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Table 3 The computational complexity of NSAF and SR-NSAF

From: A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal

Computation Multiplications
\( {x}_i(n)={\mathbf{f}}_i^T\mathbf{x}(n) \). The input signal, x(n), is K × 1 NK
\( {d}_i(n)={\mathbf{f}}_i^T\mathbf{d}(n) \). The desired signal, d(n), is K × 1 NK
\( e(n)={\sum}_{i=0}^{N-1}{\mathbf{g}}_i^T{e}_i(n) \) NK
\( {e}_{i,D}(k)={d}_{i,D}(k)-{\mathbf{x}}_i^T(k)\mathbf{w}(k) \) M
\( \mathbf{w}\left(k+1\right)=\mathbf{w}(k)+\mu {\sum}_{i=0}^{N-1}\frac{{\mathbf{x}}_i(k)}{{\left\Vert {\mathbf{x}}_i(k)\right\Vert}^2}{e}_{i,D}(k) \) M + 1
\( \mathbf{w}\left(k+1\right)=\mathbf{w}(k)+\mu {\sum}_{i=0}^{N-1}\frac{\operatorname{sgn}\left[{\mathbf{x}}_i(k)\right]}{{\left\Vert {\mathbf{x}}_i(k)\right\Vert}_1}{e}_{i,D}(k) \) 1
Total Complexity for NSAF M + 3NK + 1
Total Complexity for SR-NSAF M + 3NK + 1