This article is part of the series Advances in Subspace-Based Techniques for Signal Processing and Communications.

Open Access Research Article

New Approaches for Channel Prediction Based on Sinusoidal Modeling

Ming Chen1*, Torbjörn Ekman2 and Mats Viberg1

Author Affiliations

1 Department of Signals and Systems, Chalmers University of Technology, Göteborg, SE 412 96 , Sweden

2 Department of Electronics and Telecommunications, Norwegian Institute of Science and Technology, Trondheim NO-7491, Norway

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EURASIP Journal on Advances in Signal Processing 2007, 2007:049393  doi:10.1155/2007/49393


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2007/1/049393


Received: 4 December 2005
Revisions received: 4 April 2006
Accepted: 30 April 2006
Published: 7 September 2006

© 2007 Chen et al.

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.

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.

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