New Approaches for Channel Prediction Based on Sinusoidal Modeling
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Research Article New Approaches for Channel Prediction Based on Sinusoidal Modeling ¨ Ekman,2 and Mats Viberg1 Ming Chen,1 Torbjorn 1 Department
of Signals and Systems, Chalmers University of Technology, SE 412 96 G¨oteborg, Sweden of Electronics and Telecommunications, Norwegian Institute of Science and Technology, NO-7491 Trondheim, Norway
2 Department
Received 4 December 2005; Revised 4 April 2006; Accepted 30 April 2006 Recommended by Kostas Berberidis 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. Copyright © 2007 Ming 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.
1.
INTRODUCTION
Link adaption techniques, such as multiuser diversity, adaptive modulation and coding, and fast scheduling hold great promise to improve spectrum efficiency. However, the improvement on the system capacity depends heavily on the predictability of the short-term channel fades [1, 2]. Extensive studies on this topic were made during the last several years by different researchers [1], [3–10]. It was found that a prediction horizon corresponding to a distance of half a wavelength traveled by the mobile is considered challenging [11]. In this paper, we assume the availability of a vector y = [y(t), y(t − 1), . . . , y(t − N + 1)]T containing the N channel observations (successive estimates of a particular channel coefficient) y = h + e,
Figure 1, where the time index t = 0 and the length of the observation interval is N = 100. The published channel predictors are divided into two categories, which can be categorized as model-free predictors and model-based predictors, respectively. 1.1.
Model-free channel predictors
The first category is essentially the class of linear predictors (LP), where the channel coefficient is predicted as a weighted sum of the previous channel observations. A dth order LP of h(t + L), where d < N, is + L) = h(t
d −1 k=0
βk y(t − k) = βTd yd = flH y,
(2)
(1) where
wher
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