Evaluating the Localization Performance of Bluetooth 6.0's Channel Sounding

Bluetooth 6.0 introduces Channel Sounding (https://tinyurl.com/3vftk3xr) a technique that enhances localization accuracy by analyzing phase measurements across multiple RF frequencies. Using Phase-Based Ranging (PBR), an initiator transmits signals across different frequencies, to which a reflector responds, allowing distance estimation by leveraging phase shifts between transmitted and received signals. This advancement is particularly valuable for applications such as asset tracking, assisted living, and indoor navigation. Unlike traditional Received Signal Strength Indicator (RSSI)-based localization, which suffers from interference and limited accuracy, Channel Sounding employs multi-carrier phase ranging for improved reliability while maintaining Bluetooth's energy efficiency.

This project/thesis experimentally evaluates Channel Sounding's accuracy, latency, robustness, and energy efficiency under various conditions, including multipath-rich environments and non-line-of-sight (NLOS) scenarios (https://tinyurl.com/3u9jjxxc). The gathered experimental results could then be used to explore optimization techniques such as advanced signal processing and machine learning-based corrections to address errors caused by multipath propagation and RF interference.

Download as PDF

Thesis Type:

  • Master Project / Master Thesis

Goals and Tasks:

  • Within this context, students can explore several directions and perform different tasks, such as:
  • Develop a prototype leveraging Bluetooth 6.0's Channel Sounding on an embedded platform (e.g., nRF54L15-DK, xG24-PK6036A);
  • Conduct real-world experiments to assess accuracy, latency, robustness, and energy efficiency in multipath-rich and NLOS conditions;
  • Implement and evaluate optimization techniques such as signal processing algorithms and ML-based corrections.

Required Prior Knowledge:

  • Strong interest in wireless localization and positioning technologies;
  • Experience with embedded system development in C or Python (e.g., using Zephyr RTOS or similar platforms);
  • Basic understanding of signal processing and ML techniques.

Start:

  • As soon as possible.

Contact: