Symbol Decision Equalizer using a Radial Basis Functions Neural Network
نویسندگان
چکیده
This paper presents the problem of multiple quadrature amplitude modulated signals equalization and argues the use of a radial basis functions neural network (RBF-NN) equalizer. Different competitive learning algorithms for the RBF-NN centres determination are discussed. A new competitive learning algorithm is introduced, the rival penalized competitive learning, which rewards the winner and penalizes its first rival. The results of simulations performed in different conditions, are presented showing that the performance of the RBF-NN equalizer, which is based on this new algorithm, is better if compared with other competitive algorithms. Key-Words: communication channels, complex equalizer, quadrature amplitude modulated signals, radial basis functions neural network, competitive learning algorithms, centres vectors
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