Citation :

Agus Santoso, SN Abdul Samad, 2026. "Compressed Sensing-Driven Analog Front-End Architectures for Sub-Nyquist Signal Acquisition in High-Dimensional Systems" ESP International Journal of Emerging Multidisciplinary Research [ESP-IJEMR]  Volume 2, Issue 1: 01-14.

Abstract :

As a new paradigm for signal processing, compressed sensing (CS), allows the acquisition of signals at rates that are far below their Nyquist rate comparable to the traditional laws of sampling and reconstruction. CS offers a complete and mathematically rigorous framework for preserving the quality of reconstruction while reducing sampling requirements in high-dimensional systems where signals are intrinsically sparse or compressible in some transform domain. In this research paper, we study the design and development of AFE architectures driven by compressed sensing for sub-Nyquist signal acquisition through complex high-dimensional setups.

References :

References :

[1] Candies, E. J., Romberg, J., & Tao, T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory. (2006).

[2] Donohue, D. L. Compressed sensing. IEEE Transactions on Information Theory. (2006).

[3] Candies, E. J. and Waken, M. B. An introduction to compressive sampling. IEEE Signal Processing Magazine. (2008).

[4] Baronial, R. Compressive sensing. IEEE Signal Processing Magazine. (2007).

[5] Davenport, M. A., Duarte, M. F., Elder, Y. C. and Kutyniok, G. Introduction to compressed sensing. IEEE Signal Processing Magazine. (2012).

[6] Tropp, J. A. and Gilbert, A. C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory. (2007).

[7] Candies, E. J. and Plan, Y. A probabilistic and RIPless theory of compressed sensing. IEEE Transactions on Information Theory. (2011).

[8] Elder, Y. C. and Mishali, M. Robust recovery of signals from a structured union of subspaces. IEEE Transactions on Information Theory. (2009).

[9] Mishali, M. and Elder, Y. C. From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals. IEEE Journal of Selected Topics in Signal Processing. (2010).

[10] Mishali, M. and Elder, Y. C. Blind multiband signal reconstruction: Compressed sensing for analog signals. IEEE Transactions on Signal Processing. (2009).

[11] Vetterli, M., Marziliano, P. and Blu, T. Sampling signals with finite rate of innovation. IEEE Transactions on Signal Processing. (2002).

[12] Tropp, J. A., Laska, J. N., Duarte, M. F., Romberg, J. and Baronial, R. G. Beyond Nyquist: Efficient sampling of sparse bandlimited signals. IEEE Transactions on Information Theory. (2010).

[13] Landau, H. J. Necessary density conditions for sampling and interpolation of certain entire functions. Acta Mathematica. (1967).

[14] Laska, J. N., Kirolos, S., Duarte, M. F., Ragheb, T. S., Baronial, R. G. and Massoud, Y. Theory and implementation of an analog-to-information converter using random demodulation. IEEE International Symposium on Circuits and Systems. (2007).

[15] Tropp, J. A., Duarte, M. F., Laska, J. N., et al. Random demodulation for compressive sampling of analog signals. IEEE Transactions on Information Theory. (2010).

[16] Mishali, M. and Elder, Y. C. Sub-Nyquist sampling. IEEE Signal Processing Magazine. (2011).

[17] Kirolos, S., Laska, J., Duarte, M., et al. Analog-to-information conversion via random demodulation. IEEE Dallas/CAS Workshop. (2006).

[18] Gangopadhyay, D., et al. Compressed sensing analog front-end for bio-signal acquisition. IEEE Journal of Solid-State Circuits. (2014).

[19] Pang, Z., et al. Random demodulation-based compressed sensing architecture. Elsevier Signal Processing Journal. (2018).

[20] Elder, Y. C. Sampling Theory: Beyond Bandlimited Systems. Cambridge University Press. (2015).

[21] Elder, Y. C. and Kutyniok, G. Compressed Sensing: Theory and Applications. Cambridge University Press. (2012).

[22] Haupt, J., Bajwa, W., Raz, G. and Nowak, R. Toeplitz compressed sensing matrices. IEEE/SP Workshop. (2008).

[23] Chen, S. S., Donohue, D. L. and Saunders, M. A. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing. (1998).

[24] Tibshirani, R. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society. (1996).

[25] Needell, D. and Tropp, J. A. CoSaMP: Iterative signal recovery. Applied and Computational Harmonic Analysis. (2009).

[26] Beck, A. and Teboulle, M. A fast iterative shrinkage-thresholding algorithm (FISTA). SIAM Journal on Imaging Sciences. (2009).

[27] Boyd, S., Parikh, N., Chu, E., et al. Distributed optimization and statistical learning via ADMM. Foundations and Trends in Machine Learning. (2011).

[28] Lustig, M., Donohue, D. and Pauly, J. Sparse MRI: The application of compressed sensing. Magnetic Resonance in Medicine. (2007).

[29] Duarte, M. F., Davenport, M. A., Takhar, D., et al. Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine. (2008).

[30] Herman, M. and Strohmer, T. High-resolution radar via compressed sensing. IEEE Transactions on Signal Processing. (2009).

[31] Cohen, D. and Elder, Y. C. Sub-Nyquist radar systems. IEEE Radar Conference. (2011).

[32] Bajwa, W. U., Haupt, J., Raz, G. and Nowak, R. Compressed channel sensing. IEEE Proceedings. (2010).

[33] Nyquist Folding Receiver (NYFR) architecture for RF signal processing. U.S. Patent.

[34] Modulated Wideband Converter (MWC) for sub-Nyquist sampling. IEEE Transactions.

[35] Analog-to-Information Converter (AIC) architectures for compressed sensing. IEEE Circuits and Systems Magazine.

Keywords :

High-Dimensional Signal Processing, Sparse Signal Representation, Analog-to-Information Converter, Modulated Wideband Converter, Signal Reconstruction Algorithms, Low-Power ADC Design, Noise Robustness