Abstract
Rate adaptation (RA) is an essential mechanism in 802.11 WLAN. In the latest 802.11ac protocol, there are two emerging problems that need to be addressed for the RA. First, 802.11ac supporting more rate selections requires more efforts to find the optimal rate. Finally, 802.11ac supports higher rate. The difference between optimal rate and non-optimal rate can be so great that non-optimal rate would severely deteriorate the throughput of the WLAN. In order to tackle these problems, we develop a novel RA algorithm termed rate adaptation with Thompson sampling (RATS) for stationary and non-stationary channel environments. In this algorithm, we first consider compacting the search space by removing some rates to accelerate the convergence of the algorithm. Moreover, inspired by multi-armed bandit problem, we design RA algorithm based on Thompson sampling. Simulation results demonstrate that the performance of the proposed RATS outperforms the existing method.
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