An overview of machine learning techniques for radiowave propagation modeling

A Seretis, CD Sarris - IEEE Transactions on Antennas and …, 2021‏ - ieeexplore.ieee.org
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 …

Extremely randomized trees-based scheme for stealthy cyber-attack detection in smart grid networks

MRC Acosta, S Ahmed, CE Garcia, I Koo - IEEE access, 2020‏ - ieeexplore.ieee.org
Smart grids have become susceptible to cyber-attacks, being one of the most diversified
cyber–physical systems. Measurements collected by the supervisory control and data …

Constellation design for future communication systems: A comprehensive survey

J Barrueco, J Montalban, E Iradier, P Angueira - IEEE Access, 2021‏ - ieeexplore.ieee.org
The choice of modulation schemes is a fundamental building block of wireless
communication systems. As a key component of physical layer design, they critically impact …

Path loss prediction based on machine learning methods for aircraft cabin environments

J Wen, Y Zhang, G Yang, Z He, W Zhang - Ieee Access, 2019‏ - ieeexplore.ieee.org
Wireless communications in aircraft cabin environments have drawn widespread attention
with the increase of application requirements. To ensure reliable and stable in-cabin …

Accurate path loss prediction using a neural network ensemble method

B Kwon, H Son - Sensors, 2024‏ - mdpi.com
Path loss is one of the most important factors affecting base-station positioning in cellular
networks. Traditionally, to determine the optimal installation position of a base station, path …

Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 GHz

N Moraitis, L Tsipi, D Vouyioukas, A Gkioni, S Louvros - Wireless networks, 2021‏ - Springer
This paper presents and assesses various machine learning methods that aim at predicting
path loss in rural environment. For this purpose, models such as artificial neural network …

Toward effective planning and management using predictive analytics based on rental book data of academic libraries

N Iqbal, F Jamil, S Ahmad, D Kim - Ieee Access, 2020‏ - ieeexplore.ieee.org
Large scale data and predictive analytics are the most challenging tasks in the field of
academic data mining. Academic libraries are a great source of information and knowledge …

Extremely randomized trees regressor scheme for mobile network coverage prediction and REM construction

CE García, I Koo - IEEE Access, 2023‏ - ieeexplore.ieee.org
In mobile communications network planning (and designing any radio system), coverage
prediction helps network operators optimize cellular networks to improve customer …

Machine learning-based methods for path loss prediction in urban environment for LTE networks

N Moraitis, L Tsipi, D Vouyioukas - 2020 16th international …, 2020‏ - ieeexplore.ieee.org
This work presents prediction path loss models in an urban environment for cellular
networks with the help of machine learning methods. For this goal, Support Vector …

Predicting path loss of an indoor environment using artificial intelligence in the 28-GHz band

SA Aldossari - Electronics, 2023‏ - mdpi.com
The propagation of signal and its strength in an indoor area have become crucial in the era
of fifth-generation (5G) and beyond-5G communication systems, which use high bandwidth …