Low Electromagnetic Field Exposure Wireless Devices E-Book
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- Herausgeber: John Wiley & Sons
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- Sprache: Englisch
LOW ELECTROMAGNETIC FIELD EXPOSURE WIRELESS DEVICES Comprehensive resource covering methods of designing energy efficient and low EMF wireless device techniques Supported with real case studies and recent advancements and laying the foundation for future advancements in the field, Low Electromagnetic Field Exposure Wireless Devices: Fundamentals and Recent Advances describes both ways, i.e. hardware and software, in which the user-centric wireless communication devices can be designed to reduce the levels of EMF to limit the potential long-term effects of EMF on human health. The text covers state-of-the-art and advanced topics such as EMF exposure standards and rationale, EMF evaluation tools, radio resource allocation, energy conservation, energy harvesting, EMF-aware antenna designs, and MIMO, and highlights advancements in this exciting field to date. To aid reader comprehension, the text contains numerous tables, illustrations, and photographs. In Low Electromagnetic Field Exposure Wireless Devices: Fundamentals and Recent Advances, readers can expect to find information on: * Fundamentals and key practices, and mechanisms and assessment methods, of exposure to electromagnetic fields * The role of the smartphone on the assessment of exposure from 5G and antenna design considerations and techniques for low SAR mobile handsets * Numerical exposure assessments of communication systems at higher frequencies and age-dependent exposure estimation using numerical methods * Reinforcement learning and device-to-device communication in minimizing EMF exposure and emission-aware uplink resource allocation scheme for non-orthogonal multiple access systems For wireless user equipment designers and hardware engineers, teachers in wireless communications, and postgraduate students in antennas for communication systems, Low Electromagnetic Field Exposure Wireless Devices: Fundamentals and Recent Advances is a must-have resource, covering an important topic that is expected to only grow in significance as future technological developments are made.
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Table of Contents
Cover
Title Page
Copyright
Dedication
Editor Biography
List of Contributors
Preface
1 Electromagnetic Field Exposure: Fundamentals and Key Practices
1.1 Introduction
1.2 EMF Metric and Evaluation Framework
1.3 Application of Metric for Setting Guidelines/Limits and Reducing Exposure
1.4 Conclusion
References
2 Exposure to Electromagnetic Fields Emitted from Wireless Devices: Mechanisms and Assessment Methods
2.1 Fundamentals of EMF Interactions with the Human Body
2.2 Physical Models to Represent the Interaction of EMFs with Biological Tissue
2.3 Dosimetry Concepts
2.4 Dosimetry Methodology
2.5 Numerical Dosimetry at the Radiofrequency and Microwave Regions
References
3 Numerical Exposure Assessments of Communication Systems at Higher Frequencies
3.1 Introduction
3.2 Exposure Configuration
3.3 Plane Wave Exposure Assessment of E‐field Absorption Within the Skin Using SAR as a Function of Frequency
3.4 Plane Wave Exposure Assessment of E‐field Absorption Within Multi‐layer Model Using SAR as a Function of Frequency
3.5 Plane Wave Exposure Assessment of E‐field Absorption Within the Eye Using SAR as a Function of Frequency
3.6 Chapter Summary
Appendix 3.A
References
4 Age Dependent Exposure Estimation Using Numerical Methods
4.1 Introduction
4.2 Numerical Human Models
4.3 Age‐Dependent Tissue Properties
4.4 Numerical Validation
4.5 Chapter Summary
Appendix 4.A
References
5 Antenna Design Considerations for Low SAR Mobile Terminals
5.1 Introduction
5.2 SAR Reduction and Dual Coupling of Antenna
5.3 Coupling Manipulation Simulation Campaign
5.4 SAR Analysis and Surface Current
5.5 Resilience to Different Head Use Cases
5.6 Analysis of MIMO Performance in Data Mode
5.7 Conclusion
References
6 MIMO Antennas with Coupling Manipulation for Low SAR Devices
6.1 Introduction
6.2 Working Principle and Antenna Geometry
6.3 Antenna Measurements
6.4 Efficiency and SAR Analysis
6.5 Conclusion
References
7 Reinforcement Learning and Device‐to‐Device Communication for Low EMF Exposure
7.1 Introduction
7.2 Background
7.3 Related Works
7.4 System Model, Problem Formulation, and Proposed RL‐ID2D
7.5 Performance Evaluation
7.6 Conclusion
References
8 Unsupervised Learning Based Resource Allocation for Low EMF NOMA Systems
8.1 Introduction
8.2 EMF‐Aware PD‐NOMA Framework
8.3 Machine Learning Based User Grouping/Subcarrier Allocation
8.4 Power Assignment
8.5 Numerical Analysis
8.6 Conclusion
References
9 Emission‐Aware Resource Optimization for Backscatter‐Enabled NOMA Networks
9.1 Introduction
9.2 System Model
9.3 Proposed Solution
9.4 Performance Evaluation
9.5 Conclusion
References
10 Road Ahead for Low EMF User Proximity Devices
10.1 Introduction
10.2 Perception and Physiological Impact of EMF
10.3 EMF Exposure Evaluation Metric and Regulations: A Future Perspective
10.4 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Frequency characterization of the main biological tissue relaxati...
