Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey

Abstract

The expansion of 5G infrastructure and the deployment of large antenna arrays are set to substantially influence electromagnetic field (EMF) exposure levels within mobile networks. As a result, the accurate measurement of EMF exposure and the integration of EMF exposure constraints into radio resource management are expected to become increasingly important in future mobile communication systems. This paper provides a comprehensive review of EMF exposure evaluation frameworks for 5G networks, considering the impacts of high-energy beams, the millimeter wave spectrum, network densification and reconfigurable intelligent surfaces (RISs), while also examining EMF-aware radio resource management strategies for 5G networks and beyond, with RIS technology as an assistive factor. Furthermore, challenges and open research topics in the EMF evaluation framework and EMF-aware resource management for 5G mobile networks and beyond are highlighted. Despite the growing importance of RIS technology in enhancing mobile networks, a research gap remains in addressing specific EMF exposure considerations associated with RIS deployments. Additionally, the impact of EMF-aware radio resource allocation approaches on RIS-assisted 5G networks is still not fully understood.

1. Introduction

In order to support the demands for improved quality-of-service (QoS) and higher throughput specified in the standards for 5G and beyond, new technologies such as large-scale carrier aggregation, large-scale distributed antenna systems, denser networks and beamforming antenna arrays will be implemented in upgraded networks. However, due to the higher degree of electromagnetic field (EMF) radiation exposure anticipated as a result of these new technologies, there may be a health risk. For instance, high-gain directional beamforming antenna arrays are capable of forming narrow beams with a high concentration of electric and magnetic energy to aid signal transmission. More base stations and access points (BSs/APs) can be placed nearby to enhance connection quality as network densification further reduces cell size. Mobile users will be closer to one or more BSs/APs, increasing their exposure to EMF radiation. In [1], the author investigated human EMF exposure from 5G downlink communications in indoor and outdoor settings and compared its effects with those of current cellular technologies, taking into account the characteristics that were likely to be implemented in 5G. The simulation results revealed that EMF exposure exceeded the regulatory limits for a very short separation distance between the BSs and user equipment (UE), and the exposure level remained high throughout the network compared to present systems. Another study carried out in Naples, Italy, using the ray-tracing method [2] showed that the saturation of EMF levels had already occurred under current 2G/3G/4G technology and raised concern that the planning of a 5G network will be severely constrained by the limits on maximum EMF levels established in a wide set of regulations. Reportedly, there is growing public concern regarding the potential risk of increased radiation exposure associated with mobile networks [3,4]. This concern is further supported by the classification of radio-frequency electromagnetic fields (RF-EMFs) as possibly carcinogenic to humans (Group 2B) by the International Agency for Research on Cancer (IARC) in 2011. This classification was based on the observed increased risk of glioma, a malignant type of brain cancer, which has been associated with the use of wireless phones [5]. To address these concerns and ensure public safety, many countries around the world have established their own national standards for EMF exposure limits. These national standards often draw guidance from the guidelines set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). By adhering to these standards and guidelines, it is possible to mitigate the potential health risks associated with RF radiation exposure from mobile networks. However, it is important to continue conducting research and monitoring the effects of EMF exposure to ensure the ongoing safety and well-being of the public. The local regulatory body overseeing telecommunication companies is responsible for ensuring compliance. However, as the mobile network architecture and deployment scenario grow more complicated, monitoring and compliance auditing become increasingly challenging.

The current mobile network’s design and optimization consider only spectral efficiency (SE) and energy efficiency (EE) requirements. In the literature, radio resource management in advanced 5G network topologies, such as HetNets, C-RAN, F-RAN, cell-free networks, and ultra-dense networks, has been proposed based on these requirements [6,7,8,9]. Incorporating radioprotection principles into network design is viable but is still lacking in existing research. In order to better protect the public from any possibility of excessive radiation exposure, appropriate radiation protection features can be integrated into resource allocation algorithms for ultra-dense 5G networks. Some recent works have been inspired by this new requirement for 4G and pre-5G networks. However, due to substantial differences in architectures, technologies, and deployment scenarios for 5G networks and the new radiation exposure constraint in the optimization problem, existing methods may not be applicable anymore. In this paper, we presents studies that consider the EMF radiation exposure of some typical future network scenarios and resource allocation techniques used to minimize resources such as transmit power levels or maximize resources such as precoding/beamforming weights such that the exposure levels to radiation are below the safety standards while satisfying users’ minimum quality-of-service (QoS) requirements. The ICNIRP [10] has developed a guideline for EMF exposure limits. Based on this guideline, countries set their own national standards for EMF safety. To verify compliance with exposure standards enforced by local or national authorities, measurement or numerical simulation assessment according to ITU-T recommendations K.52 and K.61 is normally carried out by network operators. However, rapidly evolving radio-frequency technologies pose a challenge to the accurate prediction of EMF exposure based on the current guidelines. For example, the simple ray-tracing evaluation recommended by the guidelines does not take into consideration the radically different network architectures and radio technologies that will be adopting 5G standards. It is well recognized that 5G sites will intensively exploit massive multiple-input, multiple-output (MIMO) precise beamforming frequency bands in the millimeter wave (mmWave) region and new antenna radiation patterns. Rapidly evolving mobile technologies also present a big challenge for regulators in monitoring and verifying compliance to EMF safety standards for mobile services. In Section 4, we present a literature review of research related to EMF exposure due to beamforming and energy-efficient resource allocation and the impacts of integrating reflective intelligent surfaces (RISs) on EMF exposure in 5G mobile networks.

This survey follows a structured approach, starting with presenting existing related survey papers, and it highlights differences between our work and existing surveys. This is followed by a comprehensive review on EMF exposure evaluation frameworks for 5G networks, taking into account the impacts of high-energy beams and the millimeter wave spectrum. Next, this paper delves into the categories of EMF exposure used in the evaluation, and different methodologies, such as modeling, simulation, and experimental approaches, are discussed. Additionally, a review of studies that consider EMF-aware resource allocation for 5G networks is discussed, highlighting considerations for managing EMF exposure while optimizing resource allocation. Furthermore, an exploration of EMF exposure in the context of RIS-assisted 5G networks is presented for both uplink and downlink transmission protocols. Finally, challenges and open research topics in the field of EMF exposure management and resource allocation for 5G networks are reported. This paper is organized and structured as shown in Figure 1.

2. Research Methodology

To ensure a comprehensive review of the literature, a systematic approach was adopted for data collection. The Scopus and IEEE Xplore databases were utilized as the primary sources for identifying relevant studies. This review focused on publications from 2015 to 2025. The search strategy employed a combination of keywords, including “5G”, “EMF exposure”, “electromagnetic field radiation”, “specific absorption rate”, “power density”, “electric field strength”, “radio resource management”, “resource allocation”, “optimization”, “reconfigurable intelligent surface”, and “RIS”.

