NEURAL NETWORKS FOR MODELING NONLINEAR MEMORYLESS COMMUNICATION CHANNELS

This paper presents a neural network approach for modeling nonlinear m emoryless communication channels, In particular, the paper studies the approximation of the nonlinear characteristics of traveling-wave tube (TWT) amplifiers used in satellite communications, The modeling is ba sed upon multilayer neural networks, trained by the odd and even backp ropagation (BP) algorithms, Simulation results demonstrate that neural network models fit the experimental data better than classical analyt ical TWT models.