Unscented Kalman Filter-Trained Recurrent Neural Equalizer for Time-Varying Channels

Recurrent neural networks have been successfully applied to communications channel equalization because of their capability of modelling nonlinear dynamic systems. The major problems of gradient descent learning techniques, commonly employed to train recurrent neural networks, are slow convergence rates and long training sequences. This paper presents a decision feedback equalizer using a recurrent neural network trained with the unscented …