Adaptive complex modified probabilistic neural network in digital channel equalization

A novel adaptive technique is proposed for the complex-valued modified probabilistic neural network (MPNN). The adaptive feature is desirable when using the MPNN in channel equalization to track time-varying channels. The MPNN is initially trained using the clustering technique. When training is completed, the network is switched to decision-directed mode and the network parameters are adapted using stochastic gradient-based algorithms in an unsupervised manner. Simulations show that the equalizer was able to efficiently equalize 4-QAM symbol sequences transmitted through nonlinear, slowly time-varying channels.

[1]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[2]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[3]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[4]  Sheng Chen,et al.  A clustering technique for digital communications channel equalization using radial basis function networks , 1993, IEEE Trans. Neural Networks.

[5]  Sheng Chen,et al.  Complex-valued radial basis function networks , 1993 .

[6]  Anthony Zaknich,et al.  Center reduction algorithm for the modified probabilistic neural network equalizer , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[7]  Anthony Zaknich,et al.  Introduction to the modified probabilistic neural network for general signal processing applications , 1998, IEEE Trans. Signal Process..

[8]  F.-C. Chen,et al.  On the learning and convergence of the radial basis networks , 1993, IEEE International Conference on Neural Networks.

[9]  Sammy Siu,et al.  Multilayer perceptron structures applied to adaptive equalisers for data communications , 1989, International Conference on Acoustics, Speech, and Signal Processing,.