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DL-AoD Estimation-Based 5G Positioning Using Directionally Transmitted Synchronization Signals.

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Summary
This summary is machine-generated.

This study estimates the Downlink Angle of Departure (DL-AoD) for 5G User Equipment (UE) using real-world industrial network data. The method achieves accurate indoor localization without extra infrastructure, proving practical for industrial IoT.

Keywords:
5G indoor positioningIoT localization applicationsangle of departure estimationintegrated sensing and communicationneural network positioning algorithms

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Area of Science:

  • Wireless communication
  • Localization and positioning
  • Signal processing

Background:

  • Accurate indoor localization is crucial for industrial IoT and automation.
  • Existing methods often rely on simulations or specialized hardware, limiting real-world applicability.
  • Estimating Downlink Angle of Departure (DL-AoD) from 5G signals offers a promising avenue for precise positioning.

Purpose of the Study:

  • To introduce and validate a novel method for estimating DL-AoD from 5G User Equipment (UE).
  • To assess the feasibility of using these DL-AoD estimates for accurate indoor UE positioning in an industrial setting.
  • To demonstrate the effectiveness of machine learning in overcoming environmental challenges for DL-AoD estimation.

Main Methods:

  • Utilized signal strength measurements from synchronization signals transmitted by 5G anchor nodes.
  • Developed a deterministic estimator for DL-AoD, enhanced with machine learning to adapt to environmental factors like multipath.
  • Validated the method using real-world measurements from an operational 5G network in an industrial factory.

Main Results:

  • Achieved a 90th-percentile DL-AoD estimation error below 4°, outperforming previous experimental results.
  • Obtained a positioning error of 13.2 m, falling within the practical range reported in existing literature.
  • Demonstrated high angular accuracy using standard sub-6 GHz 5G signals without requiring detailed antenna or channel information.

Conclusions:

  • The proposed DL-AoD estimation method offers a practical and accurate solution for indoor localization in industrial environments.
  • Leveraging standard 5G signals eliminates the need for additional infrastructure, paving the way for cost-effective positioning solutions.
  • The findings support the integration of 5G-based localization for enhanced industrial IoT and automation applications.