A critical assessment of recurrent artificial neural networks as adaptive equalizers in digital communications

A number of neural network structures have previously been applied to the problem of equalization of digital communications channels and view the problem as one of pattern classification rather than one of inverse filtering. The recurrent neural network (RNN) has previously been shown to outperform the conventional linear transversal equalizer structure and has the advantage of requiring a small number of nodes to achieve a given level of equalization. The paper aims to highlight the mechanism by which RNNs equalize channels and to show that the dynamics of such networks create a structure unsuitable for reliable equalization.