A blind network of extended Kalman filters for nonstationary channel equalization

A blind network of extended Kalman filters (NEKF) is introduced for nonstationary linear channel equalization. The structure of NKF was recently suggested for optimal channel equalization. As the knowledge of the channel is the main constraint within the NKF equalizer, we here propose to extend the state to estimate, that was previously formed by the last M transmitted symbols, to the time-varying channel coefficients. The observation model becomes nonlinear suggesting thus extended Kalman filtering for state estimation. The proposed NEKF algorithm is completely blind towards any learning phase, with fast convergence properties. Compared to the blind Bayesian algorithm proposed by Iltis et al., (1994), the NEKF-based equalizer shows good performance with a much lower complexity.

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