Dr Muhammad Ali Jamshed
The accurate measurement of electromagnetic exposure and its application is expected to become more and more important in future wireless communication systems, given the explosion in both the number of wireless devices and equipments radiating electromagnetic-fields(EMF)and the growing concerns in the general public linked to it. Indeed, the next generation of wireless systems aims at providing a higher data rate,better quality of service(QoS), and lower latency to users by increasing the number of access points,i.e.densification, which in turn will increase EMF exposure. Similarly, the multiplication of future connected devices,e.g. internet of things(IoT)devices, will also contribute to an increase in EMF exposure. This paper provides a detailed survey relating to the potential health hazards linked with EMF exposure and the different metrics that are currently used for evaluating,limiting and mitigating the effects of this type of exposure on the general public. This paper also reviews the possible impacts of new wireless technologies on EMF exposure and proposes some novel research directions for updating the EMF exposure evaluation framework and addressing these impacts in future wireless communication systems. For instance, the impact of mmWave or massive-MIMO/beamforming on EMF exposure has yet to be fully understood and included in the exposure evaluation framework.
The densification of wireless infrastructure to meet ever-increasing quality of service (QoS) demands, and the ever-growing number of wireless devices may lead to higher levels of electromagnetic field (EMF) exposure in the environment, in the 5G era. The possible long term health effects related to the EMF radiation are still an open debate and requires attention. Therefore, in this paper, we propose a novel EMF-aware resource allocation scheme based on the power domain non-orthogonal multiple access (PD-NOMA) and machine learning (ML) technologies for reducing the EMF exposure in the uplink of cellular systems. More specifically, we use the K-means approach (an unsupervised ML approach) to create clusters of users to be allocated together and to then strategically group and assign them on the subcarriers, based on their associated channel properties. Finding the best number of clusters in the PD-NOMA environment is a key challenge, and in this paper, we have used the elbow method in conjunction with the F-test method to effectively control the maximum number of users to be allocated at the same time per subcarrier. We have also derived an EMF-aware power allocation by formulating and solving a convex optimization problem. Based on the simulation results, our proposed ML-based strategy effectively reduces the EMF exposure, in comparison with the state-of-the-art techniques.