{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:50:17Z","timestamp":1783702217934,"version":"3.55.0"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Measurement System Program of the U.K. Government\u2019s Department for Science, Innovation and Technology (DSIT), under Science Theme Reference EMT23 and EMT24 of that Program"},{"name":"European Association of National Metrology Institutes (EURAMET) European Partnership on Metrology co-financed from European Union\u2019s Horizon Europe Research and Innovation Programme","award":["21NRM03"],"award-info":[{"award-number":["21NRM03"]}]},{"name":"Metrology for Emerging Wireless Standards"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3398992","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T17:31:51Z","timestamp":1715880711000},"page":"69410-69422","source":"Crossref","is-referenced-by-count":19,"title":["Physics-Informed Machine Learning Modelling of RF-EMF Exposure in Massive MIMO Systems"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1705-2661","authenticated-orcid":false,"given":"Samuel","family":"Bilson","sequence":"first","affiliation":[{"name":"Department of Data Science, National Physical Laboratory, Teddington, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3486-2659","authenticated-orcid":false,"given":"Tian","family":"Hong Loh","sequence":"additional","affiliation":[{"name":"Department of Electromagnetic and Electrochemical Technologies, National Physical Laboratory, Teddington, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3583-3435","authenticated-orcid":false,"given":"Fabien","family":"H\u00e9liot","sequence":"additional","affiliation":[{"name":"Institute for Communication Systems (ICS), 5GIC and 6GIC, University of Surrey, Guildford, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Thompson","sequence":"additional","affiliation":[{"name":"Department of Data Science, National Physical Laboratory, Teddington, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"An assessment of the radio frequency electromagnetic field exposure from a massive MIMO 5G testbed","volume-title":"Proc. 14th Eur. Conf. Antennas Propag. (EuCAP)","author":"Loh"},{"key":"ref2","first-page":"1","article-title":"A study of experiment-based radio frequency electromagnetic field exposure evidence on stochastic nature of a massive MIMO system","volume-title":"Proc. 15th Eur. Conf. Antennas Propag. (EuCAP)","author":"Loh"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1049\/pbte099e_ch21"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"63100","DOI":"10.1109\/ACCESS.2022.3182236","article-title":"An empirical study of the stochastic nature of electromagnetic field exposure in massive MIMO systems","volume":"10","author":"H\u00e9liot","year":"2022","journal-title":"IEEE Access"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"19711","DOI":"10.1109\/ACCESS.2017.2753459","article-title":"Time-averaged realistic maximum power levels for the assessment of radio frequency exposure for 5G radio base stations using massive MIMO","volume":"5","author":"Thors","year":"2017","journal-title":"IEEE Access"},{"issue":"5","key":"ref6","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1097\/HP.0000000000001210","article-title":"International Commission on Non-Ionizing Radiation Protection (ICNIRP), \u201cGuidelines for limiting exposure to electromagnetic fields (100 kHz to 300 GHz)","volume":"118","year":"2020","journal-title":"Health Phys."},{"issue":"2","key":"ref7","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1097\/HP.0000000000001301","article-title":"IEEE committee on man and radiation\u2014COMAR technical information statement: Health and safety issues concerning exposure of the general public to electromagnetic energy from 5G wireless communications networks","volume":"119","author":"Bushberg","year":"2020","journal-title":"Health Phys."},{"key":"ref8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.envint.2014.03.007","article-title":"Radio-frequency electromagnetic field (RF-EMF) exposure levels in different European outdoor urban environments in comparison with regulatory limits","volume":"68","author":"Urbinello","year":"2014","journal-title":"Environ. Int."