Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1155/2007/49393 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
stochastic sinusoidal model,long-range channel prediction,rayleigh fading channel,model parameter,statistical sinusoidal model,different predictor,sinusoidal model,new approach,sinusoidal lmmse predictor,prediction model,sinusoidal modeling | Rayleigh fading,Computer science,Mean squared error,Artificial intelligence,Algorithm,Error detection and correction,Linear prediction,Stochastic modelling,Statistical model,Statistics,Sinusoidal model,Moving average,Machine learning | Journal |
Volume | Issue | ISSN |
2007 | 1 | 1687-6180 |
Citations | PageRank | References |
8 | 0.61 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Chen | 1 | 13 | 1.48 |
| torbjorn ekman | 2 | 460 | 33.17 |
| Mats Viberg | 3 | 1043 | 126.67 |