Journal of Signal Processing Systems

, Volume 84, Issue 3, pp 425–434 | Cite as

A Multistage Architecture for Statistical Inference with Stochastic Signal Acquisition

Article

Abstract

We describe a statistical inference approach for designing signal acquisition interfaces and inference systems with stochastic devices. A signal is observed by an array of binary comparison sensors, such as highly scaled comparators in an analog-to-digital converter, that exhibit random offsets in their reference levels due to process variations or other uncertainties. These offsets can limit the performance of conventional measurement devices. In our approach, we build redundancy into the sensor array and use statistical estimation techniques to account for uncertainty in the observations and produce a more reliable estimate of the acquired signal. We develop an observational model and find a Cramér-Rao lower bound on the achievable square error performance of such a system. We then propose a two-stage inference architecture that uses a coarse estimate to select a subset of the sensor outputs for further processing, reducing the overall complexity of the system while achieving near-optimal performance. The performance of the architecture is demonstrated using a simulated prototype for parameter estimation and symbol detection applications. The results suggest the feasibility of using unreliable components to build reliable signal acquisition and inference systems.

Keywords

Statistical inference Parameter estimation Stochastic circuits Quantization 

Notes

Acknowledgments

We wish to acknowledge our collaborators Naveen Verma and Sen Tao of Princeton University for their input on this work.

References

  1. 1.
    Kinget, P. (2005). Device mismatch and tradeoffs in the design of analog circuits. IEEE Journal Solid-State Circuits, 40(6), 1212–1224.CrossRefGoogle Scholar
  2. 2.
    Keyes, R.W. (1975). The effect of randomness in the distribution of impurity atoms on FET thresholds. Applied Physics, 8(3), 251–259.CrossRefGoogle Scholar
  3. 3.
    Razavi, B., & Wooley, B. (1992). Design techniques for high-speed, high-resolution comparators. IEEE Journal Solid-State Circuits, 27(12), 1916–1926.CrossRefGoogle Scholar
  4. 4.
    Donovan, C., & Flynn, M. (2002). A ‘Digital’ 6-bit ADC in 0.25 μm CMOS. IEEE Journal Solid-State Circuits, 37(3), 432–437.CrossRefGoogle Scholar
  5. 5.
    Paulus, C., Bluthgen, H.-m., Low, M., Sicheneder, E., Briils, N., Courtois, A., Tiebout, M., & Thewes, R. (2004). A 4GS/s 6b flash ADC in 0.13 μm CMOS. VLSI Circuits, 420–423.Google Scholar
  6. 6.
    Sundstrom, T., & Alvandpour, A. (2009). Utilizing process variations for reference generation in a flash ADC. IEEE Transactions Circuits System II, Experimentalis Briefs, 56(5), 364–368.CrossRefGoogle Scholar
  7. 7.
    Weaver, S., Hershberg, B., Kurahashi, P., Knierim, D., & Moon, U.-k. (2010). Stochastic Flash Analog-to-Digital Conversion. IEEE Transactions Circuits System II, Experimentalis Briefs, 57(11), 2825–2833.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Papadopoulos, H., Wornell, G.W., & Oppenheim, A. (2001). Sequential signal encoding from noisy measurements using quantizers with dynamic bias control. IEEE Transactions Information Theory, 47(3), 978–1002.MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Ribeiro, A., & Giannakis, G. (2006). Bandwidth-constrained distributed estimation for wireless sensor networks-Part I: Gaussian case. IEEE Transactions Signal Processing, 54(3), 1131–1143.CrossRefGoogle Scholar
  10. 10.
    Corey, R.M., Singer, A.C., Tao, S., & Verma, N. (2014). A low complexity estimation architecture based on noisy comparators. In Proceedings IEEE Workshop Signal Processing Systems.Google Scholar
  11. 11.
    Lehmann, E., & Casella, G. (1998). Theory of Point Estimation. New York: Springer.Google Scholar
  12. 12.
    Poor, H. (1994). An Introduction to Signal Detection and Estimation. New York: Springer.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.University of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations