Skip to main content
Log in

Diffusion Sign Subband Adaptive Filtering Algorithm with Enlarged Cooperation and Its Variant

  • Short Paper
  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

The recently proposed diffusion sign subband adaptive filtering (DSSAF) algorithm is more robust than most of mean-square error minimization criterion-based diffusion distributed estimation algorithms in an impulsive interference environment. To enhance its convergence rate and steady-state misalignment, this paper proposes a DSSAF algorithm with enlarged cooperation (DSSAF-EC). The DSSAF-EC algorithm exchanges not only the weight information but also measurements within individual neighborhoods. Moreover, a variant of the DSSAF-EC algorithm, called the proportionate DSSAF-EC (PDSSAF-EC) algorithm, is presented. It incorporates an adaptive gain matrix into the DSSAF-EC algorithm to proportionately adapt the weight vectors of agents. Simulation results verify that both the DSSAF-EC and PDSSAF-EC algorithms are robust against impulsive interference and that the PDSSAF-EC algorithm can obtain faster convergence rate than the DSSAF-EC algorithm in estimating a sparse unknown weight vector.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. R. Arablouei, S. Werner, K. Dogancay, Y.F. Huang, Analysis of a reduced-communication diffusion LMS algorithm. Signal Process. 117, 355–361 (2015)

    Article  Google Scholar 

  2. G. Azamia, M.A. Tinati, Steady-state analysis of the deficient length incremental LMS adaptive networks. Circuits Syst. Signal Process. 34(9), 2893–2910 (2015)

    Article  MathSciNet  Google Scholar 

  3. J. Benesty, S.L. Gay, An improved PNLMS algorithm, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Orlando, FL, USA, 2002), pp. 1881–1884

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

    Article  MathSciNet  Google Scholar 

  5. F.S. Cattivelli, A.H. Sayed, Diffusion LMS strategies for distributed estimation. IEEE Trans. Signal Process. 58(3), 1035–1048 (2010)

    Article  MathSciNet  Google Scholar 

  6. F.S. Cattivelli, A.H. Sayed, Modeling bird flight formations using diffusion adaptation. IEEE Trans. Signal Process. 59(5), 2038–2051 (2011)

    Article  MathSciNet  Google Scholar 

  7. D.L. Duttweiler, Proportionate normalized least-mean-squares adaption in echo cancelers. IEEE Trans. Speech Audio Process. 8(5), 508–518 (2000)

    Article  Google Scholar 

  8. G.O. Glentis, An efficient implementation of the memory improved proportionate affine projection algorithm. Signal Process. 118, 25–35 (2016)

    Article  Google Scholar 

  9. Y. Guo, W. Wu, B. Zhang, H. Sun, A distributed state estimation method for power systems incorporating linear and nolinear models. Signal Process. 64, 608–616 (2015)

    Google Scholar 

  10. S. Huang, C. Li, Distributed sparse total least-squares over networks. IEEE Trans. Signal Process. 63(11), 2986–2998 (2015)

    Article  MathSciNet  Google Scholar 

  11. S.M. Jung, J.H. Seo, P. Park, A variable step-size diffusion normalized least-mean-square algorithm with a combination method based on mean-square deviation. Circuits Syst. Signal Process. 34(10), 3291–3304 (2015)

    Article  Google Scholar 

  12. H.L. Lee, S.E. Kim, J.W. Lee, W.J. Song, A variable step-size diffusion LMS algorithm for distributed estimation. IEEE Trans. Signal Process. 63(7), 1808–1820 (2015)

    Article  MathSciNet  Google Scholar 

  13. J.W. Lee, S.E. Kim, W.J. Song, Data-selective diffusion LMS for reducing communication overhead. Signal Process. 113, 211–217 (2015)

    Article  Google Scholar 

  14. K.A. Lee, W.S. Gan, Improving convergence of the NLMS algorithm using constrained subband updates. IEEE Signal Process. Lett. 11, 736–739 (2004)