Boolean operators such as “AND” and “OR” were used to refine the search and retrieve relevant articles. The inclusion criteria encompassed peer-reviewed articles with a significant focus on EMF exposure and EMF-aware resource management, as well as contributions that offered empirical data or theoretical insights into the field. Studies that were unrelated to 5G networks, lacked focus on EMF considerations, or were non-peer-reviewed (e.g., editorials or commentaries) were excluded.

2.1. EMF and 5G Exposure in the Scientific Literature

An analysis of the Scopus database revealed a growing body of research related to 5G technology and EMF exposure since 2015, reflecting the increasing importance of this topic. Figure 2a shows that 54,835 publications include the keyword “5G”. However, when focusing specifically on 5G-related studies using keywords such as “EMF”, “exposure”, “electromagnetic field”, “radiation”, “specific absorption rate”, “power density”, or “electric field strength’’, the number of results decreased to 1030 publications (Figure 2b). This highlights a comparatively narrower focus on EMF exposure within 5G research, although this number is expected to grow significantly in the coming years given the relevance of this topic.

When further narrowing the scope to research involving “radio resource management”, “resource allocation”, or “optimization” combined with the previous EMF-related keywords, only 141 relevant publications (Figure 2c) were identified. Moreover, introducing “reconfigurable intelligent surfaces” or “RIS” into this search criteria reduced the number to a mere 8 papers (Figure 2d). This stark contrast underscores a significant gap in the literature regarding EMF-aware radio resource management strategies for RIS-assisted 5G networks, highlighting both the novelty and the potential of this research domain.

2.2. Research Questions

The objective of this review is to examine how EMF exposure constraints can be effectively integrated into radio resource management strategies for 5G networks and beyond. As technologies such as massive MIMO, beamforming, RISs, and the mmWave spectrum become widespread, the need for EMF-aware resource allocation will have growing significantly. This survey provides a comprehensive analysis of existing methods, identifies research gaps, and highlights future opportunities to guide further development in this field. Specifically, the review addresses the following key 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?

By addressing these questions, this review aims to offer insights that advance the design of safe and efficient mobile networks.

3. Related Work

To distinguish our contributions from prior work, we provide a comparative analysis of existing surveys in

Table 1. Comparison of existing survey papers on EMF exposure and related topics.

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		√	√	√	√	√

√ indicates that the specific criterion is met for the corresponding study.

, evaluating them across six dimensions. The first dimension, EMF exposure in pre-5G networks, was addressed briefly in [2,11], with a more detailed discussion in [12], which focused on evaluating EMF levels for legacy systems. The second dimension, EMF exposure in 5G networks, was introduced in [13], while [11,14,15,16] explored in greater depth the effects of technologies such as MIMO with beamforming on EMF exposure. The third dimension examines resource allocation (RA) techniques incorporating EMF constraints, which aim to optimize parameters like transmit power or precoding weights to maintain exposure below safety thresholds. This was thoroughly reviewed in [11], while [13,17] offer more limited discussion. The fourth dimension, the use of RISs to manage EMF exposure, has been explored only briefly in the literature. While [17,18] touch upon its potential to influence EMF levels, comprehensive studies on RIS-assisted EMF-aware network optimization remain sparse. The fifth dimension, focusing on exposure metrics, examines how different measures, such as near-field and far-field radiation, are used to evaluate compliance with safety standards. Finally, the sixth dimension involves EMF evaluation frameworks, where [19] provides a broad exploration of network planning strategies, integrating multi-objective optimization and multi-criteria decision-making techniques to enhance antenna placement and manage growing network demand. The key contributions of this paper are summarized as follows:

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

In this section, we will explore state-of-the-art research on EMF exposure in relation to the impacts of 5G technologies. Several studies have investigated the potential effects of electromagnetic radiation emitted by 5G networks. Additionally, there is a growing interest in developing EMF-aware resource allocation techniques for ultra-dense 5G networks and RIS-assisted 5G networks. These strategies strive to enhance network performance while mitigating the potential health hazards linked to EMF exposure. In summary, this section provides an overview of the latest research on EMF exposure in 5G networks, including both potential risks and emerging solutions.

4.1. EMF Exposure and 5G Radio Access Technologies

The 5th generation (5G) mobile network is key to meeting the needs resulting from the constantly growing demands of mobile users in terms of high quality-of-service (QoS) with high data rates and highly reliable massive communication between devices with low latency [22]. However, several previous studies have highlighted the issues of RF-EMF exposure in 5G networks. Concerns regarding EMF exposure in 5G mobile networks are mainly due to the three major technical technologies adopted for 5G networks. Firstly, high operating frequencies, such as the mmWave frequency, may lead to an increase in radiation exposure for users [23]. Secondly, the employment of small cells with a massive number of transmitters (e.g., gNodeB BSs) to cover smaller geographical areas leads to the gNodeBs being deployed closer to users, thus increasing EMF exposure [13]. Lastly, narrow beams are employed in 5G networks utilizing a massive number of antenna arrays. Such a multiple-antenna system can generate a very large antenna gain. This higher concentration of electromagnetic energy increases the potential for the EMF to penetrate into the human body more deeply [24,25,26]. However, not many studies in the literature have provided conclusive evidence that EMFs generated by 5G networks pose a significant threat to human health. Figure 3 summarizes concerns about EMF exposure in 5G networks. Moreover, research is still in progress to find any gaps in knowledge regarding the effects of EMFs on human health.

Several previous studies have even stressed that major concerns regarding EMF exposure are not based on scientific evidence or experiments. In [16], the authors investigated major health risk allegations against 5G technology. The authors stressed that there was no scientific evidence supporting the allegations that EMF exposure due to 5G mobile networks was hazardous. The authors investigated the impacts of mmWave radiation from a 5G BS and concluded that the power levels were below the levels required to produce any heat effect. However, the effect of other 5G technologies, such as ultra-dense mobile networks and beamforming, was not discussed. Moreover, the authors did not consider the impacts of EMFs generated by devices close to the users, such as 5G smartphones. The authors in [27] analyzed the risks associated with EMF exposure in 5G networks and investigated the latest metrics, regulations, and compliance assessment procedures for evaluating 5G EMF exposure. The authors noted that beamforming technology enables power from each antenna element to be directly concentrated towards specific users in need of service. As a result, the total power radiated by a 5G MIMO base station is not spread across the entire coverage area but rather focused on specific regions based on network and traffic conditions. Consequently, despite the potential increase in maximum radiated power, the expected EMF exposure from 5G is anticipated to be similar to, and in most cases lower than, that of previous technologies. The authors discussed the health risks of 5G technologies in a brief overview. However, approaches to mitigate and reduce EMF exposure due to new 5G technologies were not fully investigated. In [2], the authors presented the current state of the art in EMF-aware mobile networking and studied current exposure limits and EMF constraints’ impact on 5G network planning. Based on their findings, the authors indicated that installing optimal 5G networks in urban zones will be challenging due to the EMF saturation effect and strict regulation limits. The authors provided a brief discussion on the impacts of 5G technologies (e.g., beamforming) on EMF radiation exposure. Moreover, the authors presented general guidelines for the planning of 5G EMF-aware networks, which consist of the following steps: modeling 5G radio access technologies, selecting techniques for evaluating the EMF level, and validating the EMF exposure based on regulatory limits (e.g., the ICNIRP) in a realistic scenario. Finally, the authors summarized that current 5G challenges might cause inaccurate results for EMF evaluation due to several reasons. Firstly, at the time that the study was carried out, 5G technologies were not fully deployed and standardized, and revisions of regulations about EMF limits were still in progress. There were economic barriers to EMF compliance assessment; in other words, EMF measurement components were still very expensive. Co-existence with current pre-5G networks and the lack of urban planning considering existing cellular networks might affect the quality of the EMF exposure evaluation results. According to the authors, the implications of EMF exposure due to 5G technologies such as beamforming were not clearly understood yet.

