Abstract
Long-range prediction of fading wireless channel quality is considered to be one of the most important techniques for high speed wireless communication systems. For example, to enable an efficient adaptive transmission, channel state has to be predicted several symbols ahead to compensate for delay in the feedback loop and adaptation rate limitations. Autoregressive model based linear predictor is addressed in this paper, in particular an impact of predictor order on prediction accuracy for various fading scenarios is investigated. The results show that memory length is an important factor that affects channel state prediction accuracy especially for fast fading channels.
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