@inproceedings{661b57a8925c4b0abffe34d4349e1113,
title = "Optimization of 5G Infrastructure Deployment Through Machine Learning",
abstract = "The application of machine learning for optimal deployment of 5G infrastructure, such as the position and the orientation of the antenna that help achieve the best signal coverage, is investigated in this paper. This avoids the need to perform on-site measurements or extensive software simulations. Multivariate Regression (MR) and Neural Network (NN) models were applied to predict the signal coverage in an indoor environment. The results showed that the average prediction error using NN for the case investigated is 7 dB for a 60-GHz operating frequency, whereas the error using the MR technique is lower than 6 dB. The unique aspect in our work is the integration of the clustering algorithm and the NN machine learning model for predicting indoor signal coverage.",
author = "Ziheng Fu and Swagato Mukherjee and Lanagan, {Michael T.} and Prasenjit Mitra and Tarun Chawla and Narayanan, {Ram M.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 ; Conference date: 10-07-2022 Through 15-07-2022",
year = "2022",
doi = "10.1109/AP-S/USNC-URSI47032.2022.9887015",
language = "English (US)",
series = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1684--1685",
booktitle = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings",
address = "United States",
}