In [28], the authors highlighted concerns over utilizing the mmWave spectrum coupled with MIMO antennas and beamforming techniques in 5G networks regarding EMF exposure. The authors simulated the exposure levels caused by an 8 × 8 planar array antenna at 3.7 GHz. The simulation was carried out on a human model in an indoor environment using the Sim4Life simulation platform. The specific absorption rate (SAR) was used only for the exposure assessment. Based on the findings, the authors concluded that the SAR values for the simulated approach were below the maximum SAR limits (2 W/kg). The authors in [29] investigated the temperature elevation of a human head model exposed to 28 GHz fixed-beam 2 × 2, 4 × 1, and 8 × 1 patch arrays. Pennes’ bioheat transfer equation [30] was used to calculate the temperature of the human head model. Based on the results, the peak temperature due to the 2 × 2 array was higher compared to those of the 4 × 1 and 8 × 1 patch arrays. The results show that the correlation between the average power density (PD) and the peak temperature is strong over an area of a few cm².

Measuring the exact radiation exposure due to 5G networks is a challenging task that has caught the attention of several researchers. The authors in [22] stressed that the EMF measurements used in 2G, 3G, and 4G networks were not accurate enough to estimate EMF exposure due to radiation from 5G networks. The main reason for this inaccuracy was the utilization of massive MIMO with precise beamforming technologies and high-frequency bands (such as mmWave), leading to a complex radiation pattern from 5G antennae. Thus, overestimation may take place when using existing measurement methods. The authors reported that the most appropriate evaluation method for estimating EMF exposure was based on a statistical modeling approach. However, the authors provided no recommendations or guidelines for the suggested approach. In [31], the authors utilized their knowledge of a traffic beam pattern envelope to evaluate EMF exposure and to estimate the compliance boundary of an MIMO transmission by a 5G BS. The authors claimed that the far-field spherical formula (PD) could be used to estimate the EMF compliance boundaries accurately. However, this claim contradicts the finding of [32,33], who claimed that PD estimation was not capable of representing the level of energy that was actually absorbed by the human body. Moreover, in [34], the authors stated that estimating the compliance boundary based on a comparison between the maximum acceptable PD set by [10] and the calculated PD (using a deterministic analysis approach) was not accurate due to the movement of the users and the connection time between the BS and the users. The authors proposed a statistical approach to overcome this issue, which depends on the normalized average power pattern (NAPP) of a planner array antenna. An 8 × 8 MIMO antenna, which covered 120° on the azimuth plane and 30° on the vertical plane, was considered to study the compliance boundary for safety in an urban area associated with a 5G network. The authors investigated the effects of several parameters on the calculation of the NAPP. Based on the results and findings, the NAPP was majorly affected by the number of radiating antenna elements, the size of the scanning region, and the angular distribution around the element array.

Beamforming technology is vital for meeting 5G networks’ demands and requirements for a high data rate, improved fairness, and high reliability [35,36]. Generally, beamforming is a technique that steers the signals generated from an array of antenna elements toward the direction of the intended UE [37]. Specifically, by utilizing beamforming technique, the signal is sent over each transmit antenna with a weighted scaling factor. Next, previous studies that evaluated EMF exposure due to the utilization of beamforming techniques in 5G networks are reviewed. In the literature, only a few studies have focused on EMF exposure due to beamforming in 5G networks. In [27], the authors analyzed the main health risks frequently associated with specific 5G features, e.g., MIMO, beamforming, cell densification, the adoption of mmWave, and the connection of millions of devices. They suggested that the beamforming feature was key to mitigating and reducing the total power radiated by a 5G BS by concentrating the radiated power into a narrow beam directed toward the intended user. The authors in [2,38] showed that EMF exposure from antenna arrays utilizing a beamforming technique complied with the limits defined by international regulations [31], highlighting that a simple model could be employed to estimate the exclusion zones from gNBs operating with MIMO and beamforming techniques.

In [33,39], the authors evaluated EMF exposure from a 28 GHz 5G gNB utilizing an 8 × 8 planar array antenna with fixed beamforming in urban macro- and micro-cell environments. The proposed model assumes wide and fixed beams, ignoring the impact of pencil beams that are positioned toward individual users. According to the authors, the SAR measurement metric was more effective than PD in evaluating the impacts of mobile signals on human health, as the SAR reflected the level of energy that was actually absorbed by the human body. Hence, the authors used both the PD and SAR metrics to evaluate the impacts of the fixed beamforming technique with a wide beam width on the EMF exposure level. For PD evaluation, an approach similar to the one proposed in [40] was used with a fixed beam width of 65 degrees. The results showed that the EMF exposure exceeded the limits set by the FCC guidelines for the maximum PD at a compliance distance below 6 m and the maximum SAR at a compliance distance below 2 m. Thus, the authors suggested that the minimum distance for human safety should be maintained at least 6 m in cellular communications at high frequencies such as 28 GHz. The authors of [40] proposed a pencil beamforming technique by minimizing the beamwidth of a transmitted beam. The authors considered seven cells, where each cell was divided into three sectors, and there were 64 radiating elements per sector on the antenna. The impacts of this technique were evaluated in terms of EMF exposure and throughput. EMF exposure was evaluated based on the point-source model proposed in [41] by computing the PD at a measurement spot. This pencil beamforming approach was compared to two other reference solutions, namely beamforming with a fixed beam width [42] and without beamforming [41]. The proposed pencil beamforming showed a reduced EMF exposure level in comparison with the reference solutions. The authors concluded that the EMF exposure tended to be further reduced as the localization uncertainty (difference in distance between the actual and the estimated UE locations) decreased due to the narrower beam widths being synthesized. Although the technique proposed in [40] resulted in an EMF strength that was lower than the limits set by the ICNIRP [10], two research gaps were observed. These gaps are summarized as follows:

• 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.

