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access icon free Artificial neural network models for radiowave propagation in tunnels

  • Author(s): Aristeidis Seretis 1 ; Xingqi Zhang 1, 2 ; Kun Zeng 3 ; Costas D. Sarris 1
    • View affiliations
  • Source: Volume 14, Issue 11, 09 September 2020, p. 1198 – 1208
    DOI:  10.1049/iet-map.2019.0988 , Print ISSN 1751-8725, Online ISSN 1751-8733
© The Institution of Engineering and Technology
Received 15/11/2019, Accepted 04/05/2020, Revised 09/04/2020, Published 15/05/2020

The authors present a machine learning approach for the extraction of radiowave propagation models in tunnels. To that end, they discuss three challenges related to the application of machine learning to general wireless propagation problems: how to efficiently specify the input to the model, which learning method to use and what output functions to seek. The input that any propagation modelling tool (be it a ray-tracer, a full-wave method or a parabolic equation solver) uses, can be considered as visual, in the form of an image or a point cloud of the environment under consideration. Therefore, they propose an artificial neural network structure that generalises well to various geometries. The desired output can be values of the electromagnetic field components across the channel or just a path loss model. They apply these ideas to the case of arched tunnels for the first time. They consider cases where the geometric parameters of the tunnel, the position of the receiver and the frequency of operation are parts of a model trained by a vector parabolic equation solver. The model is evaluated using solver-generated as well as measured data. The numerical results demonstrate that this approach combines computational efficiency with high accuracy.

Inspec keywords: ray tracing; neural nets; radiowave propagation; electromagnetic fields; tunnels; computational electromagnetics; learning (artificial intelligence); vectors; computational geometry; parabolic equations

Other keywords: ray-tracer; point cloud; radiowave propagation models; vector parabolic equation solver; general wireless propagation problems; propagation modelling tool; geometric parameters; artificial neural network structure; full-wave method; arched tunnels; machine learning approach; output functions; electromagnetic field components; path loss model; artificial neural network models

Subjects: Graphics techniques; Electrical engineering computing; Differential equations (numerical analysis); Neural computing techniques; Radiowave propagation; Differential equations (numerical analysis)

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