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 SNR- based 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.