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.
Abstract: This paper suggests a new Affine Projection (AP) algorithm with variable data-reuse factor using the condition number as a decision factor. To reduce computational burden, we adopt a recently reported technique which estimates the condition number of an input data matrix. Several simulations show that the new algorithm has better performance than that of the conventional AP algorithm.
Abstract: In this paper, a new pseudo affine projection (AP)
algorithm based on Gauss-Seidel (GS) iterations is proposed for
acoustic echo cancellation (AEC). It is shown that the algorithm is
robust against near-end signal variations (including double-talk).