Design of an SCRFNN-based nonlinear channel equaliser

Adaptive Fuzzy Neural Filtering for Decision Feedback Equalization and Multi-Antenna Systems

“…However, if the channel has severely nonlinear distortions, classical TE and DFE perform poorly. Generally speaking, the nonlinear equalization techniques proposed to address the nonlinear channel equalization problem are those presented in [14], [16], [17], [22], [32], [35], [39], [44], [54]. Chen et al have derived a Bayesian DFE (BDFE) solution [16], which not only improves performance but also reduces computational cost compared to the Bayesian transversal equalizer (BTE).…”

“…A powerful nonlinear detecting technique called fuzzy neural network (FNN) can make effective use of both easy interpretability of fuzzy logics and superior learning ability of neural networks, hence it has been adopted for equalization problems, e.g. an adaptive neuro fuzzy inference system (ANFIS)-based equalizer [39] and a self-constructing recurrent FNN (SCRFNN)-based equalizer [44]. Multilayer perceptron (MLP)-based equalizers [32], [35] are another kind of detection.…”

“…Moreover, the self-constructing process of SCRFNN and SCFNN can construct a more compact structure due to setting conditions to restrict the generation of a new hidden node, and hence SCRFNN and SCFNN TEs results in lower computational costs compared to traditional RBF and ANFIS TEs. Although the SCRFNN TE and SCFNN TE in [43][44] have provided a scheme to obtain satisfactory BER and SER performance with low computational complexity, it doesn't take advantage of decision feedback signals to improve the detecting capability. In Section 2, a novel DFE structure incorporated with a fast SCFNN learning algorithm is presented.…”

“…In addition, the structures (i.e., the numbers of hidden nodes) of MLP and ANFIS detectors must be fixed and assigned in advance and determined by trial-and-error method. In 2005, the SCRFNN detector and its another version, i.e., self-constructing fuzzy neural network (SCFNN), have been applied to the channel equalization problem [43]- [44]. Specifically, the SCRFNN or SCFNN equalizers perform both self-constructing process and BP learning process simultaneously in the training procedure without the knowledge of channel characteristics.…”

“…Trial-and-error method is an alternative way to determine the number of hidden nodes of RBF. Except the clustering RBF detectors, there are other types of nonlinear detectors, such as multilayer perceptrons (MLPs) [32]- [38], adaptive neuro fuzzy inference system (ANFIS) [39]- [41] and self-constructing recurrent fuzzy neural networks (SCRFNNs) [42]- [44]. Traditionally, MLP and ANFIS detectors are trained by the back-propagation (BP) learning [32], [34], [35], [38], [40].…”