Preliminary studies on parameter aided EKF-CRTRL equalizer training for fast fading channels

This paper shows an enhanced training for the EKF-RTRL (Extended Kalman Filter - Real Time Recurrent Learning) single neuron Equalizer using heuristic mechanisms on the training algorithms enabling them to make the training process initial conditions set-up more automatic. The method uses a parameter which evolves accordingly in the training period. The equalizer is used for fast fading selective frequency channels using the WSS_US (Wide Sense Stationary - Uncorrelated Scattering) model. The EKF-RTRL is a symbol by symbol neural equalizer. The performance results here presented depicts several scenarios regarding the channel variation speed. The performance considered in this paper is the symbol error rate (SER).