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Effect of physical characteristics on artificial neural network error reduction for indoor propagation modeling

Eslami, S ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1109/ICEE59167.2023.10334689
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. In this paper, we examine the effect of physics-based input data in artificial neural network (ANN) for indoor propagation. Our prior knowledge of electromagnetic propagation forms, enables us to predict path loss for a specified geometry. There is a good agreement between ANN predicted output and ray-tracing results for a simple room and the error reduces notably compared to related works. Additionally, we explore feature extraction for more complex scenarios. © 2023 IEEE
  6. Keywords:
  7. Artificial neural network ; Indoor propagation ; Ray-tracing
  8. Source: 2023 31st International Conference on Electrical Engineering, ICEE 2023 ; 2023 , Pages 295-299 ; 979-835031256-0 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10334689