Discriminative learning strategy for efficient neural decision feedback equalizers

Neural networks add a degree of flexibility to the design of equalizers for digital communications. In this work several decision-feedback (DF) neural equalizers (DFNE) are compared with classical DF equalizers and Viterbi demodulators. It is shown that the choice of a cost functional based on the Discriminative Learning (DL), coupled with a fast training paradigm, can provide neural equalizers that outperform the standard DF equalizer (DFE) at practical signal to noise ratio (SNR). Resulting architectures are competitive with the Viterbi solution from cost-performance aspects.