Our evaluation shows that CIPRA jointly considers the time interval over which the prediction will occur and the coherence time of the channel to determine the optimal window size for previous channel quality indicator measurements. CIPRA uses the minimum mean square error (MMSE) method and first-order prediction. In this paper, we propose a coherence-aware Channel Indication and Prediction algorithm for Rate Adaptation (CIPRA) and evaluate it analytically and experimentally, considering both the effects of measurement errors and the staleness of channel quality indicators. Moreover, due to fast channel variations, even aggressive channel probing fails to offer an up-to-date notion of channel quality. However, the indication of channel quality varies widely across platforms from simply a received signal strength level to a measurement of signal-to-noise ratio (SNR) across sub-carriers, with each channel quality indicator having differing levels of measurement error. A number of rate adaptation protocols have been proposed using instantaneous channel quality to select the physical layer data rate.
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