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Kernelized set-membership approach to nonlinear adaptive filtering

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4 Author(s)
Malipatil, A.V. ; Dept. of Electr. Eng., Notre Dame Univ., IN, USA ; Yih-Fang Huang ; Andra, S. ; Bennett, K.

In linear filtering, the set-membership normalized least mean squares (SM-NLMS) algorithm has been shown to exhibit desirable features of selective update and optimized variable step size. In this paper, a kernel approach to the SM-NLMS algorithm is presented that makes it feasible to address nonlinear problems. An online greedy approximation technique to achieve sparsity is discussed. Simulation results are presented for two practical problems: equalization of nonlinear inter-symbol interference (ISI) channels and predistortion of nonlinear high power amplifiers (HPA).

Published in:

Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on  (Volume:4 )

Date of Conference:

18-23 March 2005