New Data Reuse Adaptive Filters with Noise Constraint

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.

Authors:

• Young-Seok Choi

Keywords:

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

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