{"title":"Data-Reusing Adaptive Filtering Algorithms with Adaptive Error Constraint","authors":"Young-Seok Choi","volume":109,"journal":"International Journal of Electronics and Communication Engineering","pagesStart":163,"pagesEnd":167,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10003974","abstract":"We present a family of data-reusing and affine<br \/>\r\nprojection algorithms. For identification of a noisy linear finite<br \/>\r\nimpulse response channel, a partial knowledge of a channel,<br \/>\r\nespecially noise, can be used to improve the performance of<br \/>\r\nthe adaptive filter. Motivated by this fact, the proposed scheme<br \/>\r\nincorporates an estimate of a knowledge of noise. A constraint, called<br \/>\r\nthe adaptive noise constraint, estimates an unknown information of<br \/>\r\nnoise. By imposing this constraint on a cost function of data-reusing<br \/>\r\nand affine projection algorithms, a cost function based on the adaptive<br \/>\r\nnoise constraint and Lagrange multiplier is defined. Minimizing the<br \/>\r\nnew cost function leads to the adaptive noise constrained (ANC)<br \/>\r\ndata-reusing and affine projection algorithms. Experimental results<br \/>\r\ncomparing the proposed schemes to standard data-reusing and affine<br \/>\r\nprojection algorithms clearly indicate their superior performance.","references":"[1] S. Haykin, Adaptive Filter Theory, 4th edition, Upper Saddle River, NJ:\r\nPrentice Hall, 2002. [2] H.-C. Shin, W.-J. Song and A. H. Sayed, \u201cMean-square performance\r\nof data-reusing algorithms,\u201d IEEE Signal Processing Lett., vol. 12,\r\npp. 851\u2013854, Dec. 2005.\r\n[3] B. A. Schnaufer and W. K. Jenkins, \u201cNew data-reusing LMS algorithms\r\nfor improved convergence,\u201d in Proc. Asilomar Conf., Pacific Groves,\r\nCA, May 1993, pp. 1584\u20131588.\r\n[4] K. Ozeki and T. Umeda, \u201cAn adaptive filtering algorithm using an\r\northogonal projection to an affine subspace and its properties,\u201d Electro.\r\nCommun. Jpn., vol. 67-A, no. 5, pp. 19\u201327, 1984.\r\n[5] H.-C. Shin and A. H. Sayed, \u201cMean-square performance of a family of\r\naffine projection algorithms,\u201d IEEE Trans. Signal Processing, vol. 52,\r\npp. 90\u2013102, Jan. 2004.\r\n[6] Y. Wei, S. B. Gelfand and J. V. krogmeier, \u201cNoise-constrained least\r\nmean square algorithm,\u201d IEEE Trans. Signal Processing, vol. 49, No. 9,\r\npp. 1961\u20131970, Sep. 2001.\r\n[7] S. Y. Choi, T.-W. Lee and D. S. Hong, \u201cAdaptive error-constrained\r\nmethod for LMS algotihms and applications,\u201d Signal Processing, vol. 85,\r\npp. 1875\u20131897, Oct. 2005.\r\n[8] H.-C. Shin, A. H. Sayed and W.-J. Song, \u201cVariable step-size NLMS and\r\naffine projection algorithms,\u201d IEEE Signal Processing Lett., vol. 11,\r\npp. 132\u2013135, Feb. 2004.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 109, 2016"}