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Improved Optimum Error Nonlinearities Using Cramer–Rao Bound Estimation

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Abstract

In this paper, we propose an efficient design of optimum error nonlinearities (OENL) for adaptive filters which minimizes the steady-state excess mean square error and attains the limit mandated by the Cramer–Rao bound (CRB) of the underlying estimation process. Novelty of the work resides in the fact that the proposed improved optimum error nonlinearities (IOENL) design incorporates the effect of CRB which was ignored in the existing literature. To achieve this, we employ two efficient methods to estimate the variance of a priori estimation error. Therefore, the proposed IOENL does not use any assumption on the distribution of input regressor elements and noise sequence. Neither the assumption of independence on the input regressor is made nor any sort of linearization is assumed. Extensive simulations are done to show the efficiency of the proposed algorithm compared to the standard least mean square algorithm and the standard OENL algorithm.

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References

  1. T. Aboulnasr, K. Mayyas, A robust variable step-size LMS type algorithm: analysis and simulation. IEEE Trans. Signal Process. 45, 631–639 (1997)

    Article  Google Scholar 

  2. T. Aboulnasr, A. Zerguine, Variable weight mixed-norm LMS-LMF adaptive algorithm. IEEE Conf. Signals Syst. Comput. 1, 791–794 (1999)

    Google Scholar 

  3. T.Y. Al-Naffouri, A.H. Sayed, Optimum error nonlinearities for long adaptive filters, in IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP),Orlando, USA (2002)

  4. T.Y. Al-Naffouri, A.H. Sayed, Transient analysis of adaptive filters with error nonlinearities. IEEE Trans. Signal Process. 51(3), 653–663 (2003)

    Article  Google Scholar 

  5. T.Y. Al-Naffouri, A.H. Sayed, Transient analysis of data-normalized adaptive filters. IEEE Trans. Signal Process. 51, 639–652 (2003)

    Article  Google Scholar 

  6. T.Y. Al-Naffouri, A.H. Sayed, T. Kailath, On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms, in IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Istanbul, Turkey, vol 1 (2000), pp. 464–467

  7. M. Arif, I. Naseem, M. Moinuddin, U.M. Al-Saggaf, Design of optimum error nonlinearity for channel estimation in the presence of class-a impulsive noise, in 6th International Conference on Intelligent and Advance System (ICIAS2016)

  8. N.J. Bershad, On error-saturation nonlinearities in LMS adaptation. IEEE Trans. Acoust. Speech Signal Process. 36(4), 440–452 (1988)

    Article  Google Scholar 

  9. N.J. Bershad, M. Bonnet, Saturation effects in LMS adaptive echo cancellation for binary data. IEEE Trans. Acoust. Speech Signal Process. 38, 1687–1696 (1990)

    Article  Google Scholar 

  10. P. Bouboulis, S. Theodoridis, The complex Gaussian Kernel LMS algorithm, in International Conference on Artificial Neural Networks (ICANN) (2010), pp. 11–20

  11. P. Bouboulis, S. Theodoridis, Extension of Wirtingers calculus to reproducing kernel Hilbert spaces and the complex kernel LMS. IEEE Trans. Signal Process. 59(3), 964–978 (2011)

    Article  MathSciNet  Google Scholar 

  12. J.A. Chambers, O. Tanrikulu, A.G. Constantinides, Least mean mixed-norm adaptive filtering. Electron. Lett. 30(19), 1574–1575 (1994)

    Article  Google Scholar 

  13. Y.R. Chien, W.J. Tseng, Switching-based variable step-size approach for partial update LMS algorithms. Electron. Lett. 9(17), 1081–1083 (2013)

    Article  MathSciNet  Google Scholar 

  14. T. Claasen, W. Mecklenbr, Comparison of the convergence of two algorithms for adaptive fir digital filters. IEEE Trans. Circuits Syst. 28(6), 510–518 (1981)

    Article  Google Scholar 

  15. C. Cowan, P. Grant, Adaptive Filters (Prentice Hall, Englewood Cliffs, 1985)

    MATH  Google Scholar 

  16. S. Douglas, T. Meng, Stochastic gradient adaptation under general error criteria. IEEE Trans. Signal Process. 42(6), 1352–1365 (1994)

    Article  Google Scholar 

  17. D.L. Duttweiler, Adaptive filter performance with nonlinearities in the correlation multiplier. IEEE Trans. Acoust. Speech Signal Process. 30(4), 578–586 (1982)

    Article  Google Scholar 

  18. E. Eweda, Comparison of RLS, LMS, and sign algorithms for tracking randomly time-varying channels. IEEE Trans. Signal Process. 42, 2937–2944 (1994)

