Why Analog-to-Information Converters Suffer in High-Bandwidth Sparse Signal Applications

In applications where signal frequencies are high, but information bandwidths are low, analog-to-information converters (AICs) have been proposed as a potential solution to overcome the resolution and performance limitations of high-speed analog-to-digital converters (ADCs). However, the hardware implementation of such systems has yet to be evaluated. This paper aims to fill this gap, by evaluating the impact of circuit impairments on performance limitations and energy cost of AICs. We point out that although the AIC architecture facilitates slower ADCs, the signal encoding, typically realized with a mixer-like circuit, still occurs at the Nyquist frequency of the input to avoid aliasing. We illustrate that the jitter and aperture of this mixing stage limit the achievable AIC resolution. In order to do so, we designed an end-to-end system evaluation framework for examining these limitations, as well as the relative energy-efficiency of AICs versus high-speed ADCs across the resolution, receiver gain and signal sparsity. The evaluation shows that the currently proposed AICs have no performance benefits over high-speed ADCs. However, AICs enable 2-10X in energy savings in low to moderate resolution (ENOB), low gain applications.

[1]  Joseph Mitola.  Cognitive Radio for Flexible Mobile Multimedia Communications , 2001, Mob. Networks Appl..

[2]  Brian M. Sadler,et al.  A Sub-Nyquist Rate Sampling Receiver Exploiting Compressive Sensing , 2011, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  Sergiy A. Vorobyov,et al.  Segmented Compressed Sampling for Analog-to-Information Conversion: Method and Performance Analysis , 2010, IEEE Transactions on Signal Processing.

[4]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[5]  Yonina C. Eldar,et al.  From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals , 2009, IEEE Journal of Selected Topics in Signal Processing.

[6]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[7]  Ramjee Prasad,et al.  OFDM for Wireless Multimedia Communications , 1999 .

[8]  Dominique Morche,et al.  GHz-range continuous-time programmable digital FIR with power dissipation that automatically adapts to signal activity , 2011, 2011 IEEE International Solid-State Circuits Conference.

[9]  W.M.C. Sansen,et al.  A micropower low-noise monolithic instrumentation amplifier for medical purposes , 1987 .

[10]  Justin K. Romberg,et al.  Compressive Sensing by Random Convolution , 2009, SIAM J. Imaging Sci..

[11]  Robert H. Walden,et al.  Analog-to-digital converter survey and analysis , 1999, IEEE J. Sel. Areas Commun..

[12]  Sameer R. Sonkusale,et al.  Input-Feature Correlated Asynchronous Analog to Information Converter for ECG Monitoring , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[13]  Jaeha Kim,et al.  Impulse sensitivity function analysis of periodic circuits , 2008, 2008 IEEE/ACM International Conference on Computer-Aided Design.

[14]  Andrea Montanari,et al.  Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.

[15]  Justin K. Romberg,et al.  Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.

[16]  Boris Murmann,et al.  A/D converter trends: Power dissipation, scaling and digitally assisted architectures , 2008, 2008 IEEE Custom Integrated Circuits Conference.

[17]  Vladimir Stojanovic,et al.  Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors , 2012, IEEE Journal of Solid-State Circuits.

[18]  Piotr Indyk,et al.  Sparse Recovery Using Sparse Random Matrices , 2010, LATIN.

[19]  S. Kirolos,et al.  Analog-to-Information Conversion via Random Demodulation , 2006, 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software.

[20]  Y. Akazawa,et al.  Jitter analysis of high-speed sampling systems , 1990 .

[21]  Omid Salehi-Abari.  Building compressed sensing systems : sensors and analog-to-information converters , 2012 .

[22]  B. Karajica,et al.  An 80-Gb/s 2/sup 31/-1 pseudorandom binary sequence generator in SiGe BiCMOS technology , 2005, IEEE Journal of Solid-State Circuits.

[23]  S. Kirolos,et al.  DepartmentofElectricalandComputerEngineering,RiceUniversity,Houston,TX DepartmentsofMathematicsandEECS,UniversityofMichigan,AnnArbor,MI , 2022 .

[24]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[25]  Piotr Indyk,et al.  Sparse Recovery Using Sparse Matrices , 2010, Proceedings of the IEEE.

[26]  Yonina C. Eldar,et al.  Efficient sampling of sparse wideband analog signals , 2008, 2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel.

[27]  Deog-Kyoon Jeong,et al.  A 20-Gb/s full-rate 27-1 PRBS generator integrated with 20-GHz PLL in 0.13-μm CMOS , 2008, 2008 IEEE Asian Solid-State Circuits Conference.

[28]  Sebastian Hoyos,et al.  Digitally assisted Analog Compressive Sensing , 2009, 2009 IEEE Dallas Circuits and Systems Workshop (DCAS).

[29]  C. Werner,et al.  Characterizing sampling aperture of clocked comparators , 2008, 2008 IEEE Symposium on VLSI Circuits.

[30]  Ieee Std,et al.  IEEE Standard for Terminology and Test Methods for Analog-to-Digital Converters , 2011 .

[31]  Richard G. Baraniuk,et al.  Theory and Implementation of an Analog-to-Information Converter using Random Demodulation , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[32]  S. Kirolos,et al.  Random Sampling for Analog-to-Information Conversion of Wideband Signals , 2006, 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software.