Optimization of 5G Infrastructure Deployment Through Machine Learning

Ziheng Fu; Swagato Mukherjee; Michael T. Lanagan; Prasenjit Mitra; Tarun Chawla; Ram M. Narayanan

doi:10.1109/AP-S/USNC-URSI47032.2022.9887015

Optimization of 5G Infrastructure Deployment Through Machine Learning

Ziheng Fu, Swagato Mukherjee, Michael T. Lanagan, Prasenjit Mitra, Tarun Chawla, Ram M. Narayanan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	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
Number of pages	2
ISBN (Electronic)	9781665496582
DOIs	https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887015
State	Published - 2022
Event	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Denver, United States Duration: Jul 10 2022 → Jul 15 2022

Publication series

Name	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

Conference

Conference	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022
Country/Territory	United States
City	Denver
Period	7/10/22 → 7/15/22

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing
Instrumentation

Access to Document

10.1109/AP-S/USNC-URSI47032.2022.9887015

Cite this

Fu, Z., Mukherjee, S., Lanagan, M. T., Mitra, P., Chawla, T., & Narayanan, R. M. (2022). Optimization of 5G Infrastructure Deployment Through Machine Learning. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings (pp. 1684-1685). (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887015

Fu, Ziheng ; Mukherjee, Swagato ; Lanagan, Michael T. et al. / Optimization of 5G Infrastructure Deployment Through Machine Learning. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1684-1685 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

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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.}",

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year = "2022",

doi = "10.1109/AP-S/USNC-URSI47032.2022.9887015",

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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.",

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Fu, Z, Mukherjee, S, Lanagan, MT, Mitra, P, Chawla, T & Narayanan, RM 2022, Optimization of 5G Infrastructure Deployment Through Machine Learning. in 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1684-1685, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022, Denver, United States, 7/10/22. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887015

Optimization of 5G Infrastructure Deployment Through Machine Learning. / Fu, Ziheng; Mukherjee, Swagato; Lanagan, Michael T. et al.
2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1684-1685 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Narayanan, Ram M.

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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.

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Fu Z, Mukherjee S, Lanagan MT, Mitra P, Chawla T, Narayanan RM. Optimization of 5G Infrastructure Deployment Through Machine Learning. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1684-1685. (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). doi: 10.1109/AP-S/USNC-URSI47032.2022.9887015

Optimization of 5G Infrastructure Deployment Through Machine Learning

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