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Analog-to-Information Conversion via Random Demodulation

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8 Author(s)
Sami Kirolos ; Dept. of Electrical and Computer Engineering, Rice University, Houston, TX ; Jason Laska ; Michael Wakin ; Marco Duarte
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Many problems in radar and communication signal processing involve radio frequency (RF) signals of very high bandwidth. This presents a serious challenge to systems that might attempt to use a high-rate analog-to-digital converter (ADC) to sample these signals, as prescribed by the Shannon/Nyquist sampling theorem. In these situations, however, the information level of the signal is often far lower than the actual bandwidth, which prompts the question of whether more efficient schemes can be developed for measuring such signals. In this paper we propose a system that uses modulation, filtering, and sampling to produce a low-rate set of digital measurements. Our "analog-to-information converter" (AIC) is inspired by the theory of compressive sensing (CS), which states that a discrete signal having a sparse representation in some dictionary can be recovered from a small number of linear projections of that signal. We generalize the CS theory to continuous-time sparse signals, explain our proposed AIC system in the CS context, and discuss practical issues regarding implementation

Published in:

2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software

Date of Conference:

Oct. 2006