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Communication Channel Equalization- Pattern Recognition or Neural Networks?
2006
2006 International Conference on Communication Technology
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
doi:10.1109/icct.2006.342046
fatcat:mrjkasw53vet7nado43ogwix54