Instant Indoor Wi-Fi Coverege Predictor
Research Poster Engineering 2025 Graduate ExhibitionPresentation by Ziheng Fu
Exhibition Number 159
Abstract
My research focuses on providing instant Wi-Fi coverage predictions for indoor environments. By analyzing floorplans and recognizing the placement of furniture, our machine learning-based predictor simulates wave propagation and interactions with objects. This allows us to generate a Wi-Fi coverage map within seconds, eliminating the need for on-site measurements or complex electromagnetic calculations. Users can apply this tool to optimize the placement of Wi-Fi routers or rearrange furniture to achieve the best signal coverage.
Importance
Reliable Wi-Fi coverage is crucial in modern indoor spaces, yet optimizing it often requires complex simulations or time-consuming measurements. My research simplifies this process with a machine learning-based predictor that instantly generates Wi-Fi coverage maps using floorplans and furniture layouts. This tool enables users to optimize router placement and indoor layouts for better connectivity without technical expertise. By making Wi-Fi optimization faster and more accessible, this work enhances network performance in homes, offices, and public spaces, improving connectivity and productivity while also supporting advanced 5G technologies such as beamforming and intelligent reflecting surfaces.