Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
Abstract
:1. Introduction
2. Research Methodology
2.1. EMF and 5G Exposure in the Scientific Literature
2.2. Research Questions
- What frameworks and methodologies are available for evaluating EMF exposure in 5G networks and how effective are they?
- Which gaps in the current literature hinder the development of efficient EMF-aware radio resource management strategies for 5G and beyond?
- What are the unique challenges and potential research directions for incorporating EMF-aware radio resource management into RIS-assisted 5G networks?
3. Related Work
- i.
- Comprehensive review of EMF exposure from 5G technologies:
- This paper provides a comprehensive and updated review of recent research on EMF exposure evaluation in 5G networks. It captures the latest trends, evaluation metrics, methodologies, and the impacts of emerging wireless technologies, offering valuable insights into 5G EMF exposure assessment and management.
- ii.
- EMF-aware radio resource management:
- This paper enhances our understanding of EMF-aware radio resource management for 5G and beyond by synthesizing recent strategies that incorporate EMF constraints into resource allocation. It examines techniques such as power control, beamforming, subcarrier allocation, and user association to balance network performance and EMF safety, with a focus on the dynamic role of RIS technology in reshaping EMF patterns to minimize exposure.
- iii.
- Identification of research challenges and opportunities:
- This paper highlights key emerging research challenges and opportunities in EMF-aware radio resource management for 5G networks and beyond, providing a forward-looking roadmap to guide future explorations and innovations in this evolving field.
4. EMF Exposure Level and 5G Technologies
4.1. EMF Exposure and 5G Radio Access Technologies
- The authors did not consider the effects of practical beamforming abnormalities, such as the presence of dual main beams, on the EMF evaluation level. The pencil beam considered was perfectly directed toward the estimated location of the UE (with perfect precoding weights).
- EMF exposure was evaluated using the PD metric only. However, based on [32], the PD metric is not effective enough to evaluate health risks, especially when the users are close to the measurement spots at high frequencies. The EMF evaluation procedure in [40] is applicable to an ideal beamformer. However, for a practical beamforming technique such as hybrid beamforming, this approach may not be accurate. EMF exposure due to hybrid beamforming has not been adequately investigated in the literature.
4.2. EMF Evaluation Methodology
4.2.1. EMF Exposure Evaluation Framework
- Geometric stochastic: This approach uses path loss modeling to estimate EMF exposure levels at various locations within the coverage area. It accounts for factors such as distance, terrain, and building materials to predict signal attenuation and EMF levels. Studies employing this approach include [72,73,74,75,76,77].
4.2.2. EMF Exposure Evaluation Metrics
- Power density (PD): PD quantifies the EMF exposure from downlink transmission and is expressed in watts per square meter (W/m2). The power received by an antenna at the receiving end is given by [90]
- Specific absorption rate (SAR): SAR measures the rate at which the human body absorbs electromagnetic energy from a nearby transmitting antenna. It is expressed in watts per kilogram (W/kg) and is a critical metric used by regulatory bodies to set exposure limits and compare the safety of mobile devices [91]. Mathematically, the SAR at a point P is defined as [92,93]
4.3. EMF-Aware Radio Resource Management
5. Challenges and Open Research Topics
5.1. Challenges
5.1.1. EMF Exposure from 5G Technologies
5.1.2. Resource Allocation in Dense 5G Networks
5.1.3. Resource Allocation in RIS-Assisted 5G Networks
5.2. Open Research Topics
5.2.1. Joint Optimization of Energy Efficiency and EMF Constraints
5.2.2. Hybrid Precoding for RIS-Assisted Networks
5.2.3. Comparative Analysis of Passive and Active RISs
5.2.4. EMF-Constrained Radio Network Planning
5.2.5. Joint Base Station and Active RIS Precoding Optimization
5.2.6. EMF-Aware Radio Access Network (RAN) Slicing
5.2.7. Resource Management for Integrated Terrestrial and Non-Terrestrial Networks
5.2.8. EMF-Aware IoT Applications
5.2.9. Adaptive EMF Exposure Control Using AI-Driven Approaches
