Classified 3D mapping and deep learning-aided signal power estimation architecture for the deployment of wireless communication systems

Y Egi, E Eyceyurt - EURASIP Journal on Wireless Communications and …, 2022 - Springer
The traditional wireless communication systems deployment models require expensive and
time-consuming procedures, including environment selection (rural, urban, and suburban) …

Received power prediction for suburban environment based on neural network

L Wu, D He, K Guan, B Ai… - 2020 International …, 2020 - ieeexplore.ieee.org
Accurate received power prediction is important to wireless network planning and
optimization, and appropriate channel modeling approach is highly demanded. The existing …

Machine-learning and 3D point-cloud based signal power path loss model for the deployment of wireless communication systems

Y Egi, CE Otero - IEEE Access, 2019 - ieeexplore.ieee.org
Modeling signal power path loss (SPPL) for deployment of wireless communication systems
(WCSs) is one of the most time consuming and expensive processes that require data …

Research on wireless intelligent propagation model based on Deep Learning

M Wu, C Li, C Song - 2020 IEEE 9th Joint International …, 2020 - ieeexplore.ieee.org
In order to better meet the growing and diversified market demand of users, mobile
operators need to deploy a large number of base stations to improve the quality of radio …

An intelligent wireless communication model based on multi-feature fusion and quantile regression neural network

Q Zheng, M Yang, D Wang, X Tian… - Journal of Intelligent & …, 2021 - content.iospress.com
Throughout the wireless communication network planning process, efficient signal reception
power estimation is of great significance for accurate 5 G network deployment. The wireless …

Path loss prediction in urban areas: A machine learning approach

IFM Rafie, SY Lim, MJH Chung - IEEE Antennas and Wireless …, 2022 - ieeexplore.ieee.org
Propagation prediction is important in that it contributes toward optimal base station
planning and placement. This is especially relevant for 5G and other future generations of …

Artificial neural network-based uplink power prediction from multi-floor indoor measurement campaigns in 4G networks

T Mazloum, S Wang, M Hamdi… - Frontiers in Public …, 2021 - frontiersin.org
Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in
the number of user equipment has strengthened public concerns on human exposure to …

Modelling Mobile Signal Strength by Combining Geospatial Big Data and Artificial Intelligence

P Fraccaro, M Benatan, K Reusch, C Fare… - Proceedings of the …, 2020 - dl.acm.org
Estimating mobile signal strength accurately is a crucial task for network providers and their
customers. However, current methodologies to estimate mobile signal strength present …

Determination of the best location for setting up a transmission tower in the city

S Bharadwaj, R Dubey, S Biswas - … International Conference on …, 2020 - ieeexplore.ieee.org
Mobile phones plays a vital role in humans' life due to day by day growth in the mobile
industry. The cost of deploying, managing, and maintaining the network infrastructure is …

Deep learning-based signal strength prediction using geographical images and expert knowledge

J Thrane, B Sliwa, C Wietfeld… - … 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
Methods for accurate prediction of radio signal quality parameters are crucial for
optimization of mobile networks, and a necessity for future autonomous driving solutions …