A Nonuniform Sampler for Wideband Spectrally-Sparse Environments
Michael B. Wakin,Stephen Becker,E. B. Nakamura,Michael C. Grant,Emilio A. Sovero,D. Ching,Juhwan Yoo,Justin Romberg,Azita Emami-Neyestanak,Emmanuel J. Candès +9 more
TLDR
A wide bandwidth, compressed sensing based nonuniform sampling (NUS) system with a custom sample-and-hold chip designed to take advantage of a low average sampling rate is presented.Abstract:
We present a wide bandwidth, compressed sensing based nonuniform sampling (NUS) system with a custom sample-and-hold chip designed to take advantage of a low average sampling rate. By sampling signals nonuniformly, the average sample rate can be more than a magnitude lower than the Nyquist rate, provided that these signals have a relatively low information content as measured by the sparsity of their spectrum. The hardware design combines a wideband Indium-Phosphide heterojunction bipolar transistor sample-and-hold with a commercial off-the-shelf analog-to-digital converter to digitize an 800 MHz to 2 GHz band (having 100 MHz of noncontiguous spectral content) at an average sample rate of 236 Ms/s. Signal reconstruction is performed via a nonlinear compressed sensing algorithm, and the challenges of developing an efficient implementation are discussed. The NUS system is a general purpose digital receiver. As an example of its real signal capabilities, measured bit-error-rate data for a GSM channel is presented, and comparisons to a conventional wideband 4.4 Gs/s ADC are made.read more
Citations
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Journal ArticleDOI
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications
Abdelmohsen Ali,Walaa Hamouda +1 more
TL;DR: This survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service.
Journal ArticleDOI
Spectral compressive sensing
TL;DR: The spectral CS (SCS) recovery framework for arbitrary frequencysparse signals is introduced and it is demonstrated that SCS signicantly outperforms current state-of-the-art CS algorithms based on the DFT while providing provable bounds on the number of measurements required for stable recovery.
Journal ArticleDOI
A Compressed Sensing Parameter Extraction Platform for Radar Pulse Signal Acquisition
Juhwan Yoo,C. Turnes,E. B. Nakamura,C. K. Le,Stephen Becker,Emilio A. Sovero,Michael B. Wakin,Michael C. Grant,Justin Romberg,Azita Emami-Neyestanak,Emmanuel J. Candès +10 more
TL;DR: A complete (hardware/ software) sub-Nyquist rate (× 13) wideband signal acquisition chain capable of acquiring radar pulse parameters in an instantaneous bandwidth spanning 100 MHz-2.5 GHz with the equivalent of 8 effective number of bits (ENOB) digitizing performance is presented.
Journal ArticleDOI
Output-only modal identification by compressed sensing: Non-uniform low-rate random sampling
Yongchao Yang,Satish Nagarajaiah +1 more
TL;DR: Results show that the proposed method in the CS framework can identify the modes using non-uniform low-rate random sensing, which is far below what is required by the Nyquist sampling theorem.
Journal ArticleDOI
A Novel Compressive Sensing Algorithm for SAR Imaging
Xiao Dong,Yunhua Zhang +1 more
TL;DR: The results show that the 2-D-DCSA can be applied to reconstructing the SAR images effectively with much less data than regularly required.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Journal ArticleDOI
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.