Table 2.2 A comparison of the current numerical and experimental electromag...
Chapter 4
Table 4.1 Body water (kg) for young and elderly men, and elderly women from...
Table 4.2 Human age predictions using animals weight [2, 10, 18].
Table 4.3 Parameters for age‐dependent generalized expression (ID: Interver...
Table 4.4 Comparison between measured animal [2] permittivity and conductiv...
Table 4.5 Cole–Cole parameters for the dielectric properties of human tissu...
Table 4.A.1 Dielectric properties of 40 different tissue types assigned to N...
Table 4.A.2 Dielectric properties of 38 different tissue types assigned to N...
Table 4.A.3 Dielectric properties of 75 different tissue types of Eartha at
Chapter 5
Table 5.1 Adjusted values of
for each
.
Table 5.2 Comparison of UT metrics with
mm and
mm at 2.4 GHz. Simulated...
Chapter 6
Table 6.1 Changes in SAR (10g (W/Kg)) value for scenarios when DGS is eithe...
Table 6.2 Changes in overall antenna efficiency in (dB)
Chapter 7
Table 7.1 Simulation parameters
Chapter 8
Table 8.1 Notations with definitions used throughout the chapter.
Table 8.2 Default simulation parameters.
List of Illustrations
Chapter 1
Figure 1.1 Common EMF exposure sources generally present in the environment ...
Figure 1.2 Antenna field areas are depicted [26].
Figure 1.3 The most often used metrics for assessing EMF exposure [9].
Figure 1.4 A typical SAR measuring setup is depicted.
Figure 1.5 An overview of PD measurement.
Chapter 2
Figure 2.1 An illustration of the effects of the applied electric field (E) ...
Figure 2.2 Flowchart showing the basic IEEE procedure for peak SAR numerical...
Figure 2.3 A valid setup for the SAR averaging volumes. Based on IEC/IEEE In...
Figure 2.4 Assignment of SAR values for each tissue sub‐volume. Based on IEC...
Figure 2.5 Schematic diagram of the probe field measurement within liquid ph...
Figure 2.6 Schematic diagram of the thermographic measurement set‐up.
Figure 2.7 Computation procedure for computing the EM, SAR, and thermal dist...
Figure 2.8 Operation of the FDTD program. * The FDTD solver iteration chart ...
Figure 2.9 A Flow chart showing the computation steps of the main FDTD progr...
Figure 2.10 The file structure for (a) model voxel configurations; and (b) t...
Figure 2.11 Plots of the normalized analytically assessed SAR patterns along...
Figure 2.12 Plots of the normalized FDTD‐computed SAR patterns along the maj...
Figure 2.13 Plots of the normalized analytically assessed SAR patterns along...
Figure 2.14 Plots of the normalized FDTD‐computed SAR patterns along the maj...
Figure 2.15 Plots of the normalized FDTD‐computed SAR patterns along the maj...
Chapter 3
Figure 3.1 Plane wave exposure configuration of (a) Single‐layer skin‐equiva...
Figure 3.2 3D view of HHECM, where all dimensions are in mm.
Figure 3.3 Sub‐volume dimensions of HHECM; where “d” is the variable distanc...
Figure 3.4 Comparison of SAR values of dry‐ and wet‐skin HHECM with its sub‐...
Figure 3.5 Comparison of SAR values of dry‐ and wet‐skin HHECM with its sub‐...
Figure 3.6 Comparison of SAR values of dry‐ and wet‐skin HHECM with its sub‐...
Figure 3.7 Comparison of SAR values of dry‐ and wet‐skin HHECM with its sub‐...
Figure 3.8 E‐field absorption within the dry‐skin from surface to 50 mm deep...
Figure 3.9 E‐field absorption within the wet‐skin from surface to 50 mm deep...
Figure 3.10 Dimensions of multi‐layer HHECM; The outer, middle and inner cub...
Figure 3.11 Comparison of SAR values of dry‐skin and multi‐layer HHECM with ...
