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Updated: Apr 15, 2026

Photorealistic Learned Landscapes for Augmented Reality
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3D Urban Outdoor WiFi 7 Network Planning and Analysis Using Ray-Tracing and Machine Learning: Transformer-Based

Emanuel-Crăciun Trînc1, Cosmin Ancuți1, Andy Vesa1

  • 1Department of Communications, Polytechnic University of Timisoara, 30006 Timisoara, Romania.

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|April 14, 2026
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Summary
This summary is machine-generated.

This study introduces a hybrid framework using ray tracing and Machine Learning for Wi-Fi 7 channel analysis in urban areas. It significantly reduces computation time for network planning and digital twin applications.

Keywords:
FT-TransformerWiFi 7digital twinmachine learningray-tracingwireless

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Area of Science:

  • Wireless Communications
  • Machine Learning
  • Computational Electromagnetics

Background:

  • Accurate wireless propagation modeling is crucial for smart city connectivity.
  • Deterministic ray-tracing methods offer high fidelity but are computationally intensive and lack scalability.
  • Existing methods struggle with the complexity of dense urban environments.

Purpose of the Study:

  • To develop a scalable and efficient framework for Wi-Fi 7 channel analysis in dense urban settings.
  • To combine the accuracy of ray tracing with the speed of Machine Learning.
  • To enable practical network planning and digital twin applications.

Main Methods:

  • A hybrid framework integrating MATLAB-based ray tracing with Machine Learning regression models.
  • Generation of a large dataset across various frequency bands, transmit powers, and reflection/diffraction scenarios.
  • Evaluation of transformer-based architectures, specifically the FT-Transformer.

Main Results:

  • The FT-Transformer model achieved high accuracy with a Mean Absolute Error (MAE) of 3.49 dB and R2 of 99.63%.
  • Computation time was drastically reduced from months of simulation to seconds during inference.
  • The framework demonstrated effective surrogate modeling capabilities.

Conclusions:

  • The proposed hybrid framework offers an accurate and computationally efficient solution for wireless propagation modeling.
  • This approach significantly enhances scalability for analyzing large 3D urban environments.
  • It provides a valuable tool for smart city network planning and digital twin development.