The fifth generation (5G) technology has been conceived to cover multiple usage scenarios from enhanced mobile broadband to ultra-reliable low-latency communications (URLLC) to massive machine type communications. However, the implementation of this new technology is causing increasing concern over the possible impact on health and safety arising from exposure to electromagnetic field radiated by 5G systems, making imperative the development of accurate electromagnetic field (EMF) measurement techniques and protocols. Measurement techniques used to assess the compliance with EMF exposure limits are object to international regulation. The basic principle of the assessment is to measure the power received from a constant radio frequency source, typically a pilot signal, and to apply a proper extrapolation factor. This kind of approach is standardized for 2G, 3G, and 4G technologies, but is still under investigation for 5G technology. Indeed, the use of flexible numerologies and advanced Time Division Duplexing (TDD) and spatial multiplexing techniques, such as beam sweeping and Massive Multiple Input Multiple Output (MIMO), requires the definition of new procedures and protocols for EMF measurement of 5G signals. In this paper a procedure for an accurate estimation of the instant maximum power received from a 5G source is proposed. The extrapolation technique is based on the introduction of proper factors that take into account the effect of the TDD and of the sweep beam in the measured value of the 5G signal level. Preliminary experimental investigation, based on code domain measurement of appropriate broadcast channels, and carried out in a controlled environment are reported, confirming the effectiveness of the proposed approach.

We target the problem of performing a large set of measurements over the territory to characterize the exposure from a 5G deployment. Since using a single Spectrum Analyzer (SA) is not practically feasible (due to the limited battery duration), in this work we adopt an integrated approach, based on the massive measurement of 5G metrics with a 5G smartphone, followed by a detailed analysis done with the SA and an ElectroMagnetic Field (EMF) meter in selected locations. Results, obtained over a real territory covered by 5G signal, reveal that 5G exposure is overall very limited for most of measurement locations, both in terms of field strength (up to 0.7 [V/m]) and as share w.r.t. other wireless technologies (typically lower than 15%). Moreover, our approach allows easily spotting measurement outliers, e.g., due to the exploitation of Dynamic Spectrum Sharing (DSS) techniques between 4G and 5G. In addition, the exposure metrics collected with the smartphone are overall a good proxy of the total exposure measured over the whole 5G channel. Moreover, the sight conditions and the distance from 5G base station play a great role in determining the level of exposure. Finally, a maximum of 130 [W] of power radiated by a 5G base station is estimated in the scenario under consideration.

The fifth generation mobile network introduces dramatic improvements with respect to the previous technologies. Features such as variable numerology, bandwidth parts, massive Multiple Input Multiple Output (MIMO) and Time Division Duplex (TDD) will extend the capabilities of the 5G wireless systems and, at the same time, will influence the measurement techniques used to assess the compliance with general public electromagnetic field exposure limits. In this study, a heterogeneous set of 5G signals is investigated with the aim of establishing an effective measurement technique suitable for the new technology. Following an experimental approach based on both modulation and zero span analysis, some important characteristics of the 5G system are highlighted and extensively discussed, and experimental procedures for estimating factors associated to TDD ($F_{TDC}$ factor) and beam sweeping (R factor), to be used in the extrapolation formulas, are presented. The results of this study represent a starting point for future investigations on effective methods to estimate both the instant maximum power and the total power transmitted during a 5G radio frame.

Maximum-Power Extrapolation (MPE) for mobile telecommunication sources follows an established paradigm based on the identification and measurement of a channel that acts as a power reference. Prior to the 5G era, the role of reference channel has been played by always-on broadcast signals since they had the great advantage of being always transmitted at the maximum power level allowed for a generic signal channel. However, the beamforming implemented by 5G sources obliges us to rethink this approach. In fact, with beamforming the 5G source can transmit data traffic streams through a beam characterized by a much higher gain than the broadcast one. This implies that the detected power for traffic beams could be much higher than the corresponding power of broadcast beams. In this paper, a novel approach for 5G MPE procedure is presented, where the direct measurement of the received power of a traffic beam is used to assess the maximum exposure generated by a 5G system. An innovative specific experimental setup is also proposed, with the use of a User Equipment (UE) with the aim of forcing the traffic beam toward the measurement positions. In this way, it is possible to directly measure the power of each Resource Element (RE) transmitted by the traffic beam. As opposed to other MPE proposals for 5G, the discussed technique does not require any correction of the measured data since it relies only on the traffic beam pointing toward the measurement position, simplifying the overall MPE procedure and thus reducing the uncertainty of the MPE estimated field strength.

The fifth-generation (5G) New Radio (NR) cellular network has been launched recently. The assignment of new spectrum bands and the widespread use of Massive MIMO (MaMIMO) and beamforming techniques for better radio coverage are two major features of the new architecture. They imply both opportunities and challenges, one of the most daring one among the latter ones is the research for methods to assess human exposure to electromagnetic fields radiated by the base stations. The long-term time-varying behavior and spatial multiplexing feature of the MaMIMO antennas, along with the radio resource utilization and adoption of Time-Division Duplexing (TDD), requires that the assessment of exposure to electromagnetic fields radiated by 5G systems is based on a statistical approach that relies on the space and time distribution of the radiated power. That, in turn, is determined through simulations based on the actual maximum transmitted power – defined as the 95th percentile of the empirical distribution obtained from historical data of radiated power – rather than on the nominal one. To ensure that exposure limits are never exceeded, a monitoring and control system (usually referred to as Power Lock (PL)) that limits the transmitted power can be used. In this paper we propose a methodology, independent from the specific technical solution implemented by the manufacturer, to characterize such control systems and determine their capability to limit the average power transmitted over a given time interval to a value that keeps the corresponding average exposure to electromagnetic fields below a specified value. Experimental results show the effectiveness of the methodology and that it can also be used to identify when the PL interacts with the higher levels of the MaMIMO system architecture.

Exposure from radio base stations is often only monitored over short periods, typically during the daily hours of working days. However, base station exposure can vary throughout the day and even between working days and weekends or holidays. The objective of this study is to determine to what extent short-term exposure measurements taken during daily hours are representative of daily average exposure levels. To achieve this goal, we analyzed a set of long-term measurements taken by monitoring units located in sensitive areas, which are characterized by a homogeneous distribution of users over working hours, such as hospitals, train stations, and university centers. Our results reveal that 6-minute measurements taken on working days can overestimate the average exposure level over 24 hours if taken over a wider time interval than that commonly considered for peak traffic and, therefore, higher exposure. Based on the common pattern of exposure over time in various locations, an extrapolation factor is proposed to predict daily exposure levels from short-time measurements.