High throughput wireless standards based on IEEE 802.11, such as IEEE 802.11ac, pose a significant challenge in selection of link configuration parameters in an automatic approach. These high throughput wireless standards have a large pool of link configuration parameters at the both physical (PHY) and media access control (MAC) layers, which include channel bonding, multiple input multiple output (MIMO) technology, short guard interval, advanced modulation and coding schemes (MCS), frame aggregation, block acknowledgement (BA) etc. In time-varying wireless channel, each wireless station must tune such multiple link configuration parameters dynamically and adaptively according to the channel and network conditions, to achieve high throughput in practical scenarios. This dynamic adjustment of link configuration parameters is known as auto link-configuration. In this paper, we design an online learning-based mechanism, BanditLink, for auto link-configuration in high throughput wireless networks. To tackle auto link-configuration based on both network load and channel conditions, BanditLink implements multi-armed bandit based adaptive learning methodology along with fuzzy logic. We analyze the performance of BanditLink from both simulation and testbed results, and observe that it can improve the network performance compared to other link adaptation methodologies.

基于IEEE 802.11的高吞吐量无线标准（例如IEEE 802.11ac）在自动选择链路配置参数方面提出了重大挑战。这些高吞吐量无线标准在物理（PHY）层和媒体访问控制（MAC）层都有大量的链路配置参数，包括信道绑定，多输入多输出（MIMO）技术，较短的保护间隔，高级调制和编码方案（MCS），帧聚合，块确认（BA）等。在时变无线信道中，每个无线站必须根据信道和网络条件动态地自适应地调整此类多链路配置参数，以在实践中实现高吞吐量场景。链接配置参数的这种动态调整称为自动链接配置。在本文中，我们设计了一种基于在线学习的机制BanditLink，用于高吞吐量无线网络中的自动链路配置。为了解决基于网络负载和信道条件的自动链接配置，BanditLink实施了基于多臂强盗的自适应学习方法以及模糊逻辑。我们从仿真结果和测试结果中分析了BanditLink的性能，并观察到与其他链路自适应方法相比，它可以提高网络性能。