Tunnel channel parameter prediction based on neural networks and ray tracing

Zhenyu Yi; Rongchen Sun

doi:10.1117/12.3034921

7 August 2024 Tunnel channel parameter prediction based on neural networks and ray tracing

Zhenyu Yi, Rongchen Sun

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 1322412 (2024) https://doi.org/10.1117/12.3034921
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

To guide the deployment of antennas in subway tunnels, the tunnel channel parameter prediction method based on Radial Basis Function is proposed. The channel parameter database for straight tunnel and curved tunnel with different turning radius at 1.4GHz is established based on Ray Tracing. The accuracy of the constructed channel parameter database is validated using measured data. By inputting coordinates of transceiver, distance, and turning radius, RBF neural network outputs key channel parameter predictions for communication system design including received power, delay spread, angular spread, and the Rician K-factor. The results demonstrate that the prediction model based on RBF can accurately predict tunnel channel parameters, making it suitable for the setup and optimization of tunnel wireless communication system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenyu Yi and Rongchen Sun "Tunnel channel parameter prediction based on neural networks and ray tracing", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 1322412 (7 August 2024); https://doi.org/10.1117/12.3034921

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