Steady-state analysis of quantized distributed incremental LMS algorithm without Gaussian restriction | Signal, Image and Video Processing Skip to main content
Springer Nature Link
Log in

Steady-state analysis of quantized distributed incremental LMS algorithm without Gaussian restriction

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper, we analyze the steady-state performance of the distributed incremental least mean-square (DILMS) algorithm when it is implemented in finite-precision arithmetic. Our analysis in this paper does not consider any distribution of input data. We first formulate the update equation for quantized DILMS algorithm, and then we use a spatial-temporal energy conservation argument to derive theoretical expressions that evaluate the steady-state performance of individual nodes in the network. We consider mean-square error, excess mean-square error, and mean-square deviation as the performance criteria. Simulation results are generated by using two types of signals, Gaussian and non-Gaussian distributed signals. As the simulation results show, there is a good match between the theory and simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Estrin, D., Pottie, G., Srivastava, M.: Intrumenting the world with wireless sensor setworks. In: Proceeding of IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP), pp. 2033–2036. Salt Lake City, UT, May (2001)

  2. Lopes G., Sayed A.H.: Incremental adaptive strategies over distributed networks. IEEE Trans. Signal Process. 55(8), 4064–4077 (2007)

    Article  MathSciNet  Google Scholar 

  3. Sayed, A.H., Lopes, C.G.: Distributed recursive least-squares strategies over adaptive networks. In: Proceeding of Asilomar Conference on Signals, Systems, Computers, pp. 233–237. Monterey, CA, Oct (2006)

  4. Ram, S.S., Nedic, A., Veeravalli, V.V.: Stochastic incremental gradient descent for estimation in sensor networks. In: Proceeding of Asilomar conference on Signals, Systems, Computers, pp. 582–586. Pacific Grove, CA, Nov (2007)

  5. Li L., Chambers J. A., Lopes C. G., Sayed A. H.: Distributed estimation over an adaptive incremental network based on the affine projection algorithm. IEEE Trans. Signal Process. 58(1), 151–164 (2010)

    Article  MathSciNet  Google Scholar 

  6. Lopes C.G., Sayed A.H.: Diffusion least-mean squares over adaptive networks: formulation and performance analysis. IEEE Trans. Signal Process. 56(7), 3122–3136 (2008)

    Article  MathSciNet  Google Scholar 

  7. Cattivelli F., Lopes C.G., Sayed A.H.: Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Trans. Signal Process. 56(5), 1865–1877 (2008)

    Article  MathSciNet  Google Scholar 

  8. Rastegarnia A., Tinati M.A., Khalili A.: Performance analysis of quantized incremental LMS algorithm for distributed adaptive estimation. Signal Process. 90(8), 2621–2627 (2010)

    Article  MATH  Google Scholar 

  9. Sayed A.H.: Fundamentals of adaptive filtering. Wiley, Hoboken (2003)

    Google Scholar 

  10. Orfanidis S.J.: Introduction to signal processing. Prentice-Hall, Englewood Cliffs (1996)

    Google Scholar 

  11. Khalili A., Tinati M.A., Rastegarnia A.: Performance analysis of distributed incremental LMS algorithm with noisy links. Int. J. Distrib. Sens. Netw. 2011, 1–10 (2011)

    Article  Google Scholar 

  12. Khalili, A., Tinati, M.A., Rastegarnia, A.: Steady-state analysis of incremental LMS adaptive networks with noisy links. IEEE Trans. Signal Processing (2011 to appear)

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 51664, Iran

    Amir Rastegarnia, Mohammad Ali Tinati & Azam Khalili

Authors
  1. Amir Rastegarnia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Ali Tinati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Azam Khalili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Rastegarnia.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rastegarnia, A., Tinati, M.A. & Khalili, A. Steady-state analysis of quantized distributed incremental LMS algorithm without Gaussian restriction. SIViP 7, 227–234 (2013). https://doi.org/10.1007/s11760-011-0224-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-011-0224-x

Keywords

Search

Navigation