. We also investigate system sensitivities (or robustness) to sparse signal model assumptions. Lastly, we show that "block convolution" is a fundamental aspect of the MWC, allowing it to successfully sample and reconstruct block-sparse (multiband) signals. Based on this concept, we propose a new acquisition system for continuous-time signals whose amplitudes are block sparse. The paper includes detailed time and frequency domain analyses of the RD and the MWC that differ, sometimes substantially, from published results.

Corrected typos, updated Section 4.3, 30 pages, 8 figures

References in corpus (6)

• Compressed Sensing of Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
• From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals
• Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
• Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
• Xampling: Signal Acquisition and Processing in Union of Subspaces
• Rank Awareness in Joint Sparse Recovery

Cited by in corpus (5)

• The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range
• A Constrained Random Demodulator for Sub-Nyquist Sampling
• High Resolution Radar Sensing with Compressive Illumination
• Compressive and Noncompressive Power Spectral Density Estimation from Periodic Nonuniform Samples
• A Survey of Wideband Spectrum Sensing Algorithms for Cognitive Radio Networks and Sub-Nyquist Approaches