Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article New Approaches for Channel Prediction Based on Sinusoidal Modeling | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 49393 13 pages doi 2007 49393 Research Article New Approaches for Channel Prediction Based on Sinusoidal Modeling Ming Chen 1 Torbjorn Ekman 2 and Mats Viberg1 1 Department of Signals and Systems Chalmers University of Technology SE 412 96 Goteborg Sweden 2 Department of Electronics and Telecommunications Norwegian Institute of Science and Technology NO-7491 Trondheim Norway 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
