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.
Highlights
Link adaption techniques, such as multiuser diversity, adaptive modulation and coding, and fast scheduling hold great promise to improve spectrum efficiency
When we say the normalized mean square error (NMSE) of a channel predictor is −10 dB with the level of confidence of 95%, we mean that the average of the normalized square error (NSE) of the best 95% of the channel predictions from simulations or measurements is less than −10 dB, while the worst 5% cases are excluded
The problem of predicting flat Rayleigh fading channels in wireless communications is studied in the frame of sinusoidal modeling in this paper
Summary
Link adaption techniques, such as multiuser diversity, adaptive modulation and coding, and fast scheduling hold great promise to improve spectrum efficiency. The improvement on the system capacity depends heavily on the predictability of the short-term channel fades [1, 2]. Extensive studies on this topic were made during the last several years by different researchers [1], [3,4,5,6,7,8,9,10]. Y(t − N + 1)]T containing the N channel observations (successive estimates of a particular channel coefficient). Such a scenario is presented, where the time index t = 0 and the length of the observation interval is N = 100. The published channel predictors are divided into two categories, which can be categorized as model-free predictors and model-based predictors, respectively
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