Kalman filtering with real-time applications
Kalman filtering with real-time applications
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
IEEE Transactions on Wireless Communications
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Joint data QR-detection and Kalman estimation for OFDM time-varying Rayleigh channel complex gains
IEEE Transactions on Communications
An EM-Based Forward-Backward Kalman Filter for the Estimation of Time-Variant Channels in OFDM
IEEE Transactions on Signal Processing - Part II
Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation
IEEE Transactions on Signal Processing
Autoregressive modeling for fading channel simulation
IEEE Transactions on Wireless Communications
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This letter deals with the estimation of a flat fading Rayleigh channel with Jakes's spectrum. The channel is approximated by a first-order autoregressive (AR(1)) model and tracked by a Kalman filter (KF). The common method used in the literature to estimate the parameter of the AR(1) model is based on a correlation matching (CM) criterion. However, for slow fading variations, another criterion based on the minimization of the asymptotic variance (MAV) of the KF is more appropriate, as already observed in few works (Barbieri et al., 2009 [1]). This letter gives analytic justification by providing approximated closed-form expressions of the estimation variance for the CM and MAV criteria, and of the optimal AR(1) parameter.