Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions

ME Morocho-Cayamcela, H Lee, W Lim - IEEE access, 2019 - ieeexplore.ieee.org
Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be
a key enabler and a leading infrastructure provider in the information and communication …

Artificial intelligence in 5G technology: A survey

MEM Cayamcela, W Lim - 2018 International Conference on …, 2018 - ieeexplore.ieee.org
A fully operative and efficient 5G network cannot be complete without the inclusion of
artificial intelligence (AI) routines. Existing 4G networks with all-IP (Internet Protocol) …

Path loss prediction based on machine learning techniques: Principal component analysis, artificial neural network, and Gaussian process

HS Jo, C Park, E Lee, HK Choi, J Park - Sensors, 2020 - mdpi.com
Although various linear log-distance path loss models have been developed for wireless
sensor networks, advanced models are required to more accurately and flexibly represent …

A UHF path loss model using learning machine for heterogeneous networks

M Ayadi, AB Zineb, S Tabbane - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
In this paper, we present and evaluate a new propagation model for heterogeneous
networks. The designed model is multiband, multienvironment, and is usable for short and …

The Current Progress and Future Prospects of Path Loss Model for Terrestrial Radio Propagation

J Wang, Y Hao, C Yang - Electronics, 2023 - mdpi.com
The radio channel model is a major factor supporting the whole life cycle of the terrestrial
radio system, including the demonstration, design, validation, operation, and so on. To …

Sensor-aided EMF exposure assessments in an urban environment using artificial neural networks

S Wang, J Wiart - International Journal of Environmental Research and …, 2020 - mdpi.com
This paper studies the time and space mapping of the electromagnetic field (EMF) exposure
induced by cellular base station antennas (BSA) using artificial neural networks (ANN). The …

Path loss prediction in smart campus environment: Machine learning-based approaches

H Singh, S Gupta, C Dhawan… - 2020 IEEE 91st Vehicular …, 2020 - ieeexplore.ieee.org
This paper presents a novel application of various machine learning (ML)-based
approaches towards prediction of path loss (PL) parameter for a smart campus environment …

Overview of the First Pathloss Radio Map Prediction Challenge

Ç Yapar, F Jaensch, R Levie… - IEEE Open Journal of …, 2024 - ieeexplore.ieee.org
Pathloss quantifies the reduction in power density of a signal radiated from a transmitter. The
attenuation is due to large-scale effects such as free-space propagation loss and …

Comparison of artificial intelligence and semi-empirical methodologies for estimation of coverage in mobile networks

DFS Fernandes, A Raimundo, F Cercas… - IEEE …, 2020 - ieeexplore.ieee.org
To help telecommunication operators in their network planning, namely coverage estimation
and optimisation tasks, this article presents a comparison between a semi-empirical …

Multiscale decomposition prediction of propagation loss in oceanic tropospheric ducts

M Dang, J Wu, S Cui, X Guo, Y Cao, H Wei, Z Wu - Remote Sensing, 2021 - mdpi.com
The oceanic tropospheric duct is a structure with an abnormal atmospheric refractive index.
This structure severely affects the remote sensing detection capability of electromagnetic …