New Approaches for Channel Prediction Based on Sinusoidal Modeling
Journal article, 2007

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.

Author

Ming Chen

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Torbjörn Ekman

Norwegian University of Science and Technology (NTNU)

Mats Viberg

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Eurasip Journal on Applied Signal Processing

1110-8657 (ISSN) 1687-0433 (eISSN)

Vol. 2007

Subject Categories

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1155/2007/49393

More information

Latest update

4/20/2018