A comparative study on the channel modeling using feedforward and recurrent neural network structures

In digital mobile communication, the non-stationary channel linear modeling become insufficient for channel nonlinear variations. The objective of this work is to select a suitable neural structure for the channel modeling. We present the advantages of a new neural structure, which is the modified Elman network (MEN), applied to digital communication problems such us the channel modeling. By comparison with the multilayer perceptron (MLP), we deduce that the MEN structure has proved the same results with MLP but involve much less computational cost