Xampling: Analog to digital at sub-Nyquist rates

By Moshe Mishali, Yonina C. Eldar, Oleg Dounaevsky, Eli Shoshan. IET Circuits Devices Syst., 2009

430 citations recorded by the source corpus.

The authors present a sub-Nyquist analog-to-digital converter of wideband inputs. The circuit realises the recently proposed modulated wideband converter, which is a flexible platform for sampling signals according to their actual bandwidth occupation. The theoretical work enables, for example, a sub-Nyquist wideband communication receiver, which has no prior information on the transmitter carrier positions. The present design supports input signals with 2 GHz Nyquist rate and 120 MHz spectrum occupancy, with arbitrary transmission frequencies. The sampling rate is as low as 280 MHz. To the best of the authors' knowledge, this is the first reported hardware that performs sub-Nyquist sampling and reconstruction of wideband signals. The authors describe the various circuit design considerations, with an emphasis on the non-ordinary challenges the converter introduces: mixing a signal with a multiple set of sinusoids, rather than a single local oscillator, and generation of highly transient periodic waveforms, with transient intervals on the order of the Nyquist rate. Hardware experiments validate the design and demonstrate sub-Nyquist sampling and signal reconstruction.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8ad205a80fe88d5abe8dc37bc78cf59886a3c53d

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Citations in the corpus

Structured Compressed Sensing: From Theory to Applications (2011, methodology): “Similar ideas were also developed in the context of array processing [21], [103], [104], equalization of sparse communication channels [105], [106], and more recently cognitive radio and multiband communications [9], [86], [107]–[109].”
Block-Sparse Signals: Uncertainty Relations and Efficient Recovery (2009, background): “Furthermore, recovery will be robust in the presence of noise and modeling errors (i.e., when the vector is not exactly block-sparse).”
Radar and Communication Coexistence: An Overview: A Review of Recent Methods (2019, background): “…must be characterized using other metrics since interference is not driving the design, and thus, no such SINR can be calculated The input transmissions then go through an RF combiner, resulting in a dynamic multiband input signal, that enables fast carrier switching for each of the bands.”
Rank Awareness in Joint Sparse Recovery (2010, background): “ructured sparsity [15], [16], [17] and blind compressed sensing [18]. These ideas have also been recently expanded to include sub-Nyquist sampling of structured analog signals [19], [20], [21], [22], [23]. As with the single measurement vector problem (SMV) several suboptimal methods for ﬁnding a sp”