Based on dichotomous coordinate descent (DCD) iterations and with the use of the variable forgetting factor (VFF), a widelylinear (Wl) l1-normrecursiveleastsquares (RlS) adaptivefilteringalgorithm is proposed fo...
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Based on dichotomous coordinate descent (DCD) iterations and with the use of the variable forgetting factor (VFF), a widelylinear (Wl) l1-normrecursiveleastsquares (RlS) adaptivefilteringalgorithm is proposed for sparse underwater acoustic channelequalization. In the proposed l1-norm Wl-RlS algorithm with VFF, the Wl model is employed to exploit the second order statistics of the non-circular signals and the VFF is employed to improve the tracking ability of the RlS algorithms. DCD iterations are incorporated in the proposed l1-norm Wl-RlS-DCD algorithm with VFF to reduce the computing complexity. Moreover, the proposed algorithms are employed by the direct adaptive decision feedback equalizer (DA-DFE). Numerical results indicate that compared with the conventional RlS, l1-norm RlS, Wl-RlS with VFF, l1-norm Wl-RlS-DCD algorithms, the proposed algorithms achieve a better performance in terms of the convergence rate, mean square errorand symbol error ratein the DA-DFE receiver. Experimental results also show that the proposed algorithms can promote the DA-DFE receiver to obtain a better performance in the sparse time-varying underwater acoustic communication system. Even though the transmitted signals are circular quadrature phase-shift keying (QPSK) through the underwater acoustic channel, the proposed adaptive RlS algorithms can still obtain a better performance.
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