Complex-valued B-spline neural network and its application to iterative frequency-domain decision feedback equalization for Hammerstein communication systems

Abstract

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identification and inversion of Hammerstein systems. Compared to its conventional CV polynomial based counterpart, CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. In this paper, we review the optimality of CV B-spline neural network approach and demonstrate its excellent approximation capability for a real-world application. More specifically, we develop a CV B-spline neural network based approach for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Advantages of B-spline neural network approach as compared to polynomial based modeling approach are extensively discussed, and the effectiveness of CV neural network based NIFDDFE is demonstrated in a simulation study.

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