论文信息 - Communication Channel Equalization- Pattern Recognition or Neural Networks?

Communication Channel Equalization- Pattern Recognition or Neural Networks?

The communication channel equalization is a difficult problem, especially when the channel is nonlinear and complex. Numerous algorithms are presented in the neural networks literature to solve this problem. In this paper, a comparison is made among the latest neural network techniques (Complex Minimal Resource Allocation Networks (CMRAN) [1]), a classical communication technique (Viterbi algorithm), and two pattern recognition techniques (Support Vector Machine (SVM), Learning Vector Quantization (LVQ)) to solve this problem. The simulation results show that Viterbi (MLSE decoding technique), and SVM methods outperform the CMRAN method.

Wayne Blanding | Satnam Singh | Krishna Pattipati | Vishal Ravindra

[1] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.

[2] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3] Paramasivan Saratchandran,et al. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm , 1998, IEEE Trans. Neural Networks.

[4] Bernhard E. Boser,et al. Pattern Recognition with Optimal Margin Classifiers , 1994 .

[5] Saleem A. Kassam,et al. Channel Equalization Using Adaptive Complex Radial Basis Function Networks , 1995, IEEE J. Sel. Areas Commun..

[6] James A. Bucklew,et al. Support vector machine techniques for nonlinear equalization , 2000, IEEE Trans. Signal Process..

[8] Ganapati Panda,et al. Nonlinear channel equalization for QAM signal constellation using artificial neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

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

[10] B. Mulgrew,et al. Complex-valued radial basis function network, Part II: Application to digital communications channel equalisation , 1994, Signal Process..

[11] Narasimhan Sundararajan,et al. Communication channel equalization using complex-valued minimal radial basis function neural networks , 2002, IEEE Trans. Neural Networks.