ArticleSensors (Basel, Switzerland)2022
Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.Sensors (Basel, Switzerland) · 2024Article
- Accurate Path Loss Prediction Using a Neural Network Ensemble Method.Sensors (Basel, Switzerland) · 2024Article
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4 authors.
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Abstract
An important and typical scenario of radio propagation in a railway or subway tunnel environment is the cascaded straight and curved tunnel. In this paper, we propose a joint path loss model for cascaded tunnels at 3.5 GHz and 5.6 GHz frequency bands. By combining the waveguide mode theory and the method of shooting and bouncing ray (SBR), it is found that the curvature of tunnels introduces an extra loss in the far-field region, which can be modeled as a linear function of the propagation distance of the signal in the curved tunnel. The channel of the cascaded straight and curved tunnel is thus characterized using the extra loss coefficient (ELC). Based on the ray-tracing (RT) method, an empirical formula between ELC and the radius of the curvature is provided for 3.5 GHz and 5.6 GHz, respectively. Finally, the accuracy of the proposed model is verified by measurement and simulation results. It is shown that the proposed model can predict path loss in cascaded tunnels with desirable accuracy and low complexity.
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