WHO-IS: Wireless Hetnet Optimization using Impact Selection

TL;DR

This paper introduces a method to identify and offload users negatively impacting wireless network performance, using impact selection and orthogonal channels, validated through real-world traces, simulations, and lab experiments.

Contribution

It presents a novel impact-based user offloading approach for wireless HetNets, combining real-world data, simulations, and lab tests with LiFi IR transceivers.

Findings

82% reduction in collision probability

61 percentage point improvement in air utilization

Targeted offloading effectively enhances network performance

Abstract

We propose a method to first identify users who have the most negative impact on the overall network performance, and then offload them to an orthogonal channel. The feasibility of such an approach is verified using real-world traces, network simulations, and a lab experiment that employs multi-homed wireless stations. In our experiment, as offload target, we employ LiFi IR transceivers, and as the primary network we consider a typical Enterprise Wi-Fi setup. We found that a limited number of users can impact the overall experience of the Wi-Fi network negatively, hence motivating targeted offloading. In our simulations and experiments we saw that the proposed solution can improve the collision probability with 82% and achieve a 61 percentage point air utilization improvement compared to random offloading, respectively.