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Paper | Year | Pre-5G | 5G | RA | RIS | Metrics | Evaluation Framework |
---|---|---|---|---|---|---|---|
[12] | 2015 | √ | √ | √ | |||
[13] | 2016 | √ | √ | ||||
[15] | 2018 | √ | √ | ||||
[2] | 2018 | √ | √ | √ | √ | ||
[16] | 2019 | √ | √ | √ | |||
[14] | 2020 | √ | √ | √ | |||
[18] | 2021 | √ | √ | ||||
[17] | 2021 | √ | |||||
[20] | 2021 | √ | |||||
[21] | 2021 | √ | √ | √ | |||
[11] | 2022 | √ | √ | √ | √ | √ | |
[19] | 2023 | √ | √ | √ | √ | √ | |
This work | 2025 | √ | √ | √ | √ | √ |
Category | Paper | Antenna Configuration | Frequency Band (GHz) | Network Architecture and Transmission | EMF Evaluation Metrics |
---|---|---|---|---|---|
Modeling and Simulation Approach | [23] | Antipodal linear tapered slot antenna (ALTSA) with 34.6° beamwidth and 16.5 dBi gain | 60 |
| Specific absorption rate (W/kg) |
[24] | Massive MIMO with codebook-based and reciprocity-based beamforming | <10 |
| Time-averaged realistic maximum power level | |
[29] | Fixed-beam 2 × 2, 4 × 1, and 8 × 1 patch arrays | 28 |
| Peak temperature in human tissue (°C) Power density (W/m2) | |
[48] | Multi-beam antenna with 10° horizontal beamwidth, 15° vertical beamwidth, and 18.6 dBi gain | 28 |
| Power density (dBW/m2) | |
[38] | 2 × 2 patch array antenna | 28 |
| Average electric field (V/m) Power density (W/m2) | |
[1,33,39] | SU-MIMO with up to 256 antennas for BS and up to 16 antennas for UE | 28 |
| Power density (W/m2) Specific absorption rate (W/kg) | |
[31] | dual-polarized element antenna array with fixed beams | 3.5 |
| Power density (W/m2) | |
[28,61] | Indoor uniform planar array antenna (64 and 1024 elements) with fixed beams | 3.7, 14 |
| Specific absorption rate (W/kg) | |
[34] | Beamforming arrays and MU-MIMO antennas (, 12 and 16 planar array) | 3.7 |
| Normalized average power pattern (NAPP) | |
[62] | 5G UE equipped with four quasi-Yagi antennas | 38 |
| Spatial-averaged power density (W/m2) | |
[40] | 64-element pencil beamforming with minimum 3° beamwidth | 3.7 |
| Power density (W/m2) | |
[63] | Massive MIMO antenna with 128 elements and multi-user beamforming technique | 28 |
| Power density (W/m2) Specific absorption rate (W/kg) | |
[64] | Massive MIMO antenna with static and dynamic beamforming | 3.5 |
| Power density (W/m2) | |
[65] | Massive MIMO with multi-beam antenna array configured into a 6-beam set | 3.4–3.7 |
| Electric feld strength (V/m) | |
[66] | Standard antenna with 10 dBi gain | 2.6 |
| Exposure index (EI) | |
[67] | 5G antenna with arbitrary beam pattern | 5.89 |
| Electric field strength (V/m) | |
[68] | 5G antenna with beam-steering technique | 3.7, 27 |
| Electric field strength (V/m) | |
Experimental Approach | [49] | Predefined (fixed) beams. | 3.5 |
| Electric field strength (V/m) |
[44] | MIMO testbed with 8 beamforming scenarios | 2.63 |
| Electric field strength (V/m) | |
[69] | Massive MIMO (64T64R) antenna with TDD multiplexing and SU-MIMO SDMA technique | 24–52 |
| Average electric field (V/m) | |
[45] | Single-user MIMO codebook-based beamforming with 8 CSI-RS ports | 3.3–3.8 |
| Time-averaged equivalent isotopically radiated power (dB) | |
[59,60] | 4T4R MIMO with 64 beams | 29.5 |
| Electric field strength (V/m) | |
[55] | Commercial 5G antenna deployment | 0.7, 3.5 |
| Power density (mW/m2) Electric field (V/m) | |
[53] | Commercial 5G FWA gNB with SU-MIMO spatial multiplexing | 3.45–3.5 |
| Electric field strength (V/m) | |
[58] | Massive MIMO with up to 96 transmit antennas using SU-MIMO and MU-MIMO | 2.63 |
| Electric field strength (V/m) | |
[50] | Commercial massive MIMO 5G BS (Ericsson AIR 5322) | 26–28 |
| Power density (mW/m2) | |
[51] | Commercial 8T8R MIMO BS antennas | 3.41 |
| Electric field strength (V/m) | |
[52] | Commercial cellular BSs in France | 0.7–3.5 |
| Electric field strength (V/m) | |
[56] | Commercial cellular BSs in Luxembourg | 0.8–3.5 |
| Electric field strength (V/m) | |
[57] | Commercial 5G massive MIMO BS (Ericsson AIR 6488) | 3.3–3.8 |
| Time-averaged equivalent isotopically radiated power (dB) |
Aspect | Geometric Stochastic | Deterministic | Experimental |
---|---|---|---|
Overview | Employs statistical methods to model the uncertainty in signal propagation [72]. | Utilizes ray-tracing to model the signal propagation with high accuracy [68,80]. | Employs measurement devices to capture the actual EMF exposure levels in real-world scenarios [55]. |
