Data-driven Processing using Parametric Neural Network for Improved Bluetooth Channel Sounding Distance Estimation

Abstract

Accurate device-to-device distance estimation is crucial for Internet-of-things (IoT) applications. Traditional methods, such as RSSI-based ranging and Time-of-Flight narrowband systems, exhibit limitations. Bluetooth Low Energy (BLE)-based phase ranging, aka Channel Sounding is a preferred technology, but existing approaches can be improved further. This paper proposes a novel approach using a parametric neural network to improve BLE channel sounding performance for device-to-device localization. Our neural network optimizes range estimation by learning from raw channel sounding data, outperforming traditional super-resolution algorithms. We demonstrate significant improvements in accuracy and precision through experimental results, with a reduction in root mean square error of up to 0.4m (indoor) and 0.04m (outdoor) and corresponding standard deviation reductions of up to 0.19m (indoor) and 0.02m (outdoor). This approach enhances device-to-device localization in IoT applications using BLE, enabling a wide range of IoT applications.