Performances of decision feedback equalizers using neural networks under frequency selective fading channels

Evaluates the performance of decision feedback equalizers (DFE) using multilayer neural networks under frequency selective fading channels. A novel DFE is proposed. The proposed DFE uses a neural network which carries out unsupervised learning selectively in a tracking mode. The neural network used can avoid false learning caused by incorrect teacher signals by setting the appropriate threshold to decide whether the learning should be carried out or not. The fading channel to be considered is frequency selective and its statistical characteristics are Rayleigh. Simulation results show that the performance of the DFE using the conventional neural network is superior to that of the conventional DFE and also show that the performance of the proposed DFE is superior to that of the DFE using the conventional neural network.<>