Applicability | Suitable for large-scale network planning and simulation [81,82]. | Suitable for small-scale network design and optimization [83]. | Suitable for assessing EMF exposure levels in real-world scenarios [84]. |
Advantages | Provides a cost-effective approach for EMF exposure level evaluation [81]. | Provides accurate and detailed analysis of EMF exposure levels [81,82]. | Provides direct measurements of EMF exposure levels [85]. |
Limitations | Limited accuracy in modeling complex environments [82]. | High computational complexity and resource requirements [81]. | High cost and logistical challenges [85]. |
State-of-the-art frameworks | 5G pencil framework [40]. | EMF evaluation based on the ray-tracing approach [63,80]. | In situ assessment of 5G NR [71] and 5G exposure assessment (5G-EA) [54]. |
Examples | Path loss models [73], such as close-in and APG models. | Ray-tracing software, such as Wireless InSite version 3.3.5 [86] and COMSOL Multiphysics version 6.3 [87]. | Measurement devices, such as Selective Radiation Meter (SRM-3000 Narda, Narda Safety Test Solutions GmbH, Pfullingen, Germany) [88] and EMF Meter (EMDEX Lite, Enertech Consultants, Inc., Campbell, CA, USA) [89]. |
Guidelines | Far-Field Limits (PD in W/m2) | Near-Field Limits (SAR in W/KG) |
---|---|---|
ICNIRP [10,94] | 10 | 2–6 |
FCC [95,96] | 10 | 1.6 |
Ref | Requirements | Purposes | Constraints | Algorithm | Pros (+) Limitation (-) | Architecture |
---|---|---|---|---|---|---|
[100] | Overall sum of transmit power minimization |
|
| Joint optimal resource scheduling solution based on convex optimization | +Optimal solutions are obtained. -The algorithm is applied to pre-5G network. | Single-cell downlink OFDMA |
[105] | Uplink electromagnetic exposure minimization |
|
| Convex optimization problem | +Low complexity. -Limited UEs in a single cell are considered. | Single uplink NOMA cell |
[106] | Uplink electromagnetic exposure minimization |
|
| Unsupervised machine learning (K-means integrated with the elbow method) | +Reduces EMF exposure compared to state-of-the-art techniques. -Limited UEs in a single cell are considered. | Single uplink NOMA cell |
[73] | Maximization of data rate |
|
| Enhanced EMF-aware BF scheme | +Low complexity. -Optimized solutions are not considered. | MU-MIMO RIS-aided 6G network |
[74] | Maximization of data rate |
|
| Dual-gradient descent EMF-aware BF scheme | +Low complexity. +Refines the findings in [73]. -Optimized solutions are not considered. | MU-MIMO RIS-aided 6G network |
[77] | Energy efficiency optimization |
|
| Maximization by alternating optimization | +Low complexity. -Simple network topology is considered. | MIMO with single RIS in LoS with a base station and a user |
[76] | EI optimization |
|
|
| +Low complexity. +Achieves 20% reduction in EI. -Simple network topology is considered. | MIMO with single RIS in LoS with a base station and users |
[108] | Enhanced spectral efficiency |
|
| Beamforming truncation | +Low complexity. -Simple network topology is considered. | mMIMO with single RIS |
[75] | Maximizes the received power |
|
| Angularly equalized virtual propagation channel | +Low complexity. -Simple network topology is considered. | mMIMO with single RIS |
[109] | Maximize coverage probability |
|
| Dimension search algorithm (bi-sectional or golden section) | +Low complexity. -Simple network topology is considered. | 5G cellular network |
[110] | Maximize achievable rate |
|
| Convex optimization problem | +Low complexity. -Simple network topology is considered. | mMIMO with mmWave (28 GHz) frequency. |
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Salem, M.A.; Lim, H.S.; Diong, K.S.; Alaghbari, K.A.; Zarakovitis, C.C.; Chien, S.F. Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey. Computers 2025, 14, 51. https://doi.org/10.3390/computers14020051
Salem MA, Lim HS, Diong KS, Alaghbari KA, Zarakovitis CC, Chien SF. Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey. Computers. 2025; 14(2):51. https://doi.org/10.3390/computers14020051
Chicago/Turabian StyleSalem, Mohammed Ahmed, Heng Siong Lim, Kah Seng Diong, Khaled A. Alaghbari, Charilaos C. Zarakovitis, and Su Fong Chien. 2025. "Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey" Computers 14, no. 2: 51. https://doi.org/10.3390/computers14020051
APA StyleSalem, M. A., Lim, H. S., Diong, K. S., Alaghbari, K. A., Zarakovitis, C. C., & Chien, S. F. (2025). Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey. Computers, 14(2), 51. https://doi.org/10.3390/computers14020051