Abstract
Kernel-based methods have had hefty success in a wide range of fields over the past decade, they are founded on the robust mathematical framework of reproducing kernel Hilbert spaces (RKHS), this space provides an interesting framework for the development of adaptive nonlinear filters. In this paper, we present a comparative study between the kernel method in Hilbert space with a reproducing kernel, and linear adaptive algorithms that is least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS) algorithms. Simulation results show excellent performance of the kernel algorithm for identification of single-input single-output (SISO) systems, compared to the linear adaptive algorithms, this by adopting the very fast fading channels called Broadband Radio Access Network (BRAN A and BRAN C).
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