# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 4 EMF Exposure Level and 5G Technologies
### 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 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Guidelines** & **Far-Field Limits (PD in W/m\({}^{2}\))** & **Near-Field Limits (SAR in W/KG)** \\ \hline ICNIRP [10; 94] & 10 & 2–6 \\ \hline FCC [95; 96] & 10 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Far-field and near-field exposure limits. 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.