This paper presents some theoretical considerations and experimental results regarding the problem of maximum power extrapolation for the assessment of the exposure to electromagnetic fields radiated by 5G base stations. In particular the results of an extensive experimental campaign using an extrapolation procedure recently proposed for 5G signal is discussed and experimentally checked on a SU-MIMO signal. The results confirm the effectiveness of the extrapolation technique. Starting from an analysis (that represents a further novel contribution of this paper) on the impact of Spatial Division Multiple Access techniques used in 5G on the measurement of EMF level, some indications of possible extension of the technique to the highly complex MU-MIMO case are also given.INDEX TERMS 5G mobile communication, antennas, base stations, health and safety, MIMO.

5G base stations usually use different beams to transmit broadcast and user data. Moreover the broadcast beam is always "on air", whilst the traffic beam is not. This represents a problem in Maximum Power Extrapolation (MPE) procedures for exposure assessment. In fact, currently adopted measurement approaches are based on the mere observation of phenomena. Recently, a different approach for MPE has been proposed in [1], forcing the traffic toward the measuring position by means of a dedicated User Equipment (UE). Consequently, the measurer loses the "passive" role assumed in the approach usually adopted, and acquires an active role forcing the system under test to assume the most suitable configuration. The use of beam-forcing UEs opens new exciting possibilities, since it makes it possible to take advantage of the UE-specific signals for the estimation for the MPE procedure. The aim of this paper is to explore the potential offered by UE-specific data structures within the MPE considering a real case regarding data acquired on a currently operative 5G base station.

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The exponential growth of mobile traffic is forcing operators to quickly increase the capacity of their networks by means of new technologies and advanced architectures. This capacity expansion not only brings increasing fixed costs for additional network infrastructures, but also inflates operational costs, which are becoming critical, mainly in terms of energy bills. In this perspective, monitoring the energy consumption of network devices and defining their energy profile models are valuable approaches for estimating energy costs and identifying the most efficient configurations. In this article, we propose an energy profiling approach that simplifies the characterization of different base station components and allows the estimation of the network energy efficiency relying only on traffic statistics. We have validated the approach over the extensive dataset of real measurements provided by a probe network for monitoring live energy consumption of Vodafone sites in three different countries

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