For 4G and pre-5G radio systems, mobile network operators have begun pushing for denser networks and heterogeneous deployments. It can be observed that more and more BSs have been installed in surrounding areas on walls, rooftops of buildings, and low antenna masts to support increased service requirements. These network deployment trends increase the possibility of the general public being in close proximity to radio BS antennas. As a result, the level of EMF exposure experienced by users will be higher. A measurement study carried out by the authors of [12] for 2G/3G systems found that EMF levels exceeded the limits in 15.6% of the locations considered. In [43], the authors measured the EMFs generated by different 2G/3G/4G BSs located in Poland and found a case where the total EMF generated by multiple operators exceeded the maximum limit. Experimental assessments of EMF exposure in real-world environments due to 5G networks are still lacking in the literature compared to EMF exposure assessments that are based on statistical and simulation approaches. Next, previous studies that have evaluated EMF exposure based on experimental approaches in 5G networks were reviewed. In [44], the authors tested a 16 × 8 MIMO testbed in eight beamforming scenarios for indoor downlink communication with multiple users at various locations. The authors acquired a heatmap of the received power for each beamforming scenario utilizing an isotropic triaxial field probe. Finally, the heatmap was converted into an RF-EMF heat map. The authors conducted this experiment to study the impact of the number of users, the position of users, and environmental fluctuation on RF-EMF exposure from a 5G BS. Based on the results, the EMF exposure varied between 1.37 V/m and 3.09 V/m at the position of the MIMO testbed. This result exceeded the maximum limits set by the ICNIRP. However, the exposure level decreased with the increment in the distance between the MIMO testbed and the location of the user. Thus, the authors concluded that these findings might lead to more concerns regarding public safety and the deployment of future wireless mobile networks.

The authors in [45] conducted a monitoring experiment for 25 massive MIMO 5G BSs over a day (24 h). The purpose of this experiment was to collect large amounts of data on EMF exposure levels in terms of the antenna radiation patterns and the spatial distribution of the BSs’ transmit power for each directed beam. In this experiment, the authors considered massive MIMO BSs located in urban areas in Australia, with the operating frequency being within 3300–3800 MHz. Based on these findings, the authors observed that the BSs utilized only 25% of the maximum average power. Moreover, the average radiation patterns were below the peak envelope gain by 9 dB, and the transmitted power was distributed evenly over the antennas’ scan range. Furthermore, the authors compared the calculated (theoretical) equivalent isotropic radiated power (EIRP) assuming constant transmit power for each beam with the reported actual $EIR{P}_{act}.$ The comparison showed that the maximum $EIR{P}_{act}$ was lower than the maximum theoretical EIRP by at least 8.8 dB. The authors concluded that the findings were consistent with the assessments made by the International Electrotechnical Commission (IEC) and provided a helpful evaluation of the EMF exposure of 5G BSs. Based on recent IEC standards, finding the maximum EMF exposure level and the compliance distance requires moving a measurement probe slowly within the area of the measurements [46]. However, this procedure is unclear regarding the speed of the probe movement and the appropriate measurement spacing for compliance distance assessments, as highlighted by the authors of [47]. Thus, the authors conducted a simulation study using experimental measurements to estimate the appropriate spacing for the EMF exposure assessment of an outdoor 3.5 GHz 5G BS in South Korea. Three installation scenarios were considered. In the first scenario, only a few reflecting structures were assumed to be present; thus, the attenuation and wave propagation were mainly due to ground reflection. This scenario was considered to ensure the EMF had a significant effect. In the second and third scenarios, reflecting structures were considered to study the variation in the direct incident field emitted from the BS. The height of the BS in the third scenario was higher than the height of the BS in the second scenario. The EMF exposure level was evaluated through the PD parameter estimated using the ray-tracing software developed in [48] based on the ray frustum algorithm. The findings were also compared with other results obtained from ray-tracing software (e.g., Wireless InSite version 3.3.5). Based on the results, the authors observed that the emitted EMF from the 5G BS was strongly dependent on the installation height of the BS relative to the height of the reflecting structure. Moreover, the results obtained from simulations and experiments indicated that a measurement spacing of 1 m was appropriate to determine the maximum EMF exposure of a 5G BS for the three studied scenarios. The authors concluded that the PD measured at the proposed 1 m compliance spacing did not exceed the exposure limits of 10 $\mathrm{d}\mathrm{B}\mathrm{W}/{\mathrm{m}}^2$ set by the ICNIRP [10]. The authors also noted that employing the beamforming feature in 5G communications made it more challenging to conduct EMF exposure measurements for a 5G BS compliance assessment.

According to [49], possible EMF issues in the 5G new radio (NR) included the use of new frequency bands and breakthroughs in technology, such as massive MIMO and beamforming. Thus, the authors proposed a comprehensive and ready-to-use exposure assessment methodology to measure or calculate in situ the time-averaged instantaneous exposure and theoretical maximum exposure from 5G NR BSs using a common spectrum analyzer. The proposed measurements consisted of six steps. Firstly, the frequency range at the measurement location and the actual position of the synchronization signal (SS) burst were determined. Next, the electric field strength per resource element (RE) of the dominant synchronization signal block (SSB) was measured. Then, the EMF exposure was evaluated based on the instantaneous electric field strength. The authors then extrapolated the electric field strength per RE of the dominant SSB beam ($E_{RE,SSB}$) compared to the theoretical maximum exposure level ($E_{\mathrm{m}\mathrm{a}\mathrm{x}})$ and compared the exposure levels with the relevant exposure limits introduced by the ICNIRP [10]. Finally, the proposed methodology was validated by an on-site experiment carried out for a 5G NR BS operating at 3.5 GHz in Düsseldorf, Germany. Based on the findings, the worst-case downlink exposure level was about 130 to 170 times higher when the 5G NR channel was fully occupied with downlink resources compared to the scenarios with a single UE. However, in all testing scenarios, the electric field strength values were less than the reference limits (61 V/m) specified by the ICNIRP/IEEE [10]. It was concluded that EMF exposure tests for 5G NR signals at higher frequencies, such as mmWave signals, could not be performed using the proposed method. However, the experimental procedure carried out for a 3.5 GHz BS remains valid for the mmWave band. In [50], the authors compare different in situ methodologies for assessing RF EMF exposure from 5G base stations operating in the FR2 frequency range. The study evaluates the accuracy, precision, and feasibility of various measurement techniques, highlighting the challenges associated with RF EMF exposure assessment in complex urban environments. Similarly, [51] investigates the relationship between extrapolated field strengths, distance, measurement time, and induced traffic from 5G C-band base stations, emphasizing the need for accurate extrapolation methods in densely populated areas. The study in [52] provides a comprehensive analysis of RF EMF exposure in diverse micro-environments across rural and urban regions in France, noting significant variations due to environmental factors and base station configurations. In [53], measurements of EMF exposure and throughput performance for a 3.5 GHz standalone 5G fixed wireless access (FWA) deployment in a baseball stadium reveal high throughput with controlled EMF exposure levels. The findings in [54] underscore the dominant role of smartphones in 5G-related RF exposure, attributing increased exposure to user behavior and device characteristics, and call for targeted mitigation strategies. The work in [55] compares EMF exposure and network throughput before and after 5G activation in a residential area, showing significant throughput gains with exposure remaining within safe limits. The authors of [56] present measurements of EMF exposure from mobile network antennas in Luxembourg. The paper details the methodologies used to assess exposure levels in various locations around the country, concluding that EMF exposure from mobile antennas is generally within recommended limits, though further monitoring is needed for high-density areas. Similarly, [57] evaluates the effective isotropic radiated power (EIRP) of 5G base stations during large stadium events, demonstrating that careful planning is crucial for ensuring EMF safety. The authors of [58] investigate the use of physics-informed machine learning models to predict RF-EMF exposure in massive MIMO systems. By combining physical models with machine learning algorithms, they improve exposure predictions and show that this approach can accurately model complex scenarios involving massive MIMO technology and RF-EMF exposure. The study in [59] reports real-world measurements of RF EMF exposure from 5G base stations operating in the mmWave band with MIMO antennas. It highlights the importance of considering antenna configurations and environmental factors when assessing exposure at mmWave frequencies.

