New Approaches for Channel Prediction Based on Sinusoidal Modeling

Chen, Ming,1972Chalmers tekniska högskola,Chalmers University of Technology (författare)

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.

Ekman, TorbjörnNorges teknisk-naturvitenskapelige universitet (NTNU),Norwegian University of Science and Technology (NTNU) (författare)
Viberg, Mats,1961Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)viberg (författare)
Chalmers tekniska högskolaNorges teknisk-naturvitenskapelige universitet (NTNU) (creator_code:org_t)

Ingår i:Eurasip Journal on Applied Signal Processing: Springer Science and Business Media LLC20071110-86571687-0433
Ingår i:EURASIP Journal on Advances in Signal Processing: Springer Science and Business Media LLC20071687-6180

Av författaren/redakt...: Chen, Ming, 1972; Ekman, Torbjörn; Viberg, Mats, 19 ...

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