Figure 3.12 Comparison of SAR values of dry‐skin and multi‐layer HHECM with ...
Figure 3.13 Comparison of SAR values of dry‐skin and multi‐layer box phantom...
Figure 3.14 Comparison of SAR values of dry‐skin and multi‐layer box phantom...
Figure 3.15 E‐field absorption within the multi‐layer model (dry‐skin, fat, ...
Figure 3.16 Comparison of maximum penetration depth of EM waves within dry‐ ...
Figure 3.17 HEECM composition with its dimensions.
Figure 3.18 Comparison of SAR values of HEECM and multi‐layer model (size no...
Figure 3.19 Comparison of maximum SAR values between HEECM and multi‐layer m...
Figure 3.20 E‐field absorption within the eye from surface to 50 mm deep ins...
Figure 3.21 Maximum penetration depth of EM waves within eye from the surfac...
Figure 3.A.1 Debye model fitted permittivity values of dry‐ and wet‐skin, fa...
Figure 3.A.2 Debye model fitted conductivity values of dry‐ and wet‐skin, fa...
Figure 3.A.3 Comparison of SAR values of dry‐skin equivalent HHECM with its ...
Figure 3.A.4 Comparison of SAR values of dry‐skin equivalent HHECM with its ...
Figure 3.A.5 Comparison of SAR values of dry‐skin equivalent HHECM with its ...
Figure 3.A.6 Comparison of SAR values of dry‐skin equivalent HHECM with its ...
Figure 3.A.7 Comparison of SAR values of dry‐skin HHECM with its sub‐volume ...
Figure 3.A.8 Comparison of SAR values of HEECM and multi‐layer model (size n...
Figure 3.A.9 Comparison of SAR values of HEECM and multi‐layer model (size n...
Figure 3.A.10 Comparison of SAR values of HEECM and multi‐layer model (size ...
Figure 3.A.11 Comparison of SAR values of HEECM and multi‐layer model (size ...
Figure 3.A.12 Comparison of SAR values of HEECM and multi‐layer model (size ...
Figure 3.A.13 Comparison of SAR values of HEECM and multi‐layer model (size ...
Chapter 4
Figure 4.1 Volume rendered images of the female model; (a) The outside surfa...
Figure 4.2 Volume rendered images of the male model; (a) The outside surface...
Figure 4.3 Volume rendered images of the female child model; (a) The outside...
Figure 4.4 Permittivity of Fat at
1 GHz
.
Figure 4.5 Conductivity of Fat at
1 GHz
.
Figure 4.6 Comparison between age‐dependent properties of fat tissue with on...
Figure 4.7 Age‐dependent fitted values for relaxation parameters of the Cole...
Figure 4.8 Plots of the normalized FIT‐computed SAR patterns along the major...
Figure 4.A.1 Comparison between age‐dependent properties of bone marrow 30% ...
Figure 4.A.2 Comparison between age‐dependent properties of bone marrow 50% ...
Figure 4.A.3 Comparison between age‐dependent properties of cornea tissue wi...
Figure 4.A.4 Comparison between age‐dependent properties of dura tissue with...
Figure 4.A.5 Comparison between age‐dependent properties of gray matter tiss...
Figure 4.A.6 Age‐dependent properties of intervertebral disc centre. Due to ...
Figure 4.A.7 Comparison between age‐dependent properties of intervertebral d...
Figure 4.A.8 Comparison between age‐dependent properties of long bone tissue...
Figure 4.A.9 Comparison between age‐dependent properties of skin tissue with...
Figure 4.A.10 Comparison between age‐dependent properties of skull tissue wi...
Figure 4.A.11 Comparison between age‐dependent properties of spinal cord tis...
Figure 4.A.12 Comparison between age‐dependent properties of tongue tissue w...
Figure 4.A.13 Comparison between age‐dependent properties of white matter ti...
Chapter 5
Figure 5.1 A traditional PIFA illustration arranged in a two‐element MIMO la...
Figure 5.2 Simulated
and
of a two‐element MIMOPIFA arrangement, position...
Figure 5.3 Following the IEEE guidelines and aligning UE with the homogeneou...
Figure 5.4 Variation of the overall efficiency and the SAR for a sphere with...
Figure 5.5 SAR and overall efficiency are varied by adjusting the difference...
Figure 5.6 Current distribution comparison of various PIFA configurations al...
Figure 5.7 Cross section comparison of SAR of various PIFA configurations al...
Figure 5.8 Cross section comparison of SAR of various PIFA configurations al...
Figure 5.9 SAR vs. phase angle variations between elements of PIFA for a fix...
Figure 5.10 The variation of SAR vs. phase angle between elements with fixed...