Recently, an advanced approach leveraging machine learning (ML) techniques has emerged for predicting EMF exposure. This novel methodology applies ML algorithms to forecast and estimate EMF exposure levels experienced by individuals or devices. By leveraging the capabilities of ML, researchers aim to enhance the accuracy and efficiency of EMF exposure prediction. This, in turn, facilitates more informed decision-making and improved management of concerns linked to EMFs. In a recent study by [60], the authors put forth two distinct models for EMF prediction. The first model forecasts RF-EMFs from signal beams, while the second model predicts RF-EMFs originating from base stations. Each of these models incorporates three ML techniques for forecasting RF-EMF values: Approximate Radial Basis Function Neural Network (Approximate-RBFNN), Exact Radial Basis Function Neural Network (Exact-RBFNN), and Generalized Regression Neural Network (GRNN). To validate their models, the authors carried out a measurement campaign on a single sector of a 5G base station operating at 29.5 GHz in Cyberjaya, Malaysia. By comparing the predicted outcomes with actual measured data, they concluded that the Exact-RBFNN algorithm exhibited superior performance, demonstrating the closest alignment with the measured values.

Table 2. Summary of EMF exposure studies for 5G technologies.

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	Near-field region Uplink	Specific absorption rate (W/kg)
	[24]	Massive MIMO with codebook-based and reciprocity-based beamforming	<10	BS utilization, time-division duplex, scheduling time, and spatial distribution of users within a cell Downlink	Time-averaged realistic maximum power level
	[29]	Fixed-beam 2 × 2, 4 × 1, and 8 × 1 patch arrays	28	Near-field region Uplink	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	Small cell with antenna height up to 15.7 m in urban region near Isu Station in Seoul Downlink	Power density (dBW/m2)
	[38]	2 × 2 patch array antenna	28	Near-field region Downlink	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	UMi with up to 200 m inter-site distance, UMa with up to 500 m inter-site distance, BS height 25 m Downlink and uplink	Power density (W/m2) Specific absorption rate (W/kg)
	[31]	$8\times 8$ dual-polarized element antenna array with fixed beams	3.5	Far-field region of a 5G BS Downlink	Power density (W/m2)
	[28,61]	Indoor uniform planar array antenna (64 and 1024 elements) with fixed beams	3.7, 14	Near-field region of an indoor 5G AP with 100 mW transmit power Downlink	Specific absorption rate (W/kg)
	[34]	Beamforming arrays and MU-MIMO antennas ($8\times 8$, 12 $\times 12,$ and 16 $\times 16$ planar array)	3.7	Theoretical hexagonal cells of 333 m diameter and BS height of 30 m Downlink	Normalized average power pattern (NAPP)
	[62]	5G UE equipped with four quasi-Yagi antennas	38	Near-field and intermediate field regions (up to 25 mm) of UE antenna Uplink	Spatial-averaged power density (W/m2)
	[40]	64-element pencil beamforming with minimum 3° beamwidth	3.7	Theoretical hexagonal cells of side L = 100 m and BS height of 15 m Downlink	Power density (W/m2)
	[63]	Massive MIMO antenna with 128 elements and multi-user beamforming technique	28	Small cell with 10 m antenna height in urban city of Rosslyn, Virginia Downlink and uplink	Power density (W/m2) Specific absorption rate (W/kg)
	[64]	Massive MIMO antenna with static and dynamic beamforming	3.5	5G SA and NSA deployments considering interference and user density Downlink	Power density (W/m2)
	[65]	Massive MIMO with multi-beam antenna array configured into a 6-beam set	3.4–3.7	BS with maximum DL capacity and transmission power of 80 W Downlink	Electric feld strength (V/m)
	[66]	Standard antenna with 10 dBi gain	2.6	Large-scale cellular networks with multiple BSs and active and passive UEs (1 BS/km2) Downlink and uplink	Exposure index (EI)
	[67]	5G antenna with arbitrary beam pattern	5.89	BS with 120 W transmit power within 1 km2 rectangular area in Lille city center, France Downlink and uplink	Electric field strength (V/m)
	[68]	5G antenna with beam-steering technique	3.7, 27	Macro-cellular and small-cell scenarios Downlink	Electric field strength (V/m)
Experimental Approach	[49]	Predefined (fixed) beams.	3.5	BS antenna height 12 m above the floor level of a parking building in Düsseldorf, Germany Downlink and uplink	Electric field strength (V/m)
	[44]	$16\times 8$ MIMO testbed with 8 beamforming scenarios	2.63	Real-world indoor environment (15 m long, 7.5 m wide, and 3 m high) Downlink	Electric field strength (V/m)
	[69]	Massive MIMO (64T64R) antenna with TDD multiplexing and SU-MIMO SDMA technique	24–52	BS antenna height 20 m above the ground Downlink	Average electric field (V/m)
	[45]	Single-user MIMO codebook-based beamforming with 8 CSI-RS ports	3.3–3.8	25 5G BSs of the Telstra commercial network in dense urban areas of Australia Downlink	Time-averaged equivalent isotopically radiated power (dB)
	[59,60]	4T4R MIMO with 64 beams	29.5	BS antenna height 10 m above ground on the upper level of a Rekascape building in Cyberjaya, Malaysia Downlink	Electric field strength (V/m)
	[55]	Commercial 5G antenna deployment	0.7, 3.5	5G tower located in Sacrofano, Rome city, Italy Downlink	Power density (mW/m2) Electric field (V/m)
	[53]	Commercial 5G FWA gNB with SU-MIMO spatial multiplexing	3.45–3.5	5G SA fixed wireless access deployment in a baseball stadium Downlink	Electric field strength (V/m)
	[58]	Massive MIMO with up to 96 transmit antennas using SU-MIMO and MU-MIMO	2.63	Indoor and outdoor environments at University of Surrey Downlink	Electric field strength (V/m)
	[50]	Commercial massive MIMO 5G BS (Ericsson AIR 5322)	26–28	Indoor LoS and NLoS environment at distances between 9.94 and 14.32 m from BS. Downlink.	Power density (mW/m2)
	[51]	Commercial 8T8R MIMO BS antennas	3.41	Single 5G BS and distance of the measurement points between 56 m and 170 m from BS Downlink	Electric field strength (V/m)
	[52]	Commercial cellular BSs in France	0.7–3.5	Set of 70 micro-environments (e.g., residential area, downtown, business area, train station, and public transport ride) in urban and rural areas of France Downlink	Electric field strength (V/m)
	[56]	Commercial cellular BSs in Luxembourg	0.8–3.5	Three urban sites in Luxembourg using fixed and mobile measuring devices Downlink	Electric field strength (V/m)
	[57]	Commercial 5G massive MIMO BS (Ericsson AIR 6488)	3.3–3.8	Deployment of nine 5G BSs in a stadium with maximum output power between 56 and 80 W Downlink	Time-averaged equivalent isotopically radiated power (dB)

