Wisegrt: Dataset for site-specific indoor radio propagation modeling with 3d segmentation and differentiable ray-tracing

L Zhang, H Sun, J Sun, RQ Hu - 2024 International Conference …, 2024 - ieeexplore.ieee.org
The accurate modeling of indoor radio propagation is crucial for localization, monitoring, and
device coordination, yet remains a formidable challenge, due to the complex nature of …

Measurement-based prediction of mmWave channel parameters using deep learning and point cloud

H Mi, B Ai, R He, A Bodi, R Caromi… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz
frequency bands. The major differences include higher path loss and sparser multipath …

A generalizable indoor propagation model based on graph neural networks

S Liu, T Onishi, M Taki… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
A surrogate model that “learns” the physics of radio wave propagation is indispensable for
the efficient optimization of communication network coverages and comprehensive …

An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area

J Wang, Z Wu, Y Hao, C Yang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
For improving the accuracy and robustness of predicting radio propagation in the frequency
bands of FM Broadcasting Services, we proposed a propagation prediction model suitable …

Generalisable convolutional neural network model for radio wave propagation in tunnels

S Huang, S Wang, X Zhang - IET Microwaves, Antennas & …, 2024 - Wiley Online Library
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) …

RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments

G Cao, Z Peng - IEEE Journal on Multiscale and Multiphysics …, 2024 - ieeexplore.ieee.org
The radio wave propagation channel is central to the performance of wireless
communication systems. In this paper, we introduce a novel machine learning-empowered …

Custom Hybrid Activation Function over Delocalization Error for Gap State Predictions of A2CeZrO6 (A = Ba2+, Sr2+, Ca2+, Mg2+) Proton Conductors: A First …

D Vignesh, E Rout - The Journal of Physical Chemistry C, 2024 - ACS Publications
The crystal symmetry and band structure are two essential identifiers for a desirable proton-
conducting electrolyte in fuel cell technology. However, structural and electrophysical …

Generalizable Machine-Learning Based Modeling of Radiowave Propagation in Stadiums

A Seretis, V Jevremovic, A Jemmali… - IEEE Open Journal of …, 2023 - ieeexplore.ieee.org
Providing high throughput and quality of service in modern stadiums necessitates the
placement of hundreds of access points (APs). Optimizing the locations of APs in such …

Stochastic evaluation of indoor wireless network performance with data-driven propagation models

S Bakirtzis, I Wassell, M Fiore… - GLOBECOM 2022-2022 …, 2022 - ieeexplore.ieee.org
Cell densification through the installation of smallcells and femtocells in indoor
environments is an emerging solution to enhance the operation of wireless networks. The …

Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges

E Zhu, H Sun, M Ji - arXiv preprint arXiv:2401.01288, 2024 - arxiv.org
Channel modeling is fundamental in advancing wireless systems and has thus attracted
considerable research focus. Recent trends have seen a growing reliance on data-driven …