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This study introduces a deep learning method using Long-Term Evolution (LTE) signal fingerprints for accurate outdoor positioning. The novel approach enhances accuracy and reduces data collection needs in complex environments.

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Area of Science:

  • Wireless communication
  • Deep learning
  • Geospatial positioning

Background:

  • Fingerprint-based positioning offers high accuracy in challenging environments.
  • Long-Term Evolution (LTE) signals are increasingly utilized for positioning applications.
  • Existing methods face challenges with signal fluctuations and data collection workload.

Purpose of the Study:

  • To develop a deep learning-based LTE signal fingerprint positioning method for outdoor environments.
  • To propose a novel hybrid location gray-scale image representation for LTE fingerprints.
  • To enhance positioning accuracy and reduce data collection efforts.

Main Methods:

  • A hybrid location gray-scale image utilizing LTE signal fingerprints was created.
  • Data enhancement techniques were employed to manage signal fluctuations.
  • A hierarchical deep neural network (DNN) architecture was used, including a modified Deep Residual Network (Resnet) for coarse localization and a multilayer perceptron (MLP) for fine-tuning via transfer learning.

Main Results:

  • The proposed method demonstrated considerable positioning accuracy in various outdoor environments.
  • The hierarchical DNN architecture effectively learned features from unstable LTE signals.
  • Transfer learning with MLP reduced the data collection workload while improving accuracy.

Conclusions:

  • The developed deep learning-based LTE positioning system achieves satisfactory accuracy in outdoor settings.
  • The novel image representation and hierarchical DNN approach effectively address signal instability and data collection challenges.
  • This technique offers a promising solution for reliable outdoor positioning using LTE signals.