Machine Learning Modeling for Radiofrequency Electromagnetic Fields (RF-EMF) Signals From mmWave 5G Signals-Reference-Cited by-同舟云学术

Machine Learning Modeling for Radiofrequency Electromagnetic Fields (RF-EMF) Signals From mmWave 5G Signals

Published:2023 Issue: Volume:11 Page:79648-79658
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Al-Jumaily Abdulmajeed¹^ORCID,Sali Aduwati¹^ORCID,Riyadh Mohammed¹^ORCID,Wali Sangin Qahtan¹^ORCID,Li Lu¹^ORCID,Osman Anwar Faizd²^ORCID

Affiliation:

1. Department of Computer and Communication Systems Engineering, WiPNET Research Centre, Faculty of Engineering, Universiti Putra Malaysia (UPM), Selangor, Serdang, Malaysia

2. Rohde & Schwarz, PAT SQUARE, Shah Alam, Selangor, Malaysia

Funder

IGNITE—Interference Modeling for 5G and FSS Coexistence at mmWave with Climate Change Considerations in the Tropical Region

BIDANET: Parametric Big Data Analytics Over Wireless Networks

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10097729.pdf?arnumber=10097729

Reference24 articles.

1. Capacity-Driven Low-Interference Fast Beam Synthesis for Next Generation Base Stations

2. Effective SSB Beam Radiation Pattern for RF-EMF Maximum Exposure Assessment to 5G Base Stations Using Massive MIMO Antennas

3. What is the radiation before 5G? A correlation study between measurements in situ and in real time and epidemiological indicators in Vallecas, Madrid

4. Machine Learning Techniques for 5G and Beyond

5. Millimeter-Wave Channel Model Parameters for Various Office Environments

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1. In-Situ Measurement of 5G Electromagnetic Exposure Levels in Urban Environments;2024 47th MIPRO ICT and Electronics Convention (MIPRO);2024-05-20

2. Architecture Design of B5G and 6G Millimeter-Wave Radio Access Network: Using Wireless Communications to Increase Coverage, Capacity and Performance;2023 16th International Conference on Developments in eSystems Engineering (DeSE);2023-12-18

3. An Exposimetric Electromagnetic Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by Means of Statistics and Convolutional Neural Networks Classification;Technologies;2023-08-24

4. Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS);Sensors;2023-07-05