Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting |
| Untertitel | (AP-S/URSI) |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 1684-1685 |
| Seitenumfang | 2 |
| ISBN (elektronisch) | 9781665496582 |
| ISBN (Print) | 978-1-6654-9657-5, 978-1-6654-9659-9 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Denver, USA / Vereinigte Staaten Dauer: 10 Juli 2022 → 15 Juli 2022 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Signalverarbeitung
- Physik und Astronomie (insg.)
- Instrumentierung
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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting : (AP-S/URSI). Institute of Electrical and Electronics Engineers Inc., 2022. S. 1684-1685.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Optimization of 5G Infrastructure Deployment Through Machine Learning
AU - Fu, Ziheng
AU - Mukherjee, Swagato
AU - Lanagan, Michael T.
AU - Mitra, Prasenjit
AU - Chawla, Tarun
AU - Narayanan, Ram M.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85139798020&partnerID=8YFLogxK
U2 - 10.1109/AP-S/USNC-URSI47032.2022.9887015
DO - 10.1109/AP-S/USNC-URSI47032.2022.9887015
M3 - Conference contribution
AN - SCOPUS:85139798020
SN - 978-1-6654-9657-5
SN - 978-1-6654-9659-9
SP - 1684
EP - 1685
BT - 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022
Y2 - 10 July 2022 through 15 July 2022
ER -