Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Microwave Mobile Communications
Microwave Mobile Communications
Nonlinear prediction of mobile radio channels: measurements and MARS model designs
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05
Unitary ESPRIT: how to obtain increased estimation accuracy with areduced computational burden
IEEE Transactions on Signal Processing
Long-range channel prediction based on nonstationary parametric modeling
IEEE Transactions on Signal Processing
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Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP) in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS) prediction model and the associated joint least-squares (LS) predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.