Physics-Informed Machine Learning Modelling of RF-EMF Exposure in Massive MIMO Systems-Reference-Cited by-同舟云学术

Physics-Informed Machine Learning Modelling of RF-EMF Exposure in Massive MIMO Systems

Published:2024 Issue: Volume:12 Page:69410-69422
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Bilson Samuel¹^ORCID,Hong Loh Tian²^ORCID,Héliot Fabien³^ORCID,Thompson Andrew¹

Affiliation:

1. Department of Data Science, National Physical Laboratory, Teddington, U.K.

2. Department of Electromagnetic and Electrochemical Technologies, National Physical Laboratory, Teddington, U.K.

3. Institute for Communication Systems (ICS), 5GIC and 6GIC, University of Surrey, Guildford, U.K.

Funder

National Measurement System Program of the U.K. Government’s Department for Science, Innovation and Technology (DSIT), under Science Theme Reference EMT23 and EMT24 of that Program

European Association of National Metrology Institutes (EURAMET) European Partnership on Metrology co-financed from European Union’s Horizon Europe Research and Innovation Programme

Metrology for Emerging Wireless Standards

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10533177.pdf?arnumber=10533177

Reference37 articles.

1. An assessment of the radio frequency electromagnetic field exposure from a massive MIMO 5G testbed;Loh

2. A study of experiment-based radio frequency electromagnetic field exposure evidence on stochastic nature of a massive MIMO system;Loh

3. Metrology for RF-exposure from massive MIMO system

4. An empirical study of the stochastic nature of electromagnetic field exposure in massive MIMO systems;Héliot;IEEE Access,2022

5. Time-averaged realistic maximum power levels for the assessment of radio frequency exposure for 5G radio base stations using massive MIMO;Thors;IEEE Access,2017