A Mechanical Wi-Fi Antenna Device for Automatic Orientation Tuning with Bayesian Optimization

TL;DR

This paper presents a mechanical Wi-Fi antenna device that automatically adjusts its orientation using Bayesian optimization, significantly improving throughput and ease of use for non-expert users.

Contribution

We introduce a novel mechanical antenna device with automated orientation tuning driven by Bayesian optimization, enhancing Wi-Fi performance without user intervention.

Findings

Antenna orientation impacts throughput by about 70 Mbps.

Bayesian optimization outperforms random search in tuning efficiency.

Automated tuning improves Wi-Fi performance in line-of-sight conditions.

Abstract

Wi-Fi access points have been widely deployed in homes, offices, and public spaces. Some APs allow users to adjust the antenna orientation to improve communication performance by optimizing antenna polarization. However, it is difficult for non-expert users to determine the optimal orientation, and users often leave the antenna orientation in ineffective positions. To address this issue, we developed a mechanical Wi-Fi antenna device capable of automatically tuning its orientation. Experimental results show that antenna orientation could cause a throughput variation of approximately 70 Mbps under line-of-sight conditions. Furthermore, Bayesian optimization identified better configurations than random search, demonstrating its effectiveness for orientation tuning.