Figure 5.11 An SAR penetration comparison aligned with voxel model using a h...
Figure 5.12 Prototype of the UE with (a)
mm and (b)
mm.
Figure 5.13 Comparison of simulated and measured S‐parameters for the UE wit...
Chapter 6
Figure 6.1 The proposed two element MIMO‐enabled dual band rim antenna's geo...
Figure 6.2 Surface current plots simulated (a) 2.1 GHz (b) 4.3 GHz.
Figure 6.3 Surface current graphs simulated at 2.1 GHz and 4.3 GHz for scena...
Figure 6.4 An example of a CMA study of a shattered metallic rim. (a) Ground...
Figure 6.5 Prototype of (a) DGS ON configuration (b) DGS OFF configuration o...
Figure 6.6 S‐parameters simulation vs. measurement.
Figure 6.7 Far‐field radiation pattern simulation vs. measurement (a) Azimut...
Figure 6.8 An example of a voxel model used to investigate the influence of
Figure 6.9 Demonstration of the influence of hand and LCD on the antenna des...
Chapter 7
Figure 7.1 (a) NB‐IoT deployment modes, (b) NB‐IoT frame structure.
Figure 7.2 Few use cases of D2D comm unication.
Figure 7.3 D2D communication relay scenarios.
Figure 7.4 Distributed direct discovery process.
Figure 7.5 Narrowband‐D2D system model.
Figure 7.6 The agent‐environment interaction in a Markov decision process us...
Figure 7.7 Cumulative
‐value vs. steps.
Figure 7.8 EDR vs. steps.
Figure 7.9 EDR vs. QL parameters.
Figure 7.10 EDR vs. transmission power.
Figure 7.11 EDR vs D2D transmission range.
Figure 7.12 Average E2E delay vs. distance.
Figure 7.13 Average E2E delay vs number of cellular relays.
Figure 7.14 Comparison of RL‐ID2D with opportunistic and deterministic model...
Figure 7.15 Comparison of RL‐ID2D with opportunistic and deterministic model...
Chapter 8
Figure 8.1 An illustration of a single cell system with multiple users.....
Figure 8.2
‐test statistic in 8.11 as a function of
for
,
, and
.....
Figure 8.3 The comparison of total EMF uplink exposure vs. the target number...
Figure 8.4 The comparison of total EMF uplink exposure vs. varying the numbe...
Figure 8.5 The comparison of total EMF uplink exposure vs. varying the time ...
Figure 8.6 The comparison of total EMF uplink exposure vs. varying the targe...
Figure 8.7 Illustration of SAR testing positions (a) Cheek position, (b) Til...
Figure 8.8 Comparison of total uplink exposure with target number of bits fo...
Chapter 9
Figure 9.1 Illustration of the system model.
Figure 9.2 Silhouette values vs.
for
,
, and
.
Figure 9.3 Comparing combined uplink EMF against a variation in the bits for...
Figure 9.4 Comparing combined uplink EMF against a variation in the number o...
Chapter 10
Figure 10.1 Exposure reduction scenario using MIMO antennas.
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
Editor Biography
List of Contributors
Preface
Begin Reading
Index
End User License Agreement
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Low Electromagnetic Field Exposure Wireless Devices
Fundamentals and Recent Advances
Edited by
Masood Ur RehmanUniversity of Glasgow, Glasgow, UK
Muhammad Ali JamshedUniversity of Glasgow, Glasgow, UK
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Library of Congress Cataloging‐in‐Publication Data Applied for:Hardback ISBN: 9781119909163
Cover Design: WileyCover Image: © whiteMocca/Shutterstock
I dedicate this effort to my parents, Khalil Ur Rehman and Ilfaz Begum;my siblings, Habib, Waheed, Tahera;my wife, Faiza;and my son, Musaab.
Masood Ur Rehman
I dedicate this effort to my parents, Jamshed Iqbal and Nuzhut Jamshed;my siblings, Laiba, Maliha, Mariam;and my wife, Aqsa Tariq.
Muhammad Ali Jamshed
Editor Biography
Masood Ur Rehman received a B.Sc. degree in electronics and telecommunication engineering from the University of Engineering and Technology, Lahore, Pakistan, in 2004 and a M.Sc. and Ph.D. degrees in electronic engineering from Queen Mary University of London, London, UK, in 2006 and 2010, respectively. He worked at Queen Mary University of London as a Postdoctoral Research Assistant until 2012 before joining the Centre for Wireless Research at the University of Bedfordshire as a Lecturer. He served briefly at the University of Essex, UK and then moved to the James Watt School of Engineering at the University of Glasgow, UK in the capacity of an Assistant Professor. His research interests include compact antenna design, radiowave propagation and channel characterization, satellite navigation system antennas in cluttered environment, electromagnetic wave interaction with human body, body‐centric wireless networks and sensors, remote health care technology, mmWave and nano‐communications for body‐centric networks, and D2D/H2H communications. He has worked on a number of projects supported by industrial partners and research councils. He has contributed to a patent and authored/co‐authored 4 books, 7 book chapters, and more than 120 technical articles in leading journals and peer reviewed conferences. Dr. Ur Rehman is a fellow of the Higher Education Academy, UK, a member of the IET and part of the technical program committees and organizing committees of several international conferences, workshops, and special sessions. He is acting as an Associate Editor of the IEEE Access and IET Electronics Letters and Lead Guest Editor of numerous special issues of renowned journals. He also serves as a reviewer for book publishers, IEEE conferences, and leading journals.
Muhammad Ali Jamshed received a Ph.D. degree from the University of Surrey, Guildford, UK, in 2021. He is endorsed by Royal Academy of Engineering under exceptional talent category. He was nominated for Departmental Prize for Excellence in Research in 2019 at the University of Surrey. He served briefly as Wireless Research Engineer at BriteYellow Ltd., UK, and then moved to James Watt School of Engineering, University of Glasgow, as a Post‐Doctoral Research Assistant. He has authored/co‐authored 2 book chapters and more than 37 technical articles in leading journals and peer reviewed conferences. His main research interests include EMF exposure reduction, low SAR antennas for mobile handsets, machine learning for wireless communication, Backscatter communication, and wireless sensor networks. He served as a Reviewer, TPC, and the Session Chair, at many well‐known conferences, i.e. ICC, WCNC, VTC, GlobeCom etc., and other scientific workshops.
List of Contributors
Yasir Alfadhl
School of Electronic Engineering and Computer Science
Queen Mary University of London
London
UK
Tim W.C. Brown
Institute of Communication Systems (ICS)
Home of 5G and 6G Innovation Centre, University of Surrey
Guildford
UK
Xiaodong Chen
School of Electronic Engineering and Computer Science
Queen Mary University of London
London
UK
Fabien Héliot
Institute of Communication Systems (ICS)
Home of 5G and 6G Innovation Centre, University of Surrey
Guildford
UK
Muhammad Ali Imran
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Muhammad Ali Jamshed
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Wali Ullah Khan
Interdisciplinary Center for Security Reliability and Trust (SnT)
University of Luxembourg
Luxembourg City
Luxembourg
Sung Won Kim
Department of Information and Communication Engineering
Yeungnam University
Gyeongsan‐si
South Korea
Ali Nauman
Department of Information and Communication Engineering
Yeungnam University
Gyeongsan‐si
South Korea
Haris Pervaiz
School of Computing and Communications
Lancaster University
Lancaster
UK
Muhammad Rafaqat Ali Qureshi
School of Electronic Engineering and Computer Science
Queen Mary University of London
London
UK
Masood Ur Rehman
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Preface
The past decade has seen a huge upsurge in the demand of wireless devices that are expected to cross the 29.4 billion mark by 2030. This increase is fueled by the advances in wearables, portables, flexible electronics, and other wireless technologies facilitating communication, transportation, and navigation needs of billions of users around the world in the wake of Internet of Things and 5G/6G. These rising numbers, along with ever‐growing data requirements, necessitate a growth in the capacity of wireless communication networks by almost 1000 times. Part of this capacity enhancement will be made possible by increasing the number of access points (APs). These developments are ultimately resulting in added electromagnetic field (EMF) exposure sources in the environment.
EMF exposure has been deemed prone to inflict adverse health and safety effects on the users. The World Health Organization (WHO) has classified these EMF radiations as possibly carcinogenic to humans and has an ongoing project to assess potential health effects of exposure to EMF in the general and working population. The Federal Communications Commission (FCC) and the International Commission on Non‐Ionizing Radiation Protection (ICNIRP) have, therefore, imposed strict safety standards for device operation. Consequently, EMF exposure characterization warranting strict adherence to these safety regulations is a vital design parameter for wireless devices to ensure the safety of the users.
The current developments and expected future growth of the wireless systems are also mounting concerns regarding users' safety and possible health consequences of EMF exposure to modern wireless technologies, such as millimeter‐wave (mmWave) communications, massive multiple‐input multiple‐output (MIMO), and beamforming. It necessitates deeper investigations on health risk assessments and requires a comprehensive reference dealing with this fundamental and paramount issue suggesting some novel directions for updating the EMF exposure evaluation framework.
