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Table 4 The D-CMLF algorithm

From: Sequential convex combinations of multiple adaptive lattice filters in cognitive radio channel identification

Stage inputs  
\( \gamma ^{f}_{\ell -1,1}(n)=\gamma _{\ell -1}(n-1), \gamma ^{b}_{\ell -1,1}(n)=\gamma _{\ell -1}(n)\) (T.4.1)
\( r^{b}_{\ell -1,k}(-1)=r^{f}_{\ell -1,k}(-1)=\delta, (k=1,\ldots,M) \) (T.4.2)
\( \Delta ^{e}_{\ell,k}(-1)=\Delta ^{f}_{\ell,k}(-1)=\Delta ^{b}_{\ell,k}(-1)= \Delta ^{eq}_{\ell }(0)=0.0, (k=1,\ldots,M) \) (T.4.3)
For k = 1,…, M  
Computations at SOPs  
\( r^{b}_{\ell -1,k}(n) = \lambda _{k} \ r^{b}_{\ell -1,k}(n-1) + \gamma ^{b}_{\ell -1,k}(n) \ \mid b_{\ell -1}^{k}(n) \mid ^{2} \) (T.4.4)
\( \gamma ^{b}_{\ell -1,k+1}(n)=\gamma ^{b}_{\ell -1,k}(n) - \mid \gamma ^{b}_{\ell -1,k}(n) \mid ^{2} \mid b_{\ell -1}^{k}(n)\mid ^{2}/r^{b}_{\ell -1,k}(n) \) (T.4.5)
\( r^{f}_{\ell -1,k}(n) = \lambda _{k} \ r^{f}_{\ell -1,k}(n-1) + \gamma ^{f}_{\ell -1,k}(n) \ \mid f_{\ell -1}^{k}(n) \mid ^{2} \) (T.4.6)
\( \gamma ^{f}_{\ell -1,k+1}(n)=\gamma ^{f}_{\ell -1,k}(n) - \mid \gamma ^{f}_{\ell -1,k}(n) \mid ^{2} \mid f_{\ell -1}^{k}(n) \mid ^{2}/r^{f}_{\ell -1,k}(n) \) (T.4.7)
Joint process estimation (ROP)  
\( e_{\ell }^{k}(n)=e_{\ell -1}^{k}(n) - \Delta ^{e^{*}}_{\ell,k}(n-1) \ b_{\ell -1}^{k}(n) \) (T.4.8)
\( \Delta ^{e}_{\ell,k}(n)= \alpha (\Delta ^{e}_{\ell,k}(n-1) + \gamma ^{b}_{\ell -1,k}(n) e^{k^{\ast }}_{\ell }(n) b^{k}_{\ell -1}(n)/r^{b}_{\ell -1,k}(n)) + (1 - \alpha) \Delta ^{eq}_{\ell }(n-1) \) (T.4.9)
Combination processing (type-3 processor)  
\( e_{\ell }^{eq}(n) = e_{\ell }^{eq}(n) + v_{\ell }^{k}(n) \ e_{\ell }^{k}(n) \) (T.4.10)
\( \Delta ^{eq}_{\ell }(n) = \Delta ^{eq}_{\ell }(n) + v_{\ell }^{k}(n) \ \Delta ^{e}_{\ell,k}(n) \) (T.4.11)
\( a_{\ell }^{k}(n+1)=a_{\ell }^{k}(n)-\mu _{a} \ e^{eq}_{\ell }(n) \ (e_{\ell }^{k}(n) - e^{eq}_{\ell }(n)) \ v_{\ell }^{k}(n) + \rho \ (a^{k}(n)-a^{k}(n-1)) \) (T.4.12)
\( \beta _{\ell }(n+1)= \beta _{\ell }(n+1) + e_{\ell }^{a^{k}(n+1)} \) (T.4.13)
\( v_{\ell }^{k}(n+1)= \frac {e^{a_{\ell }^{k}(n+1)}}{\beta _{\ell }(n+1)} \) (T.4.14)
Forward error prediction (ROP)  
\( f_{\ell }^{k}(n)=f_{\ell -1}^{k}(n) - \Delta ^{f^{*}}_{\ell,k}(n-1) \ b_{\ell -1}^{k}(n-1) \) (T.4.15)
\( \Delta ^{f}_{\ell,k}(n)= \Delta ^{f}_{\ell,k}(n-1) + \gamma ^{b}_{\ell -1,k}(n-1) \ f^{k^{\ast }}_{\ell }(n) b^{k}_{\ell -1}(n-1)/r^{b}_{\ell -1,k}(n-1) \) (T.4.16)
Backward error prediction (ROP)  
\( b^{k}_{\ell }(n)=b_{\ell -1}^{k}(n-1) - \Delta ^{b^{*}}_{\ell,k}(n-1) \ f_{\ell -1}^{k}(n) \) (T.4.17)
\( \Delta ^{b}_{\ell,k}(n)= \Delta ^{b}_{\ell,k}(n-1) + \gamma ^{f}_{\ell -1,k}(n) \ b^{k^{\ast }}_{\ell }(n) f^{k}_{\ell -1}(n)/r^{f}_{\ell -1,k}(n) \) (T.4.18)
End  
Stage outputs  
\( \gamma _{\ell }(n)=\gamma ^{b}_{\ell -1,M+1}(n)\) (T.4.19)