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.