A Practical SNR-Guided Rate Adaptation

Rate adaptation is critical to the system performance of
wireless networks. Typically, rate adaptation is considered as a
MAC layer mechanism in IEEE 802.11. Most previous work relies
only on frame losses to infer channel quality, but performs poorly
if frame losses are mainly caused by interference. Recently SNRbased
rate adaptation schemes have been proposed, but most of
them have not been studied in a real environment. In this paper,
we first conduct a systematic measurement-based study to
confirm that in general SNR is a good prediction tool for channel
quality, and identify two key challenges for this to be used in
practice: (1) The SNR measures in hardware are often
uncalibrated, and thus the SNR thresholds are hardware
dependent. (2) The direct prediction from SNR to frame delivery
ratio (FDR) is often over optimistic under interference conditions.
Based on these observations, we present a novel practical SNR Guided
Rate Adaptation (SGRA) scheme. We implement and
evaluate SGRA in a real test-bed and compare it with other three
algorithms: ARF, RRAA and HRC. Our results show that SGRA
outperforms the other three algorithms in all cases we have tested.