Wave propagation modeling techniques in tunnel environments: A survey
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 …
communication link design as it enables the delivery of optimum power to the intended …
Generalisable convolutional neural network model for radio wave propagation in tunnels
Propagation models are essential for the prediction of received signal strength and the
planning of wireless systems in a given environment. The vector parabolic equation (VPE) …
planning of wireless systems in a given environment. The vector parabolic equation (VPE) …
Empowering wireless network applications with deep learning-based radio propagation models
The efficient deployment and operation of any wireless communication ecosystem rely on
knowledge of the received signal quality over the target coverage area. This knowledge is …
knowledge of the received signal quality over the target coverage area. This knowledge is …
Uncertainty quantification neural network from similarity and sensitivity
HMD Kabir, A Khosravi, D Nahavandi… - … Joint Conference on …, 2020 - ieeexplore.ieee.org
Uncertainty quantification (UQ) from similar events brings transparency. However, the
presence of an irrelevant event may degrade the performance of similarity-based algorithms …
presence of an irrelevant event may degrade the performance of similarity-based algorithms …
Advances in computational modeling of EMC/EMI effects in communication-based train control (CBTC) systems
Communication-based train control (CBTC) systems are aimed at providing control and
signaling for rail transportation. These operate in environments that include tunnel and open …
signaling for rail transportation. These operate in environments that include tunnel and open …
Machine Learning Application for Modeling and Design Optimization of High Frequency Structures
MH Bakr, S Ali, AZ Elsherbeni - Advances in Time‐Domain …, 2022 - Wiley Online Library
A brief review of the applications of machine learning to the electromagnetic modeling and
design optimization of high‐frequency structures is presented. The structure of artificial …
design optimization of high‐frequency structures is presented. The structure of artificial …
Efficient Uncertainty Quantification with Subspace Pursuit for FDTD Based Microwave Circuit Models
The polynomial chaos expansion (PCE) method has been employed to quantify the
uncertainty of microwave structures, offering advantages over the conventionally used yet …
uncertainty of microwave structures, offering advantages over the conventionally used yet …
AI-Driven Wireless Propagation Models and Applications
We are presenting examples of artificial intelligence enabled algorithms for radiowave
propagation modeling and their application to practical problems of interests. We use …
propagation modeling and their application to practical problems of interests. We use …