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Indoor Localization with Extended Trajectory Map Construction and Attention Mechanisms in 5G.

Kexin Yang1, Chao Yu1, Saibin Yao2

  • 1The Engineering Center of SHMEC for Space Information and GNSS, East China Normal University, Shanghai 200241, China.

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|September 27, 2025
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Summary

This study introduces an attention-based framework for indoor localization using integrated sensing and communication (ISAC). It enhances data quality through trajectory-based data augmentation, improving mobile device positioning accuracy.

Keywords:
5Gfingerprint positioninggenerative adversarial networkindoor localization

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Area of Science:

  • Wireless Communication
  • Internet of Things (IoT)
  • Sensing Technologies

Background:

  • Integrated Sensing and Communication (ISAC) is crucial for future IoT, enabling simultaneous data transmission and sensing.
  • Indoor localization performance is limited by the quality and granularity of collected data.
  • Existing methods struggle with data limitations in ISAC environments.

Purpose of the Study:

  • To propose an attention-based framework for cost-efficient indoor fingerprint localization.
  • To enhance indoor localization accuracy by addressing data quality constraints.
  • To leverage trajectory-based data augmentation for improved sensing services in ISAC.

Main Methods:

  • Developed a novel trajectory-based data augmentation (TDA) method.
  • Utilized a conditional Wasserstein generative adversarial network (CWGAN) to synthesize fingerprints.
  • Employed a path generation algorithm for extended trajectory map construction.
  • Applied a multi-head attention model with direction-constrained auxiliary loss for localization.

Main Results:

  • Achieved an average indoor localization error of 1.09 meters in a real 5G environment.
  • Demonstrated at least 34% higher accuracy compared to existing indoor localization approaches.
  • Validated the effectiveness of the attention-based framework and TDA method.

Conclusions:

  • The proposed attention-based framework significantly improves indoor localization accuracy in ISAC systems.
  • Trajectory-based data augmentation effectively synthesizes crucial fingerprint data for enhanced sensing.
  • This approach offers a cost-efficient solution for precise mobile device localization in indoor IoT settings.