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Time Encoding Quantization of Bandlimited and Finite-Rate-of-Innovation Signals

Published 5 Oct 2021 in cs.IT, eess.SP, and math.IT | (2110.01928v2)

Abstract: This paper studies the impact of quantization in integrate-and-fire time encoding machine (IF-TEM) sampler used for bandlimited (BL) and finite-rate-of-innovation (FRI) signals. An upper bound is derived for the mean squared error (MSE) of IF-TEM sampler and is compared against that of classical analog-to-digital converters (ADCs) with uniform sampling and quantization. The interplay between a signal's energy, bandwidth, and peak amplitude is used to identify how the MSE of IF-TEM sampler with quantization is influenced by these parameters. More precisely, the quantization step size of the IF-TEM sampler can be reduced when the maximum frequency of a bandlimited signal or the number of pulses of an FRI signal is increased. Leveraging this insight, specific parameter settings are identified for which the quantized IF-TEM sampler achieves an MSE bound that is roughly 8 dB lower than that of a classical ADC with the same number of bits. Experimental results validate the theoretical conclusions.

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References (34)
  1. H. Naaman, S. Mulleti, Y. C. Eldar, and A. Cohen, “Time-Based Quantization for FRI and Bandlimited Signals,” in 2022 30th European Signal Processing Conference (EUSIPCO).   IEEE, 2022, pp. 2241–2245.
  2. T. Berger and J. D. Gibson, “Lossy source coding,” IEEE Trans. on Information Theory, vol. 44, no. 6, pp. 2693–2723, 1998.
  3. Z. Fang, L. Lou, K. Tang, W. Wang, B. Chen, Y. Wang, and Y. Zheng, “A CMOS-integrated radar-assisted cognitive sensing platform for seamless human-robot interactions,” in 2021 IEEE International Symposium on Circuits and Systems (ISCAS).   IEEE, 2021, pp. 1–4.
  4. H. Nyquist, “Certain topics in telegraph transmission theory,” Trans. Amer. Inst. Electr. Engineers, vol. 47, no. 2, pp. 617–644, 1928.
  5. M. Miśkowicz and D. Kościelnik, “The dynamic range of timing measurements of the asynchronous Sigma-Delta modulator,” Proc. IFAC, vol. 39, no. 21, pp. 395–400, 2006.
  6. D. Kościelnik and M. Miśkowicz, “Designing Time-to-Digital Converter for Asynchronous ADCs,” Proc. IEEE Design Diag. Electron. Circuits Syst., pp. 1–6, 2007.
  7. H. Naaman, S. Mulleti, and Y. C. Eldar, “Uniqueness and Robustness of TEM-Based FRI Sampling,” in 2022 IEEE International Symposium on Information Theory (ISIT).   IEEE, 2022, pp. 2631–2636.
  8. S. Mulleti, T. Zirtiloglu, A. Tan, R. T. Yazicigil, and Y. C. Eldar, “Power-efficient sampling,” arXiv preprint arXiv:2312.10966, 2023.
  9. S. Mulleti, A. Bhandari, and Y. C. Eldar, “Power-aware analog to digital converters,” in Sampling, Approximation, and Signal Analysis: Harmonic Analysis in the Spirit of J. Rowland Higgins.   Springer, 2024, pp. 415–452.
  10. E. Allier, G. Sicard, L. Fesquet, and M. Renaudin, “A new class of asynchronous A/D converters based on time quantization,” in Ninth International Symposium on Asynchronous Circuits and Systems, 2003. Proceedings., 2003, pp. 196–205.
  11. E. Roza, “Analog-to-digital conversion via duty-cycle modulation,” IEEE Trans. Circ. Syst. II: Analog and digital signal processing, vol. 44, no. 11, pp. 907–914, 1997.
  12. H. G. Feichtinger, J. C. Príncipe, J. L. Romero, A. S. Alvarado, and G. A. Velasco, “Approximate reconstruction of bandlimited functions for the integrate and fire sampler,” Adv. Comput. Math., vol. 36, no. 1, pp. 67–78, 2012.
  13. A. A. Lazar and L. T. Tóth, “Perfect recovery and sensitivity analysis of time encoded bandlimited signals,” IEEE Trans. Circuits Syst. I, vol. 51, no. 10, pp. 2060–2073, 2004.
