SmartLA: Reinforcement learning-based link adaptation for high throughput wireless access networks

High throughput wireless standards based on IEEE 802.11n and IEEE 802.11ac have been developed and released within the last few years as new amendments over the commercially popular IEEE 802.11. IEEE 802.11n and IEEE 802.11ac support a large pool of parameter set such as increased number of spatial streams via multiple input multiple output (MIMO) communications, channel bonding, guard intervals, different modulation and coding schemes, several levels of frame aggregation, block acknowledgement etc. As a consequence, they boost up physical data rate in the order of Gigabits per second. However, all these enhancements have their internal trade-offs with the channel quality, as explored in the existing literature. For example, higher channel bonding levels result in poor performance under high bit error rate. In a free wireless environment, multiple heterogeneous stations share the wireless channel which is again a time-varying system. Consequently, none of these link level parameters provide an optimal performance for all channel quality instances. Therefore, to practically meet the theoretical high throughput, each wireless device should adapt its physical data transmission rate dynamically by an appropriate tuning of different link parameters. Otherwise, high transmission failure may arise. In this paper, we design an adaptive automated on-line learning mechanism, called “Smart Link Adaptation” (SmartLA), for dynamic selection of link parameters, motivated by “State-Action-Reward-State-Action” (SARSA) model, a variant of reinforcement learning. SmartLA can make a wireless station quite intelligent to cope up with various network conditions by exploiting the best suited data rate observed so far for various channel conditions from the past experience as well as by exploring different possible set of parameters. We analyze the performance of SmartLA in both from simulation analysis and over a 26 nodes IEEE 802.11ac testbed (6 access points and 20 client devices). We observe that the proposed link adaptation mechanism performs significantly better compared to other competing mechanisms mentioned in the literature.