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A finite wordlength analysis of an LMS-Newton adaptive filtering algorithm

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3 Author(s)
M. L. R. de Campos ; Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada ; P. S. R. Diniz ; A. Antoniou

The effects of quantization in an least-mean-square (LMS)-Newton adaptive filtering algorithm are investigated. The algorithm considered uses an optimum convergence factor that forces the output a posteriori error to become zero in each iteration. The propagation of errors due to quantization in the internal variables of the algorithm is investigated, and a closed-form formula for the excess mean square error due to quantization is derived. Fixed-point arithmetic is assumed throughout. Several simulations confirm the accuracy of the formulas presented

Published in:

Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on

Date of Conference:

3-6 May 1993