Complex Kalman Filter Trained Recurrent Neural Network Based Equalizer for Mobile Channels

This paper presents a two state neural equalizer for mobile channels. The channel is modeled by the wide sense stationary - uncorrelated scattering (WSS-US) channel which is known to be an adequate model for wireless applications. The neural equalizer is trained by an extended Kalman filter in order to speed up the equalizer training. Simulation results are also shown in the paper for several scenarios indicating a good trade-off in performance and computational complexity. Comparisons involving traditional equalizers such as decision feedback equalizers (DFE) are also shown indicating that the proposed equalizer outperforms DFE equalizers. On the other hand, the proposed neural equalizer is outperformed by per survivor processing the (PSP) class of equalizers which are much more computational complex than the neural class of equalizers proposed in this paper.