Leveraging frame aggregation to improve access point selection

With the incredible rise in WiFi devices, proper assessment for performance is essential for Quality of Experience (QoE). In the past, many access point (AP) assessment metrics have been exploited to achieve optimal AP selection. However, these conventional metrics (e.g., throughput) are insufficient to capture the full dynamics of the AP load condition. In our paper, we posit that the recent introduction of frame aggregation by 802.11e can offer a compact and efficient representation of expected throughput for improving AP selection. We show that by conveying the characteristics of subframes during frame aggregation, we can uniquely embody the utilization, interference, and backlog traffic pressure for an access point. We validate the effectiveness of the proposed metrics with the commercial off the shelf (COTS) experiments. In addition, we explore an application case of using the metrics by adopting simple machine learning methods.