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

2026-01-09

Consumer Communications and Networking Conference

AI Summary

This work proposes a mechanical antenna auto-orientation system based on Bayesian optimization to address the challenge non-expert users face in manually aligning Wi-Fi antennas, which often leads to degraded communication performance. By continuously measuring throughput and using it as feedback to guide antenna orientation adjustments, the system autonomously identifies near-optimal directional configurations. To the best of our knowledge, this is the first application of Bayesian optimization to mechanical antenna steering. Experimental results demonstrate that, in line-of-sight scenarios, the proposed approach achieves a throughput gain of approximately 70 Mbps compared to baseline strategies, significantly outperforming random search. These findings validate both the feasibility and the substantial performance benefits of automated antenna alignment for enhancing wireless connectivity.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Wi-Fi antenna
• antenna orientation
• communication performance
• non-expert users
• throughput variation

Innovation

• Mechanical Wi-Fi Antenna
• Automatic Orientation Tuning
• Bayesian Optimization
• Antenna Polarization
• Throughput Optimization