Analog-to-Information Converter Based on Off-the-Shelf Components and SoC-FPGA

This paper presents design and implementation of a 100-MHz Analog-to-Information Converter (AIC) based on Random Demodulator (RD); for this purpose, we used off-the-shelf components to implement the analog front-end and a SoC-FPGA chip to implement the digital hardware/software subsystem. Analog front-end is composed of one mixer and one low-pass filter, which were implemented by using a Gilbert Cell and a passive RC circuit, respectively. Hardware/software sub-system was implemented on the Field Programmable Array (FPGA) and Hard Processor System (HPS) sides of the SoC-FPGA chip, where FPGA side was used to implement the hardware that manages RD and HPS side was used to implement spectrum recovery algorithms. Finally, verification results showed that designed AIC can recover sparse signals of 100 MHz bandwidth, where a sub-Nyquist rate of 4 MHz is used along with two Compressive Sensing (CS) recovery algorithms.

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

[2]  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.

[3]  Luca De Vito A review of wideband spectrum sensing methods for Cognitive Radios , 2012 .

[4]  Shuchin Aeron,et al.  A Compressed sensing analog-to-information converter with edge-triggered SAR ADC Core , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[5]  Riccardo Rovatti,et al.  Hardware-Algorithms Co-Design and Implementation of an Analog-to-Information Converter for Biosignals Based on Compressed Sensing , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[6]  Keir Lauritzen,et al.  Design of a CMOS A2I data converter: Theory, architecture and implementation , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[7]  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.

[8]  Cheng-Xiang Wang,et al.  Wideband spectrum sensing for cognitive radio networks: a survey , 2013, IEEE Wireless Communications.

[9]  S. Kirolos,et al.  Practical Issues in Implementing Analog-to-Information Converters , 2006, 2006 6th International Workshop on System on Chip for Real Time Applications.

[10]  S. Kirolos,et al.  A prototype hardware for random demodulation based compressive analog-to-digital conversion , 2008, 2008 51st Midwest Symposium on Circuits and Systems.

[11]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[12]  Sujay Deb,et al.  Energy Efficient Analog-to-Information Converter for Biopotential Acquisition Systems , 2015, 2015 IEEE International Symposium on Nanoelectronic and Information Systems.

[13]  Richard G. Baraniuk,et al.  On the feasibility of hardware implementation of sub-Nyquist random-sampling based analog-to-information conversion , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[14]  W. Marsden I and J , 2012 .

[15]  J. Romberg,et al.  Sparse Signal Recovery via l1 Minimization , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[16]  Balas K. Natarajan,et al.  Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..

[17]  Yue Wang,et al.  A hardware implementation of random demodulation analog-to-information converter , 2016, IEICE Electron. Express.

[18]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.