@inproceedings{f922dde3a819454bb6b9371af6e626aa,
title = "Relay Selection for 5G New Radio Via Artificial Neural Networks",
abstract = "Millimeter-wave supplies an alternative frequency band of wide bandwidth to better realize pillar technologies of enhanced mobile broadband (eMBB) and ultra-reliable and lowlatency communication (uRLLC) for 5G- new radio (5G-NR). When using mmWave frequency band, relay stations to assist the coverage of base stations in radio access network (RAN) emerge as an attractive technique. However, relay selection to result in the strongest link becomes the critical technology to facilitate RAN using mmWave. A disruptive approach toward relay selection is to take advantage of existing operating data and apply appropriate artificial neural networks (ANN) and deep learning algorithms to alleviate severe fading in mmWave band. In this paper, we apply classification techniques using ANN with multilayer perception to predict the path loss of multiple transmitted links and base on a certain loss level, and thus execute effective relay selection, which also recommends the handover to an appropriate path. ANN with multilayer perception are compared with other ML algorithms to demonstrate effectiveness for relay selection in 5G-NR.",
keywords = "5G-NR., Classification, Machine Learning, MmWave, Multilayer Perceptrons, Neural Network, Relay Selection, SVM and Logistic Regression, Wireless Communications",
author = "Saud Aldossari and Chen, \{Kwang Cheng\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 22nd International Symposium on Wireless Personal Multimedia Communications, WPMC 2019 ; Conference date: 24-11-2019 Through 27-11-2019",
year = "2019",
month = nov,
doi = "10.1109/WPMC48795.2019.9096156",
language = "English",
series = "International Symposium on Wireless Personal Multimedia Communications, WPMC",
publisher = "IEEE Computer Society",
booktitle = "2019 22nd International Symposium on Wireless Personal Multimedia Communications, WPMC 2019",
address = "United States",
}