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

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.