The recent deployment of 5G networks is bringing benefits to the population but it is also raising public concern about human RF-EMF exposure levels. This is particularly relevant considering the next 5G mobile devices, which are placed in close proximity to the subjects. Therefore, the aim of the following paper is focused on expanding the knowledge of the exposure levels in 5G exposure scenarios, specifically for mobile applications, using computational methods. The mobile antenna was designed considering the 5G technology innovations (i.e., mm-wave spectrum, beamforming capability, high gain and wide coverage), resulting in a phased-array antenna with eight elements at the working frequency of 27 GHz. To assess the exposure levels, different types of skin models with different grades of details and layers were considered. Furthermore, not only was the presence of a mobile phone user simulated, but also that of a person in their proximity, who could be hit by the main beam of the phased-array antenna. All the simulations were conducted in Sim4Life platform, where the exposure levels were assessed in terms of absorbed power density averaged over 4 cm² and 1 cm², following the ICNIRP guidelines. The results highlighted that the use of the homogeneous skin model led to the absorbed power density peaks being greatly underestimated, with respect to those obtained in multilayer skin models. Furthermore, interestingly, we found that the exposure levels obtained for the person passing nearby were slightly higher than those experienced by the mobile phone user himself. Finally, using the allowed input power for real mobile applications, all the values remained below the limits indicated by the ICNIRP guidelines.

The deployment of near future 5G networks will introduce modifications in the population’s exposure levels to radio-frequency electromagnetic fields (RF-EMFs). The present work aimed to face the challenge of studying the exposure variability in the presence of an access point (AP) at 3.7 GHz with 64 patch elements uniform planar array antenna and 3D beamforming capability. The novelty introduced in the methodology of the exposure’s evaluation was the combining of traditional computational methods with a new approach based on stochastic dosimetry, called polynomial chaos kriging method, in order to estimate the exposure levels for 1000 different antenna beamforming patterns with low computational efforts. The simulations were evaluated considering a child model and computing the specific absorption rate (SAR) in different tissues. The analysis of the results highlighted a high exposure variability scenario depending on the beamforming patterns of the array antenna and identified the ranges of elevation and azimuth angles of the main antenna beam that may cause the highest levels of exposure.

The Kalman filter is proposed here to track the phase noise introduced by independent transmit and receive oscillators in multiple-input multiple-output transmission. To take advantage of pilot symbols that are transmitted before and after the time instant where one wants to estimate the vector of phases, a forward-backward Kalman filter is proposed. To reduce the complexity of the Kalman filter, suboptimal methods are introduced and their performance is compared to that of the full complexity Kalman filter by computer simulation.

The study aimed at expanding the knowledge about the assessment of radio-frequency electromagnetic fields (RF-EMF) exposure, considering the novelties introduced by the incoming 5G networks. Specifically, a possible future case of indoor exposure scenario is investigated, where the presence of a 5G access point (AP) in a room is simulated. The AP was modelled by two different indoor uniform planar array (UPA) antennas at 3.7 GHz and at 14 GHz, to evaluate how the beamforming and the higher frequency use could impact the exposure levels. Different scenarios were evaluated, considering the maximum antenna gain, two different human computational models, an adult model and a child one, and by varying the distance and the orientation between the UPA antenna and the two models head. All the simulations were conducted using the Sim4Life platform and in particular the exposure levels were expressed by the specific absorption rate averaged on 10 g of tissue ( $SAR {_\text{10}{g}}$ ), which was analyzed for the skin and for some specific tissues. The work underlined that the highest $SAR {_\text{10}{g}}$ values were obtained in the head area for all scenarios, with the skin $SAR {_\text{10}{g}}$ highest peaks when the UPA is placed laterally to the human model (195.73 mW/kg and 223.29 mW/kg for the adult and child model, respectively, for 100 mW input power). Furthermore, the work permitted to highlight that the $SAR {_\text{10}{g}}$ exposure levels are slightly higher for the child model, compared to the adult one and that the distance between the UPA antenna and the human models could greatly lower the $SAR {_\text{10}{g}}$ levels. At last, it was found that the $SAR {_\text{10}{g}}$ exposure levels obtained with the UPA antenna at 14 GHz were lower than the ones at 3.7 GHz, although further investigations will be necessary.

We consider the optimization of a smart radio environment where meta-surfaces are employed to improve the performance of multiuser wireless networks working at sub-THz frequencies. Motivated by the extