Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz
Accurate channel models are essential to evaluate mobile communication system
performance and optimize coverage for existing deployments. The introduction of various …
performance and optimize coverage for existing deployments. The introduction of various …
Predicting path loss distribution of an area from satellite images using deep learning
Path loss prediction is essential for network planning in any wireless communication system.
For cellular networks, it is usually achieved through extensive received signal power …
For cellular networks, it is usually achieved through extensive received signal power …
A Deep Learning Method for Path Loss Prediction Using Geospatial Information and Path Profiles
T Hayashi, K Ichige - IEEE Transactions on Antennas and …, 2023 - ieeexplore.ieee.org
Beyond 5G/6G should provide services everywhere, and it is necessary to expand area
coverage and develop high-frequency bands from millimeter waves to terahertz waves …
coverage and develop high-frequency bands from millimeter waves to terahertz waves …
Enhanced MDT-based performance estimation for AI driven optimization in future cellular networks
HN Qureshi, A Imran, A Abu-Dayya - IEEE Access, 2020 - ieeexplore.ieee.org
Minimization of drive test (MDT) allows coverage estimation at a base station by leveraging
measurement reports gathered at the user equipment (UE) without the need for drive tests …
measurement reports gathered at the user equipment (UE) without the need for drive tests …
Image-driven spatial interpolation with deep learning for radio map construction
Radio maps are a promising technology that can boost the capability of wireless networks by
enhancing spectrum efficiency. Since spatial interpolation is a critical challenge to construct …
enhancing spectrum efficiency. Since spatial interpolation is a critical challenge to construct …
SICNN: Spatial interpolation with convolutional neural networks for radio environment mapping
R Hashimoto, K Suto - 2020 International Conference on …, 2020 - ieeexplore.ieee.org
This paper addresses the spatial interpolation problem in measurement-based radio
environment estimation. For accurate interpolation, we need to extract global and local radio …
environment estimation. For accurate interpolation, we need to extract global and local radio …
An ubiquitous 2.6 GHz radio propagation model for wireless networks using self-supervised learning from satellite images
The performance of any Mobile Wireless Network (MWN) is dependent on the appropriate
level of radio coverage, with Path Loss (PL) models being a valuable resource for its …
level of radio coverage, with Path Loss (PL) models being a valuable resource for its …
Exploiting future radio resources with end-to-end prediction by deep learning
J Guo, C Yang, I Chih-Lin - IEEE Access, 2018 - ieeexplore.ieee.org
Machine learning is a powerful tool to predict user behavior and harness the vast amount of
data measured in cellular networks. Predictive resource allocation is a promising approach …
data measured in cellular networks. Predictive resource allocation is a promising approach …
Comparison of empirical and ray-tracing models for mobile communication systems at 2.6 GHz
Accurate channel models for predicting received power under slow fading impairments are
essential for planning 5G solutions due to the increased range of possible transmission …
essential for planning 5G solutions due to the increased range of possible transmission …
Gendt: mobile network drive testing made efficient with generative modeling
Drive testing continues to play a key role in mobile network optimization for operators but its
high cost is a big concern. Alternative approaches like virtual drive testing (VDT) target …
high cost is a big concern. Alternative approaches like virtual drive testing (VDT) target …