},{"key":"ref9","first-page":"1","article-title":"A statistical approach for RF exposure compliance boundary assessment in massive MIMO systems","volume-title":"Proc. WSA 22nd Int. ITG Workshop Smart Antennas","author":"Baracca"},{"key":"ref10","volume-title":"Determination of RF Field Strength, Power Density and SAR in the Vicinity of Base Stations for the Purpose of Evaluating Human Exposure","year":"2022"},{"key":"ref11","volume-title":"Impact of EMF Limits on 5G Network Roll-Out","author":"T\u00f6rnevik","year":"2017"},{"key":"ref12","volume-title":"Case Studies Supporting IEC 62232\u2014Determination of RF Field Strength, Power Density and SAR in the Vicinity of Radiocommunication Base Stations for the Purpose of Evaluating Human Exposure","year":"2019"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1097\/HP.0000000000001089"},{"key":"ref14","volume-title":"Measurement method for 5G NR base stations up to 6 GHz","year":"2020"},{"issue":"15","key":"ref15","doi-asserted-by":"crossref","first-page":"5280","DOI":"10.3390\/app10155280","article-title":"Analysis of the actual power and EMF exposure from base stations in a commercial 5G network","volume":"10","author":"Colombi","year":"2020","journal-title":"Appl. Sci."},{"key":"ref16","doi-asserted-by":"crossref","first-page":"184658","DOI":"10.1109\/ACCESS.2019.2961225","article-title":"In-situ measurement methodology for the assessment of 5G NR massive MIMO base station exposure at sub-6 GHz frequencies","volume":"7","author":"Aerts","year":"2019","journal-title":"IEEE Access"},{"issue":"1","key":"ref17","doi-asserted-by":"crossref","first-page":"121","DOI":"10.3390\/electronics11010121","article-title":"Machine learning for physical layer in 5G and beyond wireless networks: A survey","volume":"11","author":"Tanveer","year":"2021","journal-title":"Electronics"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.47120\/npl.ms40"},{"issue":"7","key":"ref19","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.3390\/s20071927","article-title":"Path loss prediction based on machine learning techniques: Principal component analysis, artificial neural network, and Gaussian process","volume":"20","author":"Jo","year":"2020","journal-title":"Sensors"},{"issue":"6","key":"ref20","doi-asserted-by":"crossref","first-page":"3955","DOI":"10.1109\/TAP.2022.3149665","article-title":"Artificial intelligence enabled radio propagation for communications\u2014Part II: Scenario identification and channel modeling","volume":"70","author":"Huang","year":"2022","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TAP.2022.3149663"},{"key":"ref22","first-page":"1","article-title":"Beamformed fingerprint learning for accurate millimeter wave positioning","volume-title":"Proc. IEEE 88th Veh. Technol. Conf. (VTC-Fall)","author":"Gante"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"111030","DOI":"10.1109\/ACCESS.2021.3102825","article-title":"Predicting power density of array antenna in mmWave applications with deep learning","volume":"9","author":"Bang","year":"2021","journal-title":"IEEE Access"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1002\/bem.22361"},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2021.777798","article-title":"Artificial neural network-based uplink power prediction from multi-floor indoor measurement campaigns in 4G networks","volume":"9","author":"Mazloum","year":"2021","journal-title":"Frontiers Public Health"},{"issue":"3","key":"ref26","doi-asserted-by":"crossref","first-page":"396","DOI":"10.3390\/telecom3030021","article-title":"Prediction of RF-EMF exposure by outdoor drive test measurements","volume":"3","author":"Wang","year":"2022","journal-title":"Telecom"},{"issue":"5","key":"ref27","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1109\/JRPROC.1946.234568","article-title":"A note on a simple transmission formula","volume":"34","author":"Friis","year":"1946","journal-title":"Proc. IRE"},{"key":"ref28","volume-title":"5G\/6G Innovation Centre","year":"2023"},{"key":"ref29","volume-title":"PathWave System Design (SystemVue)","year":"2023"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1023\/a:1010933404324"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7_10"},{"key":"ref33","article-title":"Practical Bayesian optimization of machine learning algorithms","volume":"25","author":"Snoek","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref34","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref35","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J. Comput. Phys."},{"issue":"6","key":"ref36","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nature Rev. Phys."},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-022-01939-z"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10533177.pdf?arnumber=10533177","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T04:52:11Z","timestamp":1716353531000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10533177\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3398992","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}