Bhatia, N. (CSE) – Building Adaptive Intelligence into Wireless Sensing

WiFi-based indoor positioning is a widely researched area focused on determining the location of devices. Accurate indoor positioning has numerous applications, including asset tracking and indoor navigation. Despite advances, their adoption in practice remains limited due to several challenges such as environmental changes that cause signal fading, multipath effects, and interference, all of which reduce positioning accuracy. Moreover, telemetry data vary across WiFi device vendors, presenting distinct features and formats, while use-case requirements can also differ significantly. At present, there is no unified model capable of handling these variations effectively.

We present WiFiGPT, a decoder-only transformer-based system designed to address these variations while achieving high localization accuracy. Our experiments with WiFiGPT show that it can effectively capture subtle spatial patterns in noisy wireless telemetry, making them reliable regressors. Compared to state-of-the-art methods, our approach matches and often surpasses conventional techniques across multiple types of telemetry. Achieving sub-meter accuracy for RSSI and FTM and centimeter-level precision for CSI highlights the potential of LLM-based localization to outperform specialized methods, without the need for handcrafted signal processing or calibration. Other work includes EchoSense, which utilizes CSI to monitor vital signs such as heart rate and respiration with high accuracy.

Event Host: Nayan Bhatia, PhD Student, Computer Science & Engineering

Advisor: Katia Obraczka