  14. A. A. Lazar and L. T. Toth, “Time encoding and perfect recovery of bandlimited signals,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. (ICASSP), vol. 6, 2003, pp. VI–709.
  15. A. A. Lazar, “Time encoding with an integrate-and-fire neuron with a refractory period,” Neurocomputing, vol. 58, pp. 53–58, 2004.
  16. M. Rastogi, A. Singh Alvarado, J. G. Harris, and J. C. Príncipe, “Integrate and fire circuit as an ADC replacement,” in Proc. IEEE Int. Symp. Circuits Syst., 2011, pp. 2421–2424.
  17. S. Ryu, C. Y. Park, W. Kim, S. Son, and J. Kim, “A Time-Based Pipelined ADC Using Integrate-and-Fire Multiplying-DAC,” IEEE Trans. Circuits Syst. I, vol. 68, no. 7, pp. 2876–2889, 2021.
  18. H. Naaman, N. Glazer, M. Namer, D. Bilik, S. Savariego, and Y. C. Eldar, “Hardware Prototype of a Time-Encoding Sub-Nyquist ADC,” arXiv preprint arXiv:2301.02012, 2023.
  19. B. Rajendran, A. Sebastian, M. Schmuker, N. Srinivasa, and E. Eleftheriou, “Low-Power Neuromorphic Hardware for Signal Processing Applications: A Review of Architectural and System-Level Design Approaches,” IEEE Signal Process. Mag., vol. 36, no. 6, pp. 97–110, 2019.
  20. F. Barranco, C. Fermüller, and Y. Aloimonos, “Contour Motion Estimation for Asynchronous Event-Driven Cameras,” Proc. IEEE, vol. 102, no. 10, pp. 1537–1556, 2014.
  21. A. A. Lazar and E. A. Pnevmatikakis, “Video time encoding machines,” IEEE Trans. on Neural Networks, vol. 22, no. 3, pp. 461–473, 2011.
  22. K. Adam, A. Scholefield, and M. Vetterli, “Sampling and reconstruction of bandlimited signals with multi-channel time encoding,” IEEE Trans. Signal Process., vol. 68, pp. 1105–1119, 2020.
  23. N. T. Thao and D. Rzepka, “Time encoding of bandlimited signals: Reconstruction by pseudo-inversion and time-varying multiplierless FIR filtering,” IEEE Trans. Signal Process., vol. 69, pp. 341–356, 2020.
  24. K. Adam, A. Scholefield, and M. Vetterli, “Asynchrony Increases Efficiency: Time Encoding of Videos and Low-Rank Signals,” IEEE Trans. Signal Process., vol. 70, pp. 105–116, 2021.
  25. D. Gontier and M. Vetterli, “Sampling based on timing: Time encoding machines on shift-invariant subspaces,” Applied and Comput. Harmonic Anal., vol. 36, no. 1, pp. 63–78, 2014.
  26. A. Aldroubi and K. Gröchenig, “Nonuniform sampling and reconstruction in shift-invariant spaces,” SIAM review, vol. 43, no. 4, pp. 585–620, 2001.
  27. R. Alexandru and P. L. Dragotti, “Reconstructing classes of non-bandlimited signals from time encoded information,” IEEE Trans. Signal Process., vol. 68, pp. 747–763, 2019.
  28. S. Rudresh, A. J. Kamath, and C. S. Seelamantula, “A Time-Based Sampling Framework for Finite-Rate-of-Innovation Signals,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. (ICASSP), 2020, pp. 5585–5589.
  29. H. Naaman, S. Mulleti, and Y. C. Eldar, “FRI-TEM: Time encoding sampling of finite-rate-of-innovation signals,” IEEE Trans. Signal Process., 2022.
  30. H. Naaman, E. Reznitskiy, N. Glazer, M. Namer, and Y. C. Eldar, “Sub-Nyquist time-based sampling of FRI signals,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. (ICASSP), 2021.
  31. A. Papoulis, “Limits on bandlimited signals,” Proc. of the IEEE, vol. 55, no. 10, pp. 1677–1686, 1967.
  32. M. Vetterli, P. Marziliano, and T. Blu, “Sampling signals with finite rate of innovation,” IEEE Trans. Signal Process., vol. 50, no. 6, pp. 1417–1428, 2002.
  33. R. Tur, Y. C. Eldar, and Z. Friedman, “Innovation rate sampling of pulse streams with application to ultrasound imaging,” IEEE Trans. Signal Process., vol. 59, no. 4, pp. 1827–1842, 2011.
  34. R. M. Gray and D. L. Neuhoff, “Quantization,” IEEE Trans. on information theory, vol. 44, no. 6, pp. 2325–2383, 1998.
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