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Transfer Learning for Wireless Fingerprinting Localization Based on Optimal Transport.

Siqi Bai1, Yongjie Luo1, Qun Wan1

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|December 10, 2020
PubMed
Summary

Wireless fingerprinting localization faces challenges with changing radio maps. Optimal transport-based transfer learning effectively maps old radio maps to new ones, improving positioning accuracy and performance.

Keywords:
adaptive radio mapfingerprinting localizationindoor positioningoptimal transporttransfer learning

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Area of Science:

  • Computer Science
  • Signal Processing
  • Machine Learning

Background:

  • Wireless fingerprinting localization (FL) relies on radio fingerprint maps for positioning.
  • Radio maps are dynamic, necessitating frequent updates, which are costly.
  • Transfer learning offers a solution for adapting old radio maps to new environments.

Purpose of the Study:

  • To investigate the efficacy of optimal transport (OT)-based transfer learning for wireless fingerprinting localization.
  • To address the challenge of radio map changes in FL systems.
  • To improve positioning accuracy and performance in dynamic environments.

Main Methods:

  • Utilized optimal transport (OT) to directly map source domain fingerprints to the target domain.
  • Minimized Wasserstein distance to better match data distributions between domains.
  • Employed feature-based transfer learning as a comparison method.
  • Simulated transfer scenarios using two channel models and validated with public measured data.

Main Results:

  • OT-based transfer learning demonstrated superior accuracy and performance compared to feature-based methods.
  • The method effectively matched data distributions, leading to improved positioning in the target domain.
  • Validation confirmed the robustness and effectiveness of OT in dynamic FL scenarios.

Conclusions:

  • Optimal transport-based transfer learning is a highly effective approach for adapting wireless fingerprinting localization systems to changing radio maps.
  • This method offers significant improvements in positioning accuracy and performance.
  • OT-based transfer learning is crucial for maintaining reliable FL in complex, dynamic environments.