SampleLite: A Hybrid Approach to 802.11n Link Adaptation

We consider the link adaptation problem in 802.11n wireless LANs that involves adapting MIMO mode, channel bonding, modulation and coding scheme, and frame aggregation level with varying channel conditions. Through measurement-based analysis, we find that adapting all available 802.11n features results in higher goodput than adapting only a subset of features, thereby showing that holistic link adaptation is crucial to achieve best performance. We then design a novel hybrid link adaptation scheme termed SampleLite that adapts all 802.11n features while being efficient compared to sampling-based open-loop schemes and practical relative to closed loop schemes. SampleLite uses sender-side RSSI measurements to significantly lower the sampling overhead, by exploiting the monotonic relationship between best settings for each feature and the RSSI. Through analysis and experimentation in a testbed environment, we show that our proposed approach can reduce the sampling overhead by over 70% on average compared to the widely used Minstrel HT scheme. We also experimentally evaluate the goodput performance of SampleLite in a wide range of controlled and real-world interference scenarios. Our results show that SampleLite, while performing close to the ideal, delivers goodput that is 35-100% better than with existing schemes.