Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30075
New Data Reuse Adaptive Filters with Noise Constraint

Authors: Young-Seok Choi

Abstract:

We present a new framework of the data-reusing (DR) adaptive algorithms by incorporating a constraint on noise, referred to as a noise constraint. The motivation behind this work is that the use of the statistical knowledge of the channel noise can contribute toward improving the convergence performance of an adaptive filter in identifying a noisy linear finite impulse response (FIR) channel. By incorporating the noise constraint into the cost function of the DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive algorithms are derived. Experimental results clearly indicate their superior performance over the conventional DR ones.

Keywords: Adaptive filter, data-reusing, least-mean square (LMS), affine projection (AP), noise constraint.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339059

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 957

References:


[1] A. H. Sayed, Fundamentals of Adaptive Filtering, New York: Wiley, 2003.
[2] B. A. Schnaufer and W. K. Jenkins, “New data-reusing LMS algorithms for improved covergence,” in Proc. Asilomar Conf., Pacific Groves, CA, pp. 1584–1588, May 1993,
[3] R. A. Soni, K. A. Gallivan and W. K. Jenkins, “Convergence properties of affine projection and normalized data reusing methods,,” in Proc. Asilomar Conf., vol. 2, pp. 1166–1170, Nov. 1998,
[4] K. Ozeki and T. Umeda, “An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties,” Electro. Commun. Jpn., vol. 67-A, no. 5, pp. 19–27, 1984.
[5] H.-C. Shin and A. H. Sayed, “Mean-square performance of a family of affine projection algorithms,” IEEE Trans. Signal Process., vol. 12, no. 1, pp. 90–102, Jan. 2004.
[6] Y. Wei, S. B. Gelfand and J. V. Krogmeier, “Noise-constrained least mean squares algorithm,” IEEE Trans. Signal Process., vol. 49, no. 9, pp. 1961–1970, Sep. 2001.
[7] A. Zerguine, M. Moinuddin and S. A. A. Imam, “A noise constrained least mean fourth adaptive algorithm,” Signal Process., vol. 91, no. 1, pp. 136–149, Jan. 2011.
[8] H.-C. Shin, W.-J. Song and A. H. Sayed, “Mean-square performance of data-reusing adaptive algorithms,” IEEE Trans. Signal Process., vol. 12, no. 12, pp. 851–854, Dec. 2005.
[9] M. Dufflo, Random Iteratie Models, Springer-Verlag, Berlin, Germany, 1997.
[10] K.-Y. Hwang and W.-J. Song, “An affine projection adaptive filtering algorithm with selective regressors,” IEEE Trans. Circuits Syst. II, vol. 54, no. 1, pp. 43–46, Jan. 2007.
[11] S.-E. Kim, Y.-S. Choi, M.-K. Song and W.-J. Song, “A subband adaptive filtering algorithm employing dynamic selection of subband filters,” IEEE Trans. Signal Process., vol. 17, no. 3, pp. 245–248, Mar. 2010.