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New Approaches for Channel Prediction Based on Sinusoidal Modeling

EURASIP Journal on Advances in Signal Processing20062007:049393

https://doi.org/10.1155/2007/49393

Received: 4 December 2005

Accepted: 30 April 2006

Published: 7 September 2006

Abstract

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.

Keywords

Monte Carlo SimulationWireless CommunicationUrban EnvironmentMoving AverageJoint Moving

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Authors’ Affiliations

(1)
Department of Signals and Systems, Chalmers University of Technology, Göteborg, Sweden
(2)
Department of Electronics and Telecommunications, Norwegian Institute of Science and Technology, Trondheim, Norway

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Copyright

© Ming Chen et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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