Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments

Qian, Jingyuan; Wu, Yating; Saleem, Asad; Zheng, Guoxin

doi:10.3390/s22124524

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…When the waveguide mode theory and the method of shooting and bouncing rays (SBR) are used together, it is found that both straight and curved tunnels lose power in the far-field region when they are stacked together. So, the authors came up with a method that takes an extra loss coefficient into account in the propagation model [90]. (8) where d BP is the breakpoint that can be determined using (9), and extra loss coefficient (ELC) can be determined using (10).…”

Section: B: Waveguide-shooting and Bouncing Rays (Wg-sbr)mentioning

confidence: 99%

Wave Propagation Modeling Techniques in Tunnel Environments: A Survey

et al. 2023

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The prediction of radio signal transmission in tunnels is important for wireless communication link design as it enables the delivery of optimum power to the intended receiver inside the tunnel. Several techniques have been proposed to estimate path loss for wireless communication inside tunnels. The radio coverage range and where base stations need to be installed can be figured out with the help of propagation models. At the time of developing these models, modeling complexity and environmental attributes, such as the tunnel geometry and the electrical and magnetic properties of its walls and interior materials are considered. A limited amount of literature currently overviews how waves transmits through tunnels and how electromagnetic wave transmission has recently changed. This survey, which examines current advancements in the propagation of waves in tunnels, aims to address this gap. In this review, thirty wave propagation models were analyzed and grouped into distinct categories for the first time. The detailed specifications of each model were omitted to make the survey easy to comprehend. This comparative research will help service providers adopt an appropriate propagation model and plan efficient transmitter and receiver placement and link management for a specific tunnel environment.INDEX TERMS Artificial neural network, radio wave propagation, ray tracing, scaling, tunnel.

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Section: B: Waveguide-shooting and Bouncing Rays (Wg-sbr)mentioning

confidence: 99%

Wave Propagation Modeling Techniques in Tunnel Environments: A Survey

et al. 2023

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“…Regarding deterministic models, they are based on the electromagnetic theory. These models offer precise path loss values at any given position using ray tracing and finite-difference time-domain methods [ 15 , 16 , 17 ]. However, these models require detailed geometric data, such as a two-dimensional (2-D) or 3-D map of a specific region and the dielectric properties of obstacles to predict path loss.…”

Section: Introductionmentioning

confidence: 99%

Accurate Path Loss Prediction Using a Neural Network Ensemble Method

Kwon,

Son

2024

Sensors

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Path loss is one of the most important factors affecting base-station positioning in cellular networks. Traditionally, to determine the optimal installation position of a base station, path-loss measurements are conducted through numerous field tests. Disadvantageously, these measurements are time-consuming. To address this problem, in this study, we propose a machine learning (ML)-based method for path loss prediction. Specifically, a neural network ensemble learning technique was applied to enhance the accuracy and performance of path loss prediction. To achieve this, an ensemble of neural networks was constructed by selecting the top-ranked networks based on the results of hyperparameter optimization. The performance of the proposed method was compared with that of various ML-based methods on a public dataset. The simulation results showed that the proposed method had clearly outperformed state-of-the-art methods and that it could accurately predict path loss.

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