    Article  Google Scholar 

  15. J. Li, G. AlRegib, Rate-constrained distributed estimation in wireless sensor networks. IEEE Trans. Signal Process. 55(5), 1624–1643 (2007)

    Article  MathSciNet  Google Scholar 

  16. L. Li, J. He, Y. Zhang, Affine projection algorithms over distributed wireless networks. J. Commun. Univ. China (Sci. Technol.) 19(2), 34–43 (2012)

    Google Scholar 

  17. Y. Liu, C. Li, Z. Zhang, Diffusion sparse least-mean squares over networks. IEEE Trans. Signal Process. 60(8), 4480–4485 (2012)

    Article  MathSciNet  Google Scholar 

  18. Y. Liu, W.K. Tang, Enhanced incremental LMS with norm constraints for distributed in-network estimation. Signal Process. 94, 373–385 (2014)

    Article  Google Scholar 

  19. Z. Liu, Y. Liu, C. Li, Distributed sparse recursive least-squares over networks. IEEE Trans. Signal Process. 62(6), 1386–1395 (2014)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  22. P.D. Lorenzo, S. Barbarossa, A.H. Sayed, Bio-inspired decentralized radio access based on swarming mechanisms over adaptive networks. IEEE Trans. Signal Process. 61(12), 3183–3197 (2013)

    Article  Google Scholar 

  23. P.D. Lorenzo, A.H. Sayed, Sparse distributed learning based on diffusion adaptation. IEEE Trans. Signal Process. 61(6), 1419–1433 (2013)

    Article  MathSciNet  Google Scholar 

  24. J. Ni, Diffusion sign subband adaptive filtering algorithm for distributed estimation. IEEE Signal Process. Lett. 22(11), 2029–2033 (2015)

    Article  Google Scholar 

  25. J. Ni, X. Chen, J. Yang, Two variants of the sign subband adaptive filter with improved convergence rate. Signal Process. 96(Part B), 325–331 (2014)

    Article  Google Scholar 

  26. J. Ni, F. Li, Efficient implementation of the affine projection sign algorithm. IEEE Signal Process. Lett. 19(1), 24–26 (2012)

    Article  Google Scholar 

  27. J. Ni, L. Ma, Distributed subband adaptive filtering algorithms. Acta Electron. Sin. 43(11), 2131–2137 (2015)

    Google Scholar 

  28. A.H. Sayed, Adaptation, learning, and optimization over networks. Found. Trends Mach. Learn. 7(4–5), 311–801 (2014)

    Article  MATH  Google Scholar 

  29. A.H. Sayed, Adaptive networks. Proc. IEEE 102(4), 460–497 (2014)

    Article  Google Scholar 

  30. T. Shao, Y.R. Zheng, J. Benesty, An affine projection sign algorithm robust against impulsive interferences. IEEE Signal Process. Lett. 17(4), 327–330 (2010)

    Article  Google Scholar 

  31. Z. Yang, Y.R. Zheng, S.L. Grant, Proportionate affine projection sign algorithms for network echo cancellation. IEEE Trans. Audio Speech Lang. Process. 19(8), 2273–2284 (2011)

    Article  Google Scholar 

  32. H. Zhao, Y. Yu, S. Gao, X. Zeng, Z. He, Memory proportionate APA with individual activation factors for acoustic echo cancellation. IEEE Trans. Audio Speech Lang. Process. 22(16), 1047–1055 (2014)

    Article  Google Scholar 

  33. Y.R. Zheng, V.H. Nascimento, Two variable step-size adaptive algorithms for non-Gaussian interference environment using fractionally lower-order moment minimization. Signal Process. 23, 831–844 (2013)

    MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61471251 and 61101217 and in part by the Natural Science Foundation of Jiangsu Province under Grant BK20131164.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jingen Ni.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, J., Ni, J. Diffusion Sign Subband Adaptive Filtering Algorithm with Enlarged Cooperation and Its Variant. Circuits Syst Signal Process 36, 1714–1724 (2017). https://doi.org/10.1007/s00034-016-0371-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-016-0371-y

Keywords

Navigation