期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 13, 期 3, 页码 687-696出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2002.1000133
关键词
channel equalization; complex minimal resource allocation network (CMRAN); quadrature amplitude modulation (QAM); radial basis function (RBF) neural network
In this paper, a complex radial basis function neural network is proposed for equalization of quadrature amplitude modulation (QAM) signals in communication channels. The network utilizes a sequential learning algorithm referred to as complex minimal resource allocation network (CMRAN) and is an extension of the MRAN algorithm originally developed for online learning in real-valued radial basis function (111317) networks. CMRAN has the ability to grow and prune the (complex) RBF network's hidden neurons to ensure a parsimonious network structure. The performance of the CMRAN equalizer for nonlinear channel equalization problems has been evaluated by comparing it with the functional link artificial neural network (FLANN) equalizer of Patra et al. and the Gaussian stochastic gradient (SG) RBF equalizer of Cha and Kassam. The results clearly show that CMRANs performance is superior in terms of symbol error rates and network complexity.
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