summarizes key studies in this area, highlighting the categories of modeling, simulation, and experimental approaches.

4.2. EMF Evaluation Methodology

The evaluation methodology for EMF exposure is defined in international standards, such as IEC 62232 [46] and ITU K.91 [70]. These standards provide guidelines for assessing the EMF levels emitted by individual base stations (BSs) and BS sites through either calculations or measurements. The main objective of these evaluation guidelines is to determine the compliance boundaries of cellular BSs and devices in terms of EMF exposure. Typically, simplified models such as the free-space formula and time-averaged antenna patterns are used to calculate exclusion zones, which may not accurately represent the spatial distribution of EMF levels across the entire site area. Alternatively, field measurements can be conducted using operational 5G sites to evaluate EMF exposure [45,71]. However, this approach presents challenges such as site and equipment availability, complex parameters of BSs and devices, and dynamic traffic and multi-user conditions [69]. Furthermore, conducting fine-grained analysis for the entire site area through measurements is not feasible, leading to significant variability in EMF levels between different locations in urban scenarios. In such cases, deterministic techniques like ray-tracing offer a promising approach for effectively evaluating EMF exposure. EMF evaluation metrics depend on two main measurement approaches, i.e., downlink and uplink measurements. Figure 4 presents types and examples of EMF exposure evaluation categories and common metrics.

4.2.1. EMF Exposure Evaluation Framework

In this section, we present state-of-the-art frameworks for evaluating EMF exposure across different categories. The aim is to provide researchers and practitioners with an overview of current methods and best practices for assessing EMF exposure in various environments. We discuss the applicability, advantages, limitations, and examples associated with each category, as well as relevant evaluation metrics. As shown in Figure 4, EMF exposure evaluation can be broadly classified into three main categories:

• 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].
• Deterministic: The deterministic approach uses ray-tracing techniques to model the propagation of radio waves in a given environment. It provides a detailed assessment of EMF exposure, considering reflections, diffraction, and scattering. This method has been applied in [63,78].
• Experimental: The experimental approach involves direct measurement of EMF exposure using specialized instruments and devices. While highly accurate, it can be time-consuming and costly. Relevant works employing this method include [49,50,51,52,53,54,56,57,58,59,71,79].

Each category offers unique advantages and limitations.

Table 3. Comparative analysis of EMF exposure evaluation approaches.

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].

provides a comparative summary of these approaches, highlighting key distinctions.

Despite advancements in EMF exposure evaluation, there remains a research gap concerning frameworks tailored for RIS deployments. Although RIS technology plays a crucial role in enhancing wireless communication systems, studies specifically addressing its impact on EMF exposure are limited. In [80], a new RIS-assisted 5G mmWave network planning framework is proposed. This framework uses machine learning (ML) classification for multi-RIS clustering to optimize RIS placement and improve network efficiency while minimizing the number of deployed RISs. The study provides a comparative analysis of passive and active RIS impact on EMF exposure in densely populated urban areas, revealing that the exposure from multiple active RISs is approximately 7.5 times higher than that from passive RISs. While the exploration of RIS technology in EMF-aware radio resource management has begun, it remains underdeveloped. Section 4.3 reviews existing research addressing EMF-aware radio resource management with specific consideration of RIS technology.

4.2.2. EMF Exposure Evaluation Metrics

In mobile communication systems, EMF exposure is evaluated differently for downlink and uplink transmission. Downlink measurements refer to far-field radiation exposure, where the EMF radiation from the base station’s (BS) transmitting antenna is measured at a distance far from the source. In contrast, uplink measurements involve near-field radiation exposure, occurring when a user’s mobile device transmits data to a BS. In this case, the radiated energy is absorbed primarily by the user’s body or head due to the proximity of the device.

The primary metrics used for EMF exposure evaluation are power density (PD) for downlink transmissions and specific absorption rate (SAR) for uplink transmissions.

• 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]

  $${\mathrm{P}}_{\mathrm{R}}={\mathrm{P}}_{\mathrm{D}}{\mathrm{A}}_{\mathrm{e}},$$

  (1)

  where ${\mathrm{A}}_{\mathrm{e}}$ is the effective area (or aperture) of the receiving antenna and $P_D$ is the power density (PD) of the radiated signal. For an isotropic antenna, the effective area is calculated as

  $${\mathrm{A}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{G}\mathsf{\lambda }}^2}{4\mathsf{\pi }}},$$

  (2)

  where λ is the wavelength of the signal and G is the antenna gain. Thus, PD can be derived as follows:

  $$\mathrm{PD}={\displaystyle \frac{{\mathrm{P}}_{\mathrm{R}}}{{\mathrm{A}}_{\mathrm{e}}}}.$$

  (3)

• 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]

  $$\mathrm{SAR}(P)={\displaystyle \frac{\sigma \left|\mathrm{E}\right(P){|}^2}{\rho }},$$

  (4)

  where $\sigma$ is the conductivity of the tissue (S/m), $\mathrm{E}(P)$ is the electric field intensity (V/m), and $\rho$ is the mass density of the tissue (kg/${\mathrm{m}}^3$).

Table 4. Far-field and near-field exposure limits.

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

provides an overview of the current exposure limit recommendations for PD and SAR as established by various international regulatory bodies. These standards serve as guidelines to ensure safety and compliance in wireless communication systems.

