A new demixer scheme for blind source separation using general neural network model

There has been a surge of interest in blind source separation (BSS) because of its potential applications in several areas of engineering and science such as wireless systems. We propose a new neural network demixing scheme using a general neural network structure for the BSS problem for the instantaneous mixtures. It is shown that the existing feedforward (FF) and feedback (FB) neural network schemes can be reduced from the new general model. The results demonstrate that the new scheme is more robust and offers superior convergence properties.