Transforming Decoder-Only Transformers for Accurate WiFi-Telemetry Based Indoor Localization
Nayan Sanjay Bhatia, Katia Obraczka

TL;DR
This paper introduces WiFiGPT, a GPT-based system that leverages large language models to improve WiFi-based indoor localization accuracy, handling diverse telemetry data and environmental variations without extensive calibration.
Contribution
The paper presents WiFiGPT, a novel GPT-based approach that effectively models WiFi telemetry for indoor localization, surpassing traditional methods in accuracy and robustness without handcrafted processing.
Findings
Achieves sub-meter accuracy for RSSI and FTM
Attains centimeter-level precision for CSI
Outperforms conventional localization techniques
Abstract
Wireless Fidelity (WiFi) based indoor positioning is a widely researched area for determining the position of devices within a wireless network. Accurate indoor location has numerous applications, such as asset tracking and indoor navigation. Despite advances in WiFi localization techniques -- in particular approaches that leverage WiFi telemetry -- their adoption in practice remains limited due to several factors including environmental changes that cause signal fading, multipath effects, interference, which, in turn, impact positioning accuracy. In addition, telemetry data differs depending on the WiFi device vendor, offering distinct features and formats; use case requirements can also vary widely. Currently, there is no unified model to handle all these variations effectively. In this paper, we present WiFiGPT, a Generative Pretrained Transformer (GPT) based system that is able to…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · GNSS positioning and interference · Speech and Audio Processing
MethodsAttention Is All You Need · Linear Layer · Layer Normalization · Multi-Head Attention · Dense Connections · Softmax · Position-Wise Feed-Forward Layer · Absolute Position Encodings · Residual Connection · Byte Pair Encoding
