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Complex-valued SRFNN with Decision Feedback for QAM signalling systems

This paper proposes a novel adaptive decision feedback equalizer (DFE) based on self-constructing recurrent fuzzy neural network (SRFNN) for quadrature amplitude modulation systems. Without the prior knowledge of channel characteristics, a novel training scheme containing both selfconstructing learning and back-propagation algorithms is derived for the SRFNN. The proposed DFE is compared with several neural network (NN)-based DFEs on a nonlinear complex-valued channel. The results show that the SRFNN DFE is superior to classical NN DFEs in terms of symbol-error rate and convergence speed. Keywords—Adaptive equalizers, ISI, MLP, TSK