Thermal Radiation (TR) mode: A Deployment Perspective for 5G NR
<|reference_start|>Thermal Radiation (TR) mode: A Deployment Perspective for 5G NR: The 5G New Radio NR technology is under standardization process by 3GPP to provide outline for a new radio interface for the next generation of cellular networks. The aim of the 5G networks include not only to provide enhanced capacity coverage but also support advanced services such as enhanced mobile broadband (eMBB) Ultra-Reliable Low Latency Communication URLLC massive Machine Type Communication mMTC. Key features of NR include Ultra lean carrier design to minimize the power consumption by limiting the always-on signal transmissions and to reduce interference in the neighboring cells . Another feature is the use of massive number of antennas for transmission as well as reception of signals. This rise in the number of antennas to provide a greater coverage brings about various challenges and impact in the system. With the increase in investigations in the mmWave frequencies, there is a need to investigate the health hazards they have on human body and the environment at large. This paper intends to provide an insight into the harmful impacts of Radio Frequency RF fields. The radiation metric to study the RF impact for far field is power density and for near field is Specific Absorption Rate SAR. These are the two main EM radiation metrics to find out the exposure due to uplink and downlink phenomenon in mobile communications. Mobile communication systems are addressed particularly to discuss the Electromagnetic EM Radiation impact as smart phones are used in close proximity to the body. A proposal in the form of Thermal Radiation TR mode is given to reduce the radiations emitted from a mobile phone. The performance of the proposed mode is validated from the results by achieving reduced power density, complexity and exposure ratio.<|reference_end|>
@article{kour2024thermal,
    title={Thermal Radiation (TR) mode: A Deployment Perspective for 5G NR},
    author={Haneet Kour, Rakesh Kumar Jha, Sanjeev Jain, Shubha Jain},
    journal={IEEE Potentials, 2023},
    year={2024},
    doi={10.1109/MPOT.2021.3091077},
    archivePrefix={arXiv},
    eprint={2407.17511},
    primaryClass={cs.NI eess.SP}
}