Decision feedback equalizers based on two weighted neural network

In this work, we present new decision-feedback equalizers based on two weighted neural networks. It is shown that the choice of an innovative cost functional based on the discriminative learning (DL) technique, coupled with a fast training paradigm, can provide neural equalizers that outperform standard decision feedback equalizers (decision feedback Es) at a practical signal to the noise ratio (SNR). In particular, the novel neural sequence detector (NSD) is introduced, which allows extending of the concepts of Viterbi-like sequence estimation to neural architectures. Resulted architectures are competitive with the Viterbi solution from cost-performance aspects, as demonstrated in experimental tests.