A deep learning network planner: Propagation modeling using real-world measurements and a 3D city model
In urban scenarios, network planning requires awareness of the notoriously complex
propagation environment by accounting for blocking, diffraction, and reflection on buildings …
propagation environment by accounting for blocking, diffraction, and reflection on buildings …
EM DeepRay: An expedient, generalizable, and realistic data-driven indoor propagation model
Efficient and realistic indoor radio propagation modeling tools are inextricably intertwined
with the design and operation of next-generation wireless networks. Machine-learning (ML) …
with the design and operation of next-generation wireless networks. Machine-learning (ML) …
Spatial signal strength prediction using 3D maps and deep learning
Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied
to simulating complex physics by learning physics models thanks to large data. Inspired by …
to simulating complex physics by learning physics models thanks to large data. Inspired by …
Pathloss prediction using deep learning with applications to cellular optimization and efficient D2D link scheduling
In this paper we propose a highly efficient and very accurate method for estimating the
propagation pathloss from a point x to all points y on the 2D plane. Our method, termed …
propagation pathloss from a point x to all points y on the 2D plane. Our method, termed …
Machine learning-based urban canyon path loss prediction using 28 ghz manhattan measurements
Large bandwidth at millimeter wave (mm-wave) is crucial for fifth generation (5G) and
beyond, but the high path loss (PL) requires highly accurate PL prediction for network …
beyond, but the high path loss (PL) requires highly accurate PL prediction for network …
DeepRay: Deep learning meets ray-tracing
Efficient and accurate indoor radio propagation modeling tools are essential for the design
and operation of wireless communication systems. Lately, several attempts to combine radio …
and operation of wireless communication systems. Lately, several attempts to combine radio …
Is ray-tracing viable for millimeter-wave networking studies?
A Ichkov, P Mähönen, L Simić - 2020 IEEE 31st Annual …, 2020 - ieeexplore.ieee.org
The promise of millimeter-wave (mm-wave) frequencies for high capacity cellular networks
depends on precise alignment of the narrow directional beams to either line-of-sight (LOS) …
depends on precise alignment of the narrow directional beams to either line-of-sight (LOS) …
Deep learning propagation models over irregular terrain
Accurate path gain models are critical for coverage prediction and radio frequency (RF)
planning in wireless communications. In many settings irregular terrain induces blockages …
planning in wireless communications. In many settings irregular terrain induces blockages …
[HTML][HTML] Deep learning for radio propagation: Using image-driven regression to estimate path loss in urban areas
Radio propagation modeling and path loss prediction have been the subject of many
machine learning-based estimation attempts. Our current work uses deep learning for the …
machine learning-based estimation attempts. Our current work uses deep learning for the …
Wisegrt: Dataset for site-specific indoor radio propagation modeling with 3d segmentation and differentiable ray-tracing
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 …
device coordination, yet remains a formidable challenge, due to the complex nature of …