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Spectrum Leakage Effect Mitigation in RMPI Analog to Information Convertors

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Abstract (2. Language): 
With emergence of Compressive Sensing (CS) theory, new signal acquisition devices called Analog to Information Convertors (AIC) were proposed. One of the most well-known AICs among various AIC architectures is Random Modulator Pre-Integrator (RMPI) AIC. Due to the window processing of signals in RMPI AICs, a phenomena called spectral leakage manifests itself in Discrete Fourier Transform representation of the signal. The spectral leakage in effect reduces signal sparsity and hence degrades quality of the recovered signal. Investigating this problem, in this paper we propose a modified architecture of RMPI analog to information convertor, which successfully handles spectral leakage issue through applying a window envelop in analog domain, prior to measuring the signal. Our simulation results suggest a 25 dB improvement in SNR of recovered signal.
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REFERENCES

References: 

[1] R. H. Walden, "Analog-to-digital converter survey and analysis," Selected Areas in Communications, IEEE Journal on, vol. 17, pp. 539-550, 1999.
[2] E. J. Candè and M. B. Wakin, "An introduction to compressive sampling," Signal Processing Magazine, IEEE, vol. 25, pp. 21-30, 2008.
[3] R. Baraniuk and P. Steeghs, "Compressive radar imaging," in Radar Conference, 2007 IEEE, 2007, pp. 128-133.
[4] M. Bertocco, G. Frigo, and C. Narduzzi, "On compressed sensing and super-resolution in DFT-based spectral analysis," in Proceedings 19th IMEKO TC-4 Symposium and 17th IWADC Workshop Advances in Instrumentation and Sensors Interoperability, 2013, pp. 615-620.
[5] Y. Chi, L. L. Scharf, and A. Pezeshki, "Sensitivity to basis mismatch in compressed sensing," Signal Processing, IEEE Transactions on, vol. 59, pp. 2182-2195, 2011.
[6] R. G. Baraniuk, "Compressive sensing," IEEE signal processing magazine, vol. 24, 2007.
[7] P. Maechler, C. Studer, D. E. Bellasi, A. Maleki, A. Burg, N. Felber, et al., "VLSI design of approximate message passing for signal restoration and compressive sensing," Emerging and Selected Topics in Circuits and Systems, IEEE Journal on, vol. 2, pp. 579-590, 2012.
[8] L. Craven, O. Nagy, and L. Hanlen, "Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing," in 2010 Australian Communications Theory Workshop (AusCTW), 2010.

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