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  • Research Article
  • Open Access

New Approaches for Channel Prediction Based on Sinusoidal Modeling

EURASIP Journal on Advances in Signal Processing20062007:049393

  • Received: 4 December 2005
  • Accepted: 30 April 2006
  • Published:


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.


  • Monte Carlo Simulation
  • Wireless Communication
  • Urban Environment
  • Moving Average
  • Joint Moving

Authors’ Affiliations

Department of Signals and Systems, Chalmers University of Technology, Göteborg, SE, 412 96, Sweden
Department of Electronics and Telecommunications, Norwegian Institute of Science and Technology, Trondheim, NO-7491, Norway


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© Ming Chen et al. 2007

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