4.3. EMF-Aware Radio Resource Management

Ultra-dense networks (UDNs) have emerged as a prominent solution to fulfill the high-capacity requirement of 5G networks. In the literature, the definition of a UDN varies. In [97], a UDN refers to a network where the density of the BSs is similar to or more than the density of the users. In [98], it is identified as a network reaching the point where its capacity grows sublinearly due to the growing impact of interference as the BS density increases. Another definition in [99] describes UDNs as networks with inter-site distances of only a few meters. The authors in [98] provided an overview of UDN development in a 5G network. The challenges of deploying UDNs are mainly higher interference and issues related to EE requirements. They noted the complexity of integrating EE-based resource allocation with interference management and traffic steering within UDNs, drawing significant research attention. Current radio resource allocation optimizations primarily target SE, EE, or a combination of both. Reviews of various resource allocation schemes in 5G cloud RAN (CRAN) and heterogeneous networks are presented in [6,8], offering detailed taxonomies of optimization methods and solution approaches. However, these studies do not incorporate EMF exposure as a constraint in their objective functions. More recently, [20] surveyed resource allocation for 5G heterogeneous networks, focusing on metrics such as EE, power consumption, quality-of-service (QoS), and secrecy rate. Despite extensive coverage, EMF-aware resource allocation remains underexplored. The authors emphasized the need for green communication approaches and recommended deep learning-based algorithms for efficient resource allocation in 5G networks.

The introduction of small cells, precise beamforming, and other 5G enhancements has raised concerns about exceeding EMF exposure limits. In [100], a joint subcarrier and power allocation method incorporating the PD and SAR metrics to keep EMF exposure within safe limits while ensuring QoS is proposed. However, this study focused solely on downlink pre-5G transmissions in single-cell networks. In [101], a non-convex optimization problem was formulated to maximize EE with SE constraints in a non-orthogonal multiple access (NOMA) system. A comparison with conventional orthogonal frequency-division multiple access (OFDMA) in [102] demonstrated the superior performance of NOMA with optimized power allocation. Similarly, [103] applied non-cooperative game theory to maximize EE for cognitive radio NOMA (CR-NOMA), showing substantial improvements over conventional orthogonal multiple access (OMA) presented in [104].

Further advancements include EMF-aware resource allocation schemes using power-domain NOMA (PD-NOMA). In [105], a convex optimization approach is proposed to minimize EMF exposure for uplink NOMA while maintaining an acceptable QoS. In [106], a K-means clustering algorithm is employed to group the users based on channel properties, and successive interference cancelation (SIC) is applied to reduce intra-group interference. The proposed ML-based strategy outperformed conventional techniques. Nevertheless, future work must address EMF-aware resource allocation for advanced architectures such as multi-tier heterogeneous networks, mmWave communications, and unmanned aerial vehicles (UAVs).

The unique challenges of mmWave transmission, particularly its susceptibility to terrain irregularity and the geometric characteristics of buildings or obstacles, necessitate novel solutions. Traditional network designs fail in dense urban scenarios, where line-of-sight (LoS) conditions are critical. Deploying additional gNBs increases costs and power consumption, but reconfigurable intelligent surfaces (RISs) offer a cost-effective alternative to enhance mmWave coverage. Despite their potential, most studies on RIS-assisted 5G networks prioritize SE or EE, neglecting EMF exposure. Few studies address EMF-aware designs for RIS-enhanced environments. The few studies investigating EMF exposure in 5G networks assisted by RISs are summarized in

Table 5. Summary of recent studies in EMF-aware 5G radio resource management.

Ref	Requirements	Purposes	Constraints	Algorithm	Pros (+) Limitation (-)	Architecture
[100]	Overall sum of transmit power minimization	Subcarrier indexing of user Power allocation	MPE SAR QoS	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	Subcarrier selection Power assignment	QoS Maximum transmit power Maximum number of users	Convex optimization problem	+Low complexity. -Limited UEs in a single cell are considered.	Single uplink NOMA cell
[106]	Uplink electromagnetic exposure minimization	Subcarrier selection Power assignment	QoS Transmit power SIC Number of users per subcarrier	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	Power allocation	Maximum transmit power EMF constraint	Enhanced EMF-aware BF scheme	+Low complexity. -Optimized solutions are not considered.	MU-MIMO RIS-aided 6G network
[74]	Maximization of data rate	Power allocation	Maximum transmit power EMF constraint	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	Beamforming weight design Reflective precoding weight design	Transmit power Uplink and downlink SAR	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	Power assignment Reflective precoding weight design	Spectral efficiency Transmit power	Optimal step size sub-algorithm Lagrangian multipliers	+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	Downlink data rate	EMF constraint Received power	Beamforming truncation	+Low complexity. -Simple network topology is considered.	mMIMO with single RIS
[75]	Maximizes the received power	Beamforming weight design	EMF constraint	Angularly equalized virtual propagation channel	+Low complexity. -Simple network topology is considered.	mMIMO with single RIS
[109]	Maximize coverage probability	Restricted area density allocation	Restricted distance Maximum power density	Dimension search algorithm (bi-sectional or golden section)	+Low complexity. -Simple network topology is considered.	5G cellular network
[110]	Maximize achievable rate	Number of antennas User association	SNR threshold Maximum power density	Convex optimization problem	+Low complexity. -Simple network topology is considered.	mMIMO with mmWave (28 GHz) frequency.

. In [107], passive and active RISs were compared for signal-to-interference-plus-noise ratio (SINR) performance, with active RISs demonstrating superior SINR improvements. However, this study did not evaluate EMF exposure differences, highlighting a significant research gap.

Beamforming (BF) techniques with EMF awareness were explored in [75,108]. These studies introduced innovative BF schemes and compared them to conventional techniques, including maximum ratio transmission (MRT) and zero forcing, with and without RIS integration. The proposed schemes excelled in optimizing spectral efficiency while minimizing EMF exposure, providing a balanced approach to both communication performance and safety considerations.

In [109], the authors analyze joint uplink and downlink EMF exposure in wireless networks, aiming to maximize coverage probability while minimizing exposure within safety standards. The study models exposure as a function of base station density, user power, and traffic distribution. It reveals that downlink transmissions typically dominate in terms of the total exposure, whereas uplink contributions become significant in sparse networks or poor signal conditions. The authors propose strategies such as optimizing base station placement, improving beamforming, and balancing traffic to reduce exposure. In [110], the authors address the challenge of optimizing user association in 5G networks while minimizing EMF exposure. They introduce an EMF-aware user association framework that balances network performance with compliance to EMF safety standards. The framework models user association as an optimization problem, incorporating factors such as base station transmit power, user distances, and traffic loads. By optimizing user assignments to base stations and leveraging advanced algorithms, this approach reduces both uplink and downlink EMF exposure while ensuring efficient resource utilization. Simulation results demonstrate that the proposed framework effectively minimizes EMF exposure without compromising network throughput, making it a practical solution for future 5G deployments. Table 5 provides a comprehensive summary of recent studies focused on EMF-aware radio resource management in 5G networks, highlighting key approaches, methodologies, and performance outcomes.