A dedicated book tackling this important issue is seldom available. Therefore, this volume will not only fill this gap but also educate the reader on most important aspects of designing energy efficient and low EMF wireless devices laying foundation for future advancements. A multidisciplinary approach based on artificial intelligence (AI) and new multiplexing technologies like non‐orthogonal multiple access (NOMA) is adopted to devise efficient mechanisms and techniques realizing low EMF solutions through integration of antenna design, system modeling, and signal processing.
Both software and hardware solutions to minimize EMF exposure covering state‐of‐the‐art and advanced topics are discussed. EMF evaluation tools and numerical assessment methods for conventional as well as future wireless systems' enablers such as mmWave technologies are detailed as also is EMF reduction through radio resource allocation, energy conservation, EMF‐aware antenna design, backscatter communication, and MIMO. Moreover, a comprehensive account of validation studies as well as the modeling and selection of dielectric properties for all the age groups are utilized to provide sufficient background and highlight recent advancements. The book is concluded by highlighting potential future directions of research and implementation for energy‐efficient and low EMF user proximity wireless devices. The book covers a wide variety of subject categories and would, therefore, benefit a larger readership in the scientific community.
University of Glasgow
Masood Ur Rehman
Glasgow, UK
Muhammad Ali Jamshed
1Electromagnetic Field Exposure: Fundamentals and Key Practices
Muhammad Ali Jamshed1, Fabien Héliot2, Tim W.C. Brown2, and Masood Ur Rehman1
1James Watt School of Engineering, University of Glasgow, Glasgow, UK
2Institute of Communication Systems (ICS), Home of 5G and 6G Innovation Centre, University of Surrey, Guildford, UK
1.1 Introduction
In the past, significant research efforts have been devoted to first understanding how EM field (EMF) exposure affects humans [1–3] and, then, to create tools for measuring exposure and defining exposure metrics [4–6]; these measuring techniques and metrics can be used to establish exposure recommendations [7]. Indeed, the health impact of EMF, magnetic field (MF), and electrical field (EF) is currently being contested in studies and among the general public, particularly for children [8]. Wireless communication (e.g. the cellular system) has grown so rapidly in recent decades that it is now one of the most major sources of EMF exposure in the general environment (see Figure 1.1). Similarly to other sources of EMF exposure, measures and recommendations have been created in wireless communication throughout the last decades [10] to restrict exposure and, thereafter, enhance approaches to minimize it [11]. In the future generation of communication networks, the exponential increases in both multimedia traffic and connected devices will necessitate a rise in the number of access points (APs) (e.g. base stations) to meet demand. As a result of the rising number of wireless devices and APs, the level of EMF exposure will increase. Similarly, the widespread use of mmWave spectrum in 5G, which will have carrier frequencies over 24 GHz, is anticipated to have an effect on exposure since it would necessitate a high density of APs[12]. Recent research in [13–16] has revealed that exposure at these frequencies may pose some health risks.
Figure 1.1 Common EMF exposure sources generally present in the environment [9].
The chapter's structure and key topics of discussion are summarized as follows:
Section 1.2
covers the existing techniques for assessing
EMF
exposure in various circumstances, i.e. the
EMF
assessment framework, and includes information on the metrics most typically used for measuring
EMF
exposure in communication. First, research projects relating to the
EMF
exposure assessment frameworks are provided; the majority of these studies outline their
EMF
exposure evaluation mechanism, examine the reasons of exposure, and then recommend solutions to minimize it. Second, different categories of exposure metrics are reviewed, where each category of metrics is explained vis–á–vis its target scenario(s). Third, generic metrics are presented, which are developed by integrating measurements from several categories.
Section 1.3
explains and illustrates how the various available
EMF
metrics have been utilized for restricting (i.e. creating standards) or lowering exposure.
Finally,
Section 1.4
concludes the chapter.
