Perfect reconstruction formulas and bounds on aliasing error in sub-nyquist nonuniform sampling of multiband signals

By Raman Venkataramani, Yoram Bresler. IEEE Transactions on Information Theory, 2000

298 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/dc720d07aa7bc4d068fd1b38e7c3aabcd1e571fa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals (2009, background): “In [3] and [4], a multicoset sampling strategy was developed, independent of the signal support, to acquire multiband signals at low rates.”
Wideband spectrum sensing for cognitive radio networks: a survey (2013, background): “In [13], some sampling patterns were proved to be valid for un iq e signal reconstruction.”
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications (2017, background): “2) Multi-Coset Approach: Multi-coset sampling (MC) is a special case of the general periodic non-uniform subNyquist sampling technique for acquiring sparse multi-band signals [114], [115].”
Xampling: Analog to digital at sub-Nyquist rates (2009, background): “The analog bandwidth limitation, which stems from the speed of the ADC internal track-and-hold circuitry, holds for pointwise strategies when the carrier frequencies are known [23]–[25] and even when utilized for the case of unknown transmission locations [19].”
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals (2009, background): “We believe that these applications will be easier to achieve in the near future because suitable ADC and DSP technology is already available.”
Structured Compressed Sensing: From Theory to Applications (2011, background): “The approaches in [156], [160] were semi-blind: a sampler design independent of band positions combined with the reconstruction algorithm of [155] which requires exact support knowledge.”