Wireless transmissions, propagation and channel modelling for IoT technologies: Applications and challenges

HAH Alobaidy, MJ Singh, M Behjati, R Nordin… - IEEE …, 2022 - ieeexplore.ieee.org
The Internet of Things (IoT) has rapidly expanded for a wide range of applications towards a
smart future world by connecting everything. As a result, new challenges emerge in meeting …

[HTML][HTML] Large scale survey for radio propagation in develo** machine learning model for path losses in communication systems

H Chiroma, P Nickolas, N Faruk, E Alozie, IFY Olayinka… - Scientific African, 2023 - Elsevier
Several orthodox approaches, such as empirical methods and deterministic methods, had
earlier been used for the prediction of path loss in wireless communication systems. These …

Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz

J Thrane, D Zibar, HL Christiansen - Ieee Access, 2020 - ieeexplore.ieee.org
Accurate channel models are essential to evaluate mobile communication system
performance and optimize coverage for existing deployments. The introduction of various …

Artificial neural network based path loss prediction for wireless communication network

L Wu, D He, B Ai, J Wang, H Qi, K Guan… - IEEE access, 2020 - ieeexplore.ieee.org
Accurate path loss (PL) prediction is essential for predicting transmitter coverage and
optimizing wireless network performance. Traditional PL models are difficult to cope with the …

[HTML][HTML] Comparative analysis of major machine-learning-based path loss models for enclosed indoor channels

MK Elmezughi, O Salih, TJ Afullo, KJ Duffy - Sensors, 2022 - mdpi.com
Unlimited access to information and data sharing wherever and at any time for anyone and
anything is a fundamental component of fifth-generation (5G) wireless communication and …

Machine learning-based urban canyon path loss prediction using 28 ghz manhattan measurements

A Gupta, J Du, D Chizhik… - … on Antennas and …, 2022 - ieeexplore.ieee.org
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 …

Mobile network coverage prediction based on supervised machine learning algorithms

MFA Fauzi, R Nordin, NF Abdullah… - Ieee Access, 2022 - ieeexplore.ieee.org
The need for wider coverage and high-performance quality of mobile networks is critical due
to the maturity of Internet penetration in today's society. One of the primary drivers of this …

Near ground pathloss propagation model using adaptive neuro fuzzy inference system for wireless sensor network communication in forest, jungle and open dirt road …

GPN Hakim, MH Habaebi, SF Toha, MR Islam… - Sensors, 2022 - mdpi.com
In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such
as forest; jungle; and open dirt road environments; wireless communications usually suffer …

Fusing diverse input modalities for path loss prediction: A deep learning approach

SP Sotiroudis, P Sarigiannidis, SK Goudos… - Ieee …, 2021 - ieeexplore.ieee.org
Tabular data and images have been used from machine learning models as two diverse
types of inputs, in order to perform path loss predictions in urban areas. Different types of …

From Simulators to Digital Twins for Enabling Emerging Cellular Networks: A Tutorial and Survey

M Manalastas, MUB Farooq, SMA Zaidi… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Simulators are indispensable parts of the research and development necessary to advance
countless industries, including cellular networks. With simulators, the evaluation, analysis …