Application of recurrent neural networks to communication channel equalization
M.J. Bradley, P. Mars · 2002
The paper examines the mechanism by which recurrent neural networks (RNNs) achieve equalization whilst operating on simple digital communication channels. The mode of operation is seen to be essentially similar to the conventional decision feedback equalizer (DFE) and the RNN node nonlinearity is identified as a limiting factor. Two versions of an alternative RNN structure are formulated for channels with longer impulse responses based on soft-decision feedback. Simulations demonstrate the improved BER performance compared with the DFE.