1.2 EMF Metric and Evaluation Framework
A significant amount of work has been carried out in recent years for evaluating the EMF exposure in various scenarios, using different measurement systems and tools, to assess the potential risks emanating from EMF radiations in wireless communications and mitigate their effects (through guidelines and EMF‐aware reduction techniques). As a result, EMF monitoring has gained relevance in wireless networks over the last decade [17], given that ambient RF‐EMF exposure does not remain constant over time owing to environmental changes and variations in the number of active users (as well as the nature of their device usage). For example, the moniT (acronym for electromagnetic radiation exposure assessment in mobile communications) project, funded by Optimus, TMN, and Vodafone [18], provided public information on population exposure to EMF from mobile communication systems in Portugal from 2004 to 2012. This project's monitoring system was built on a network of autonomous remote probing stations and a comprehensive EMF sounding program, both of which were carried out in public spaces around the country. According to the project monitoring data, the EMF values of mobile systems were below the required threshold. Another EMF assessment and monitoring effort was the SEMONT project, which was implemented and utilized for real‐time EMF exposure evaluation. Monitoring findings indicated that possible exposure was well below the permissible level set by Serbian legislation for the general population [19]. Their approach was then utilized to quantify the exposure produced by GSM when fluctuations in traffic circumstances were considered [20]. According to RF exposure assessments, exposure levels tend to grow with rising urbanization [21]. Meanwhile, the exposure survey assessment in [22] discovered that exposure levels in Europe are not exceeding the recommended levels, but exposure from wireless communication devices has increased significantly over the last years, accounting for more than 60% of total exposure.
In addition to these monitoring initiatives, other projects, such as the monitoring and control activities relating to electromagnetic fields in the RF range (MONICEM) and low EMF exposure future network (LEXNET) projects, have established new EMF assessment metrics that may be used to reduce the overall level of EMF exposure. For example, in MONICEM, which was supported by both the inter‐University center for the study of interactions between electromagnetic fields and biosystems (ICEmB) and the institute for environmental protection and research (ISPRA), it was discovered that services such as cellular base stations, wireless networks, and so on create large amounts of EMF radiations, much over the natural limitations. The project created an environmental impact indicator (FIAE) based on the EMF derived from a generic source [23]. Similarly, in the LEXNET project, which was funded by the European Commission, a new realistic metric known as the exposure index (EI) [24] was developed to quantify the degree of EMF exposure to people in the environment. Using this criterion, the research established innovative strategies for lowering (by at least 50%) human exposure to electromagnetic (EM) radiation generated by wireless communication while maintaining quality of service (QoS) [25]. The metrics created in MONICEM or LEXNET are intended for assessing or realistically modeling EMF exposure across vast geographical regions while accounting for various forms of EMF radiations. These more general measurements or assessment frameworks sometimes rely on or combine existing measures created for more particular contexts. For example, consider the EI created by LEXNET, which includes in its definition the specific absorption rate (SAR) and power density (PD), both of which are typical metrics for measuring the EMF exposure of wireless communication devices and equipment. In the following sections, we will first go through the most often used metrics in wireless communications for analyzing EMF exposure in various circumstances, and then describe how some of them may be combined to generate more general metrics.
1.2.1 EMF Exposure Factors
As a byproduct of its transmission, each device delivering information to another device creates EMF exposure to users or persons in wireless communications. In general, the total exposure at any location in a given region under observation is the sum of the radiations emitted by all transmitting devices in the vicinity (accounting for both the active and passive exposures). The severity of the exposure is determined by four major criteria, which are discussed in the following.
1.2.1.1 Transmit Antenna Regions
Transmitting antennas typically have two radiating regions: near field and far field, with the near field region further classified as reactive and radiated near field dependent on the distance and frequency of the radiating antenna. The reactive near field lies in the immediate proximity of the antenna, where the electric and MF are out of phase, making the reactive effect more dominating. The radiating near field, also known as the Fresnel area, is the space between the reactive near and far fields. In this area, the radiating impact of the antenna begins to outweigh the reactive effect. The far field area, on the other hand, is further away from the antenna and has the electric and MF in phase. It should be noted that each zone is determined by specific boundary criteria, which are further specified in Figure 1.2. In Figure 1.2, denotes the diameter of the antenna, the radius of each zone, and the wavelength of an EM wave. The impact of the near field on EMF exposure is more significant in the uplink scenario, when the antenna(s) of a user mobile device radiate(s) to send data to an access point (AP) and most of the antenna(s) dissipated energy can easily be absorbed by the user body/head (given the user body/presence head's in the near field region) [27]. The influence of the radiated EMF, on the other hand, decreases with distance in the far field. It should be emphasized that active exposure normally results from near field EMF waves, whereas passive exposure typically results from far field radiations.
Figure 1.2 Antenna field areas are depicted [26].
1.2.1.2 Transmit Antenna Characteristics
The transmitting antenna's parameters, such as transmitted power, antenna gain, directivity, effective aperture, polarization, beam width, and so on, are critical in defining the extent of exposure. The intensity of exposure is generally proportional to the intensity of the EF, which is proportional to the transmit power. For example, in [28], the EMF radiations from mobile communication antennas were examined by taking into consideration the relevance of antenna characteristics for determining exposure.