Integrating EMF radiation exposure constraints into radio resource allocation algorithms offers the potential for safer and more energy-efficient 5G services while ensuring users’ QoS requirements are met. This approach acknowledges the dual importance of minimizing EMF exposure and efficiently managing radio resources. EMF exposure considerations are incorporated into resource management problems in two primary ways. In the first case, EMF metrics, such as PD, are included directly in the objective function as cost parameters to be minimized. In the second case, EMF exposure metrics are treated as constraints within the optimization problem, ensuring that system performance is optimized while adhering to predefined safety limits. The optimization process involves several resource variables, including beamforming weights (digital or analog), reflecting weights for RISs, which can be passive or active, optimal placement of base stations (gNB), RIS antenna elements, and power allocation strategies. However, optimizing these resources while taking into account EMF exposure presents significant challenges. Balancing the intricate requirements of resource efficiency, user experience, and EMF exposure reduction is a complex task that demands advanced algorithmic solutions, such as non-convex optimization techniques, and a comprehensive understanding of the interplay between these factors. Figure 5 summarizes radio resource management considering EMF exposure metrics.

5. Challenges and Open Research Topics

This section discusses key challenges and open research areas related to EMF exposure in 5G networks, including health effects, measurement complexities, resource allocation, and the impact of emerging technologies like RISs.

5.1. Challenges

This subsection outlines specific challenges that need to be addressed.

5.1.1. EMF Exposure from 5G Technologies

The complexity of 5G network features, such as densification, massive MIMO, and mmWave technology, introduces significant challenges in understanding and managing EMF exposure. The long-term health effects remain largely unknown, requiring extensive research to provide conclusive evidence. Additionally, accurate measurement and assessment are hindered by factors such as proximity to transmission sources, diverse antenna types, and the lack of standardized measurement protocols. Public concern and widespread misinformation regarding 5G-related health risks further complicate efforts to provide clear, science-based information.

5.1.2. Resource Allocation in Dense 5G Networks

Balancing network performance with EMF exposure constraints in dense 5G deployments poses numerous challenges. Increased base station deployment to improve coverage and capacity can lead to higher EMF levels, particularly with advanced technologies such as cooperative MIMO, beamforming, and multi-tier heterogeneous networks. Practical issues, including dual main beam effects and other beamforming abnormalities, exacerbate these difficulties by causing an uneven EMF distribution. Effective strategies for EMF-aware resource allocation must consider these factors while maintaining optimal service quality.

5.1.3. Resource Allocation in RIS-Assisted 5G Networks

RIS technology can enhance energy efficiency and spectral efficiency, but it also complicates EMF exposure management. RISs introduce complex radiation patterns, making accurate EMF measurement more challenging. The lack of comprehensive studies on the impact of RISs in 5G networks necessitates further research. Factors such as beamforming, interference from reflections, and topology-specific measurement issues require innovative strategies for EMF-aware resource management.

5.2. Open Research Topics

This subsection provides a list of research directions based on the identified challenges.

5.2.1. Joint Optimization of Energy Efficiency and EMF Constraints

Optimizing both energy efficiency (EE) and EMF exposure requires advanced algorithms that balance network performance and regulatory safety limits [72]. RIS deployments, user association, multiplexing/modulation techniques, cooperative transmissions, power control, and beamforming can be leveraged to develop solutions that achieve sustainable and safe 5G operations.

5.2.2. Hybrid Precoding for RIS-Assisted Networks

Designing hybrid precoding algorithms under EMF exposure constraints presents a challenging optimization problem. Research should focus on formulating joint optimization strategies for precoding matrices and power allocation, incorporating machine learning and understanding radio propagation dynamics.

5.2.3. Comparative Analysis of Passive and Active RISs

Passive and active RIS technologies affect EMF exposure differently. Existing comparisons emphasize performance metrics [107], but further studies must evaluate their impact under EMF constraints to guide technology adoption.

5.2.4. EMF-Constrained Radio Network Planning

RIS-assisted 5G networks demand new radio network planning methods that incorporate EMF limitations. Planning must consider reflection properties, placement, the number of RIS elements, clustering, interference, and trade-offs between performance and EMF safety [111,112].

5.2.5. Joint Base Station and Active RIS Precoding Optimization

Future research should develop joint optimization algorithms for base station and active RIS precoding while addressing both EMF exposure and power constraints [75,76,77]. Techniques like fractional programming and deep reinforcement learning can be employed to enhance efficiency.

5.2.6. EMF-Aware Radio Access Network (RAN) Slicing

RAN slicing [113] must evolve to incorporate EMF awareness. Techniques optimizing antenna deployment, transmit power, and frequency bands while meeting EMF constraints can enhance network flexibility and safety.

5.2.7. Resource Management for Integrated Terrestrial and Non-Terrestrial Networks

Integrating terrestrial and non-terrestrial networks, including satellites, drones, and high-altitude platforms, requires EMF-aware resource management [114,115]. Research must address cross-network coordination and dynamic EMF optimization to ensure comprehensive performance and safety.

5.2.8. EMF-Aware IoT Applications

The proliferation of IoT devices, autonomous vehicles, and smart city applications in 5G networks increases EMF exposure complexity. Research is needed to develop strategies that manage EMF exposure while ensuring seamless connectivity, efficient device management, and robust data transmission. Optimization frameworks that dynamically adjust power levels and network parameters in real-time environments could improve both safety and performance.

5.2.9. Adaptive EMF Exposure Control Using AI-Driven Approaches

AI-driven solutions offer dynamic control of EMF exposure by optimizing network parameters such as power levels, beamforming, and antenna configurations in real time. Machine learning models, including deep reinforcement learning, can adapt to traffic patterns and user density to balance performance and safety. Federated learning enhances privacy, while explainable AI improves transparency for regulatory compliance. Future research should focus on scalable, energy-efficient AI frameworks for intelligent EMF management in 5G networks and beyond.

6. Conclusions

In this paper, we provided a comprehensive review of recent works on EMF exposure, focusing on four main topics. First, we analyzed existing state-of-the-art survey papers and distinguished our work by categorizing it into five dimensions: 5G, resource allocation (RA), reconfigurable intelligent surfaces (RISs), EMF exposure, and EMF evaluation frameworks. Second, we conducted an in-depth discussion and comparison of 5G technologies, EMF evaluation frameworks, and metrics, exploring how critical enabling technologies—such as network densification, massive MIMO, and mmWave—affect EMF radiation exposure. Additionally, we extensively reviewed EMF-aware radio resource allocation techniques for 5G networks with and without the integration of RIS technologies. For the EMF exposure evaluation, we categorized approaches into geometric stochastic, deterministic, and experimental methods, highlighting state-of-the-art frameworks; their applicability, advantages, and limitations; practical examples; and the key metrics used to measure exposure. Each topic was examined to provide a detailed overview of current research and advancements. Finally, we identified major challenges and outlined open research directions related to EMF exposure management and resource allocation in 5G networks, offering insights for future exploration in this evolving field.