In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near a 4G and 5G base-station site in Belgium. For Twitch, VoLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time, both for 4G and 5G. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%.
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This study evaluates the maximum theoretical exposure to radiofrequency (RF) electromag- netic fields (EMFs) from a Fifth-generation (5G) New Radio (NR) base station (BS) while using four commonly used mobile applications: YouTube for video streaming, WhatsApp for voice calls, Instagram for posting pictures and videos, and running a Video game. Three factors that might affect exposure, i.e., distance of the measurement positions from the BS, measurement time, and induced traffic, were examined. Exposure was assessed through both instantaneous and time-averaged extrapolated field strengths using the Maximum Power Extrapolation (MPE) method. The former was calculated for every measured SS-RSRP (Secondary Synchronization Reference Signal Received Power) power sample obtained with a sampling resolution of 1 second, whereas the latter was obtained using a 1-min moving average applied on the applications’ instantaneous extrapolated field strengths datasets. Regarding distance, two measurement positions (MPs) were selected: MP1 at 56 meters and MP2 at 170 meters. Next, considering the measurement time, all mobile application tests were initially set to run for 30 minutes at both MPs, whereas the video streaming test (YouTube) was run for an additional 150 minutes to investigate the temporal evolution of field strengths. Considering the traffic, throughput data vs. both instantaneous and time-averaged extrapolated field strengths were observed for all four mobile applications. In addition, at MP1, a 30-minute test without a User Equipment (UE) device was conducted to analyze exposure levels in the absence of induced traffic. The findings indicated that the estimated field strengths for mobile applications varied. It was observed that distance and time had a more significant impact than the volume of data traffic generated (throughput). Notably, the exposure levels in all tests were considerably lower than the public exposure thresholds set by the ICNIRP guidelines.INDEX TERMS 5G NR, C-band, human exposure assessment, mobile applications, traffic data, maximum extrapolation method, RF-EMF.
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This review offers a detailed examination of the current landscape of radio frequency (RF) electromagnetic field (EMF) assessment tools, ranging from spectrum analyzers and broadband field meters to area monitors and custom-built devices. The discussion encompasses both standardized and non-standardized measurement protocols, shedding light on the various methods employed in this domain. Furthermore, the review highlights the prevalent use of mobile apps for characterizing 5G NR radio network data. A growing need for low-cost measurement devices is observed, commonly referred to as “sensors” or “sensor nodes”, that are capable of enduring diverse environmental conditions. These sensors play a crucial role in both microenvironmental surveys and individual exposures, enabling stationary, mobile, and personal exposure assessments based on body-worn sensors, across wider geographical areas. This review revealed a notable need for cost-effective and long-lasting sensors, whether for individual exposure assessments, mobile (vehicle-integrated) measurements, or incorporation into distributed sensor networks. However, there is a lack of comprehensive information on existing custom-developed RF-EMF measurement tools, especially in terms of measuring uncertainty. Additionally, there is a need for real-time, fast-sampling solutions to understand the highly irregular temporal variations EMF distribution in next-generation networks. Given the diversity of tools and methods, a comprehensive comparison is crucial to determine the necessary statistical tools for aggregating the available measurement data.
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