1.2.1.3 Duration of Exposure
As with any other sort of exposure, such as pollution or cigarette smoke, the longer the exposure, the greater the exposure dosage. For example, [29] has demonstrated that the duration of exposition is associated to a rise in body temperature when humans are exposed to RF radiation, which can be hazardous over time. Similarly, [30] claims that growing mobile phone usage might have negative impacts on the human reproductive system.
1.2.1.4 Electrical Properties of Biological Tissues
Variations in the dielectric characteristics of organic materials and tissues can be regarded as a significant influence in EMF exposure. Indeed, as previously stated, children absorb more radiation than adults due to differences in the dielectric characteristics of their tissues. For example, [31], which explored the changes in dielectric constant between bones and fatty regions using microwave tomography, found a relatively large deviation in dielectric constant between soft and hard tissues. Meanwhile, [32] provides a thorough experimental examination linked to the variation in dielectric constant of different biological tissues for frequencies ranging from 10 Hz to 20 GHz.
1.2.2 EMF Exposure Metrics
Several metrics have been defined throughout the years in order to analyze and predict the EMF exposure of wireless communication systems in various circumstances, depending on the numerous parameters indicated in Section 1.2.1. To the best of our knowledge, there are four primary categories of EMF metrics, namely, SAR, PD, exposure‐ratio, and dosage, which may be grouped as shown in Figure 1.3.
Figure 1.3 The most often used metrics for assessing EMF exposure [9].
1.2.2.1 Specific Absorption Rate
The SAR is a measure of the generated EMF inside the human body when exposed to a transmitting antenna's near field. Watts per kilogram are the units of measurement of SAR. The SAR measure is widely used by regulatory organizations throughout the world to determine exposure standards and evaluate the exposure produced by various handset [35]. Indeed, to ensure public safety, each handset maker should give the electromagnetic energy deposition within surrounding biological tissues, as measured by the SAR [36, 37]. The SAR in the near field of an antenna mounted on a wireless device can be expressed as [38];
In (1.1), represents the conductivity of the exposed tissue(s), indicates the strength of the EF and is the mass density of the sample under test. Figure 1.4 depicts a typical setup for measuring the SAR of a human head, in which a radio frequency (RF) radiating device (with two antenna components in our example) is positioned close to a phantom head, and a probe (receiver) is used to measure the strength of [33]. To test SAR in the worst case scenario, the phantom head would be filled with a sugar solution that replicated the dielectric and conduction characteristics of brain tissue on average. The SAR may be further classified based on the EMF absorbed by different areas of the human body as whole body averaged SAR, organ‐specific SAR, and peak spatial average SAR [39, 40].
Figure 1.4 A typical SAR measuring setup is depicted.
Source: Jamshed et al. [9]/with permission of IEEE.
In confined contexts (i.e. rooms), the whole‐body averaged SAR or global SAR may be measured by measuring the reverberation time with and without humans within the room; the whole‐body SAR is then approximated based on the difference in reverberation time [41]. The organ‐specific SAR or local SAR is used to estimate the radiation absorption of a given organ inside the human body, and it is averaged spatially over the mass of a certain organ or tissue in the body [42]. Local SAR medicinal consequences are localized to a single bodily tissue averaged over 1 g or 10 g. In contrast to local SAR, global SAR considers the biological impacts on the entire body. In conjunction with the preceding SAR definitions, the peak‐spatial average SAR is used to determine the limits of SAR absorption for different areas of the human body, as well as to offer guidelines for safeguarding humans from RF near field exposure [40]. Meanwhile, for frequencies over 24 GHz, the energy contribution received by biological tissues is quite minimal in the reactive near field. Indeed, the average SAR becomes null for frequencies higher than 10 GHz due to the shallow penetration depth [43]; thus, the point‐wise power loss density (PLD) methodology is typically used to estimate the correct radiations absorbed by the human body and obtain accurate exposure measurement in the mmWave frequency band. The following equation illustrates the relationship between PLD and SAR[43];
where defines the mass density of the sample under test.
1.2.2.2 Power Density
In contrast to the SAR, which is beneficial for assessing EMF exposure in the near field of an antenna, the PD is the metric of choice for measuring EMF exposure in the far radiating field of an antenna, and is measured in Watts per square meter. In general, [26] gives the PD of an isotropic antenna in its far field, which is uniform (power per unit area) in all directions, and is as follows;
where is the transmitting power of the transmit antenna and is the distance at which the PD is measured. Whereas in the context of human body exposure, the PD of a transmitting antenna in its far field region can be defined as [44]
where (V/m) represents the root‐mean‐squared value of the EF strength incident on the tissue surface of a human body and (V/A) is the wave impedance. Furthermore, because EF and MF
