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New Approaches for Channel Prediction Based on Sinusoidal Modeling
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 049393 (2006)
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.
Duel-Hallen A, Hu S, Hallen H: Long-range prediction of fading signals: enabling adaptive transmission for mobile radio channels. IEEE Signal Processing Magazine 2000,17(3):62–75. 10.1109/79.841729
Falahati S, Svensson A, Ekman T, Sternad M: Adaptive modulation systems for predicted wireless channels. IEEE Transactions on Communications 2004,52(2):307–316. 10.1109/TCOMM.2003.822715
Andersen JB, Jensen J, Holdt S, Frederiksen F: Prediction of future fading based on past measurements. Proceedings of the 50th Vehicular Technology Conference (VTC '99), September 1999, Amsterdam, The Netherlands 1: 151–155.
Chen M, Viberg M: LMMSE channel predictor based on sinusoidal modeling. Proceedings of IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM '04), July 2004, Barcelona, Spain 377–381.
Hwang J-K, Winters JH: Sinusoidal modeling and prediction of fast fading processes. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '98), November 1998 2: 892–897.
Semmelrodt S, Kattenbach R: Performance Analysis and Comparison of Different Fading Forecast Schemes for Flat Fading Radio Channels. COST 273 TD(03)045, Barcelona, Spain, January 2003
Ekman T, Sternad M, Ahlén A: Unbiased power prediction of Rayleigh fading channels. Proceedings of IEEE Vehicular Technology Conference (VTC '02), September 2002, Vancouver, British Columbia, Canada 1: 280–284.
Chen M, Ekman T, Viberg M: Two new approaches for channel prediction based on sinusoidal modeling. EURASIP Journal on Applied Signal Processing, special issue on advances in subspace-based techniques for signal processing and communications, 2006
Ekman T: An FIR predictor interpretation of LS estimation of sinusoidal amplitudes followed by extrapolation. Proceedings of the 12th European Signal Processing Conference (EUSIPCO '04), September 2004, Vienna, Austria
Chen M: Channel prediction based on sinusoidal modeling, Licentiate thesis.
Sternad M, Ekman T, Ahlén A: Power prediction on broadband channels. Proceedings of IEEE Vehicular Technology Conference (VTC '01), May 2001, Rhodes, Greece 4: 2328–2332.
Jakes WC Jr.: Microwave Mobile Communications. IEEE Press, Piscataway, NJ, USA; 1974.
Svantesson T, Wallace JW, Semmelrodt S: Performance evaluation of MIMO channel prediction algorithms using measurements. Proceedings of the 13th IFAC Symposium on System Identification, August 2003, Rotterdam, The Netherlands
Ekman T, Kubin G: Nonlinear prediction of mobile radio channels: measurements and MARS model designs. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 5: 2667–2670.
Lütkepol H: Handbook of Matrices. John Wiley & Sons, Chichester, UK; 1996.
Schmidt RO: A signal subspace approach to multiple emitter location and spectral estimation, Ph.D. dissertation.
Barabell AJ: Improving the resolution performance of eigenstructure-based direction-finding algorithms. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '83), May 1983, Boston, Mass, USA 1: 336–339.
Kumaresan R, Scharf LL, Shaw AK: An algorithm for pole-zero modeling and spectral analysis. IEEE Transactions on Acoustics, Speech, and Signal Processing 1986, 34: 637–640. 10.1109/TASSP.1986.1164843
Roy R, Paulraj A, Kailath T: ESPRIT—a subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Transactions on Acoustics, Speech, and Signal Processing 1986,34(5):1340–1342. 10.1109/TASSP.1986.1164935
Haardt M, Nossek JA: Unitary ESPRIT: how to obtain increased estimation accuracy with a reduced computational burden. IEEE Transactions on Signal Processing 1995,43(5):1232–1242. 10.1109/78.382406
Kay SM: Fundamentals of Statistical Signal Processing, Estimation Theory. Prentice-Hall, Upper Saddle River, NJ, USA; 1993.
Stoica P, Söderström T: Statistical analysis of MUSIC and subspace rotation estimates of sinusoidal frequencies. IEEE Transactions on Signal Processing 1991,39(8):1836–1847. 10.1109/78.91154
Tichavsky P: High-SNR asymptotics for signal-subspace methods in sinusoidal frequency estimation. IEEE Transactions on Signal Processing 1993,41(7):2448–2460. 10.1109/78.224253
Akaike H: Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics 1969, 21: 243–247. 10.1007/BF02532251
Rissanen J: Modeling by shortest data description. Automatica 1978,14(5):465–471. 10.1016/0005-1098(78)90005-5
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Chen, M., Ekman, T. & Viberg, M. New Approaches for Channel Prediction Based on Sinusoidal Modeling. EURASIP J. Adv. Signal Process. 2007, 049393 (2006). https://doi.org/10.1155/2007/49393
- Monte Carlo Simulation
- Wireless Communication
- Urban Environment
- Moving Average
- Joint Moving