Skip to main content
Log in

Coefficient-Gradient-Based Individualized Stepsize Adaptation Mechanism for Robust System Identification

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

Abstract

To efficiently reduce the impact of the trading-off between the convergence rate and the quality of identifying a system, and also to improve the robustness of the algorithm against unknown sparsity levels, a Modified Absolute Weighted Input using Log function (MAWILOG) for NLMS algorithm is proposed. The essence of the proposed algorithm is to assign, individually, each coefficient of the adaptive filter a variable stepsize that adapts according to a Log-term that takes advantage of the input power and the input signal underlying each filter coefficient, and adapts to the gradient value of each coefficient magnitude. Due to these attributes, the proposed approach outperforms the proportionate NLMS (PNLMS)-family regardless of sparsity level that is achieved by using the gradient of each coefficient individually to allocate large stepsize values for high gradient coefficients without directly inserting a sparse-aware constraint. Additionally, this technique is capable of overcoming the high computational complexity and high steady-state mean-square-deviation of the (PNLMS)-family. Simulation results of the proposed algorithm versus the comparable algorithms such as the NLMS, PNLMS, improved PNLMS, block-sparse PNLMS, and block-sparse IPNLMS are presented. Results have demonstrated that the proposed algorithm outperforms others in maintaining the lowest steady-state mean-square-deviation while attaining a fast convergence rate under various types of systems.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

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

  2. J. Benesty, C. Paleologu, S. Ciochina, On regularization in adaptive filtering. IEEE/ACM Trans. Audio Speech Lang. Process. 19(6), 1734–1742 (2011)

    Article  Google Scholar 

  3. J. Cui, P.A. Naylor, D.T. Brown, An improved IPNLMS algorithm for echo cancellation in packet-switched networks, in Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, Canada (2004), pp. 141–144

  4. D.L. Duttweiler, Proportionate normalized least-mean squares adaptation in echo cancelers. IEEE/ACM Trans. Audio Speech Lang. Process. 8(5), 508–518 (2000)

    Article  Google Scholar 

  5. H. Deng, M. Doroslovacki, Improving convergence of the PNLMS algorithm for sparse impulse response identification. IEEE Signal Process. Lett. 12(3), 181–184 (2005)

    Article  Google Scholar 

  6. H. Deng, M. Doroslovacki, Proportionate adaptive algorithms for network echo cancellation. IEEE Trans. Signal Process. 54(5), 1794–1803 (2006)

    Article  Google Scholar 

  7. F.D.C. De Souza, O.J. Tobias, R. Seara, Morgan DR (2010) A PNLMS algorithm with individual activation factors. IEEE Trans. Signal Process. 58(4), 2036–2047 (2010)

    Article  MathSciNet  Google Scholar 

  8. S. L. Gay, An efficient, fast converging adaptive filter for network echo cancellation, in Proceedings Thirty-Second Asilomar Conference Signals. Systems and Computers, Monterey, CA (1998), pp. 394–398

  9. O. Gatera, H. Ilhan, A.H. Kayran, A novel LMS-BLM algorithm for AF relays based cooperative wireless networks. Int. J. Electron. Commun. 70(11), 1480–1488 (2016)

    Article  Google Scholar 

  10. S. Haykin, Adaptive Filter Theory, 2nd edn. (Prentice-Hall, Englewood Cliffs, 1996).

    MATH  Google Scholar 

  11. S. Jiang, Y. Gu, Block-sparsity-induced adaptive filter for multi-clustering system identification. IEEE Trans. Signal Process. 63(20), 5318–5330 (2015)

    Article  MathSciNet  Google Scholar 

  12. B. Krstajic, L. Stankovic, Z. Uskokovic, Combined adaptive filter with LMS-Based algorithms. Int. J. Electron. Commun. 57(4), 295–299 (2003)

    Article  Google Scholar 

  13. A. Khalili, A modified non-negative LMS algorithm for online system identification. Int. J. Electron. Commun. 95, 42–46 (2018)

