An overview of machine learning techniques for radiowave propagation modeling
We give an overview of recent developments in the modeling of radiowave propagation,
based on machine learning (ML) algorithms. We identify the input and output specification …
based on machine learning (ML) algorithms. We identify the input and output specification …
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 …
Toward physics-based generalizable convolutional neural network models for indoor propagation
A fundamental challenge for machine learning (ML) models for electromagnetics is their
ability to predict output quantities of interest (such as fields and scattering parameters) in …
ability to predict output quantities of interest (such as fields and scattering parameters) in …
Channel measurements and models for high-speed train wireless communication systems in tunnel scenarios: A survey
The rapid developments of high-speed trains (HSTs) introduce new challenges to HST
wireless communication systems. Realistic HST channel models play a critical role in …
wireless communication systems. Realistic HST channel models play a critical role in …
Electromagnetic effective-degree-of-freedom limit of a MIMO system in 2-D inhomogeneous environment
Compared with a single-input-single-output (SISO) wireless communication system, the
benefit of multiple-input-multiple-output (MIMO) technology originates from its extra degree …
benefit of multiple-input-multiple-output (MIMO) technology originates from its extra degree …
A Gaussian beam approximation approach for embedding antennas into vector parabolic equation-based wireless channel propagation models
Vector parabolic equation (VPE) methods have been widely applied to the modeling of radio-
wave propagation in tunnel environments, offering high computational efficiency and fidelity …
wave propagation in tunnel environments, offering high computational efficiency and fidelity …
Artificial neural network models for radiowave propagation in tunnels
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 …
propagation models in tunnels. To that end, they discuss three challenges related to the …
Two-slope path loss model for curved-tunnel environment with concept of break point
The curvature of tunnels introduces an extra loss in the wave propagation. A simulation and
measurement study are performed on the straight and the curved tunnels to investigate the …
measurement study are performed on the straight and the curved tunnels to investigate the …
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) …
Integrating physics-based wireless propagation models and network protocol design for train communication systems
Physics-based wireless propagation modeling and network protocol design have evolved
over decades as orthogonal areas in communication systems research. This fragmented …
over decades as orthogonal areas in communication systems research. This fragmented …