@inproceedings{e099f4562e474e34b6397446d7c34923,
title = "Overcoming Wireless Channel modeling and Relay Signal Selection Via Artificial Intelligence Techniques in the 5G and Beyond",
abstract = "Wireless technology has faced technical challenges that have been unresolved or only partially addressed. Issues such as modeling the wireless channel and selecting the optimum signal This paper proposes using Artificial Intelligence (AI) to tackle these concerns. Machine Learning (ML) can estimate wireless channel states based on available data. Regression and classification techniques have been used to improve communication and meet 5G standards. The effectiveness of ML and Deep Learning techniques were compared to achieve the best accuracy. This paper shows how AI can revolutionize the design of 5G-NR and future generations with an accurate prediction of 99.99\%.",
keywords = "Classification, Machine Learning, MmWave, Multilayer Perceptrons, Neural Network, SVM and Logistic Regression, Wireless Communications",
author = "Aldossari, \{Saud Alhajaj\} and Abdullah Aldosary and Chen, \{Kwang Cheng\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Ubiquitous and Future Networks, ICUFN 2023 ; Conference date: 04-07-2023 Through 07-07-2023",
year = "2023",
doi = "10.1109/ICUFN57995.2023.10200723",
language = "English",
series = "International Conference on Ubiquitous and Future Networks, ICUFN",
publisher = "IEEE Computer Society",
pages = "810--815",
booktitle = "ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks",
address = "United States",
}