    Article  Google Scholar 

  19. W.A. Gardner, Learning characteristic of stochastic-descent algorithms: a general study, analysis, and critique. Signal Process. 6(2), 113–133 (1984)

    Article  MathSciNet  Google Scholar 

  20. J. Gibson, S. Gray, MVSE adaptive filtering subject to a constraint on MSE. IEEE Trans. Circuits Syst. 35(5), 603–608 (1988)

    Article  Google Scholar 

  21. R. Gitlin, H. Meadors, S. Weinstein, The tap-leakage algorithm: an algorithm for the stable operation of a digitally implemented, fractionally spaced adaptive equalizer. Bell Syst. Tech. J. 61, 1817–1839 (1982)

    Article  Google Scholar 

  22. I.S. Gradshteyn, I.M. Ryzhik, Table of Integral, Series, and Products, Corrected and Enlarged Edition (Academic Press, INC, New York, 1980)

    MATH  Google Scholar 

  23. R.W. Harris, D. Chabries, F.A. Bishop, A variable stepsize (vs) algorithm. IEEETrans. Acoust. Speech Signal Process. 34, 499–510 (1986)

    Article  Google Scholar 

  24. S. Haykin, Adaptive Filter Theory (Prentice Hall, Englewood Cliffs, 1996)

    MATH  Google Scholar 

  25. S. Koike, Convergence analysis of a data echo canceler with a stochastic gradient adaptive fir filter using the sign algorithm. IEEE Trans. Signal Process. 43(12), 2852–2861 (1995)

    Article  Google Scholar 

  26. R.H. Kwong, E.W. Johnston, A variable stepsize LMS algorithm. IEEE Trans. Signal Process. 40, 1633–1642 (1992)

    Article  Google Scholar 

  27. W. Liu, J.C. Principe, S. Haykin, Kernel Adaptive Filtering : A Comprehensive Introduction (Wiley, New Jersey, 2010)

    Book  Google Scholar 

  28. V. Mathews, S. Cho, Improved convergence analysis of stochastic gradient adaptive filters using the sign algorithm. IEEE Trans. Acoust. Speech Signal Process. 35(4), 450–454 (1987)

    Article  Google Scholar 

  29. V.J. Methews, Z. **e, A stochastic gradient adaptive filter with gradient adaptive step size. IEEE Trans. Signal Process. 41, 2075–2087 (1993)

    Article  Google Scholar 

  30. M. Moinuddin, A. Zerguine, A.U.H. Sheikh, Multiple access interference plus noise constrained least mean square (MNCLMS) algorithm for CDMA systems. IEEE Trans. Circuit Syst. 55(9), 2870–2883 (2008)

    Article  MathSciNet  Google Scholar 

  31. J. Nagumo, A. Noda, A learning method for system identification. IEEE Trans. Autom. Control AC–12, 282–287 (1967)

    Article  Google Scholar 

  32. K. Ozeki, T. Umeda, An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties. IEICE Trans. Jpn. J67–A, 126–132 (1984)

    MathSciNet  Google Scholar 

  33. A.H. Sayed, Fundamentals of Adaptive Filtering (Wiley, New Jersey, 2008)

    Google Scholar 

  34. A.H. Sayed, M. Rupp, Robustness Issues in Adaptive Filtering, DSP Handbook (CRC Press, Boca Raton, 1998)

    Google Scholar 

  35. W.A. Sethares, Adaptive algorithms with nonlinear data and error functions. IEEE Trans. Signal Process. 40(9), 2199–2206 (1992)

    Article  Google Scholar 

  36. T.J. Shan, T. Kailath, Adaptive algorithms with an automatic gain control feature. IEEE Trans. Acoust. Speech Signal Process. 35, 122–127 (1988)

    Google Scholar 

  37. E. Walach, B. Widrow, The least mean fourth (LMF) adaptive algorithm and its family. IEEE Trans. Inf. Theory 30(2), 275–283 (1984)

    Article  Google Scholar 

  38. Y. Wei, S.B. Gefand, J.V. Krogmeier, Noise constrained least mean squares algorithm. IEEE Trans. Signal Process. 49, 1961–1970 (2010)

    Google Scholar 

Download references

Acknowledgements

This project was funded by the Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, under grant No. (CEIES-18-04-01). The authors, therefore, acknowledge the technical and financial support of CEIES. The authors also acknowledge the support of Karachi Institute of Economics and Technology (PAF-KIET) Pakistan in facilitating this research.

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Correspondence to Imran Naseem.

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Arif, M., Naseem, I., Moinuddin, M. et al. Improved Optimum Error Nonlinearities Using Cramer–Rao Bound Estimation. Circuits Syst Signal Process 38, 5169–5186 (2019). https://doi.org/10.1007/s00034-019-01114-0

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  • DOI: https://doi.org/10.1007/s00034-019-01114-0

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