A comparison between recurrent neural network architectures for digital equalization
J.D. Ortiz-Fuentes, Mikel L. Forcada · 2002
This paper shows a comparison between three different first-order recurrent neural network (RNN) architectures (fully recurrent, partially recurrent, and Elman (1990)), trained using the real-time recurrent learning (RTRL) algorithm and the GSM training sequence ratio (26/114) for digital equalization of 2-ary PAM signals. The results show no substantial effect of the particular architecture or the number of units on the overall performance. This is due to the assumption of a suboptimal equalization scheme by the RNNs, because of the learning algorithm. The results are compared to those obtained using a classical (decision-feedback equalizer) approach.