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CIPRA: Coherence-aware channel indication and prediction for rate adaptation

A number of rate adaptation protocols have proposed using instantaneous channel quality to select the physical layer data rate. However, due to fast channel variations, even aggressive probing of the channel before each data packet is often unable to offer an up-to-date notion of channel quality. 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 measurement errors and the staleness of the channel quality indicator. CIPRA uses Minimum-Mean-Square-Error (MMSE), first-order prediction and 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. We also implement a Doppler shift estimation method in hardware to assist the proposed channel prediction algorithm. We show that CIPRA outperforms existing methods in terms of prediction fidelity and throughput via experimental results from an FPGA-based platform on emulated and in-field wireless channels. In our experiments in the field, CIPRA achieves up to 1.66 times the throughput achieved by the indication and prediction method currently used by off-the-shelf cards. CIPRA could be easily applied to other channel indicators, although we only evaluated RSSI-based rate adaptation in our experiments to isolate our gains.