    Article  Google Scholar 

  14. P. Loganathan, A.W.H. Khong, P.A. Naylor, A sparseness controlled proportionate algorithm for acoustic echo cancellation, in 16th European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland (2008), pp. 1–5

  15. P. Loganathan, E.A.P. Habets, P.A. Naylor, A partitioned block proportionate adaptive algorithm for acoustic echo cancellation, in Proceedings of the Second APSIPA Annual Summit and Conference, Biopolis, Singapore (2010), pp. 371–376

  16. J. Liu, S.L. Grant, Proportionate adaptive filtering for block-sparse system identification. IEEE/ACM Trans. Audio Speech Lang. Process. 24(4), 623–630 (2016)

    Article  Google Scholar 

  17. Y. Li, Y. Wang, T. Jiang, Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation. Int. J. Electron. Commun. 70(7), 895–902 (2016)

    Article  Google Scholar 

  18. H.A. Mohamed-Kazim, I. Abdel-Qader, A framework for sparse adaptive system identification via integrating a new multiple time-varying stepsize and zero-attracting techniques, in IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (2018), pp. 33–39

  19. H.A. Mohamed-Kazim, I. Abdel-Qader, A new time adjusting step-size LMS technique for noise cancellation framework with mean square deviation analysis. WSEAS Trans. Syst. 17(30), 264–275 (2018)

    Google Scholar 

  20. H.A. Mohamed-Kazim, I. Abdel-Qader, A fast and robust system identification on compressive sensing signal recovery based on multiple time-vary step-size adaptation technique, in IEEE National Aerospace Electronics Conference (NAECON), Dayton, OH, USA (2019), pp. 379–383

  21. H.A. Mohamed-Kazim, I. Abdel-Qader, A.M. Harb, A fast MPPT algorithm for smart grid-PV connected system based on multiple sparse-aware time-varying stepsize adaptation technique, in IEEE 10th International Renewable Energy Congress (IREC), Sousse, Tunisia (2019)

  22. H.A. Mohamed-Kazim, I. Abdel-Qader, A.M. Harb, Efficient maximum power point tracking based on reweighted zero-attracting variable stepsize for grid interfaced photovoltaic systems. Elsevier J. Comput. Electr. Eng. (CAEE), 85(106672) (2020)

  23. S.J. Park, C.G. Cho, C. Lee, D.H. Youn, S.H. Park, Integrated echo and noise canceler for hands-free applications. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 49(3), 188–195 (2002)

    Article  Google Scholar 

  24. O. Taheri, S.A. Vorobyov, Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis. Signal Process. 104, 70–79 (2014)

    Article  Google Scholar 

  25. T.V. Waterschoot, G. Rombouts, P. Verhoeve, M. Moonen, Doubletalk-robust prediction error identification algorithms for acoustic echo cancellation. IEEE Trans. Signal Process. 55(3), 846–858 (2007)

    Article  MathSciNet  Google Scholar 

  26. Y.H. YuZhao, Proportionate NSAF algorithms with sparseness-measured for acoustic echo cancellation. Int. J. Electron. Commun. 75, 53–62 (2017)

    Article  Google Scholar 

  27. Z. Zecevic, B. Krstajic, M. Radulovic, A new adaptive algorithm for improving the ANC system performance. Int. J. Electron. Commun. 69(1), 442–448 (2015)

    Article  Google Scholar 

  28. W. Zeng, H. Li, X. Zhu, C. Chen, A 2D adaptive beamforming method in sparse array. Int. J. Electron. Commun. 77, 100–104 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the editors and reviewers for their valuable comments and suggestions, which have helped to improve the quality of this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haider A. Mohamed-Kazim.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Availability of Data and Material

All data generated or analyzed during this study are included in this published article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohamed-Kazim, H.A., Abdel-Qader, I. Coefficient-Gradient-Based Individualized Stepsize Adaptation Mechanism for Robust System Identification. Circuits Syst Signal Process 40, 3033–3074 (2021). https://doi.org/10.1007/s00034-020-01612-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-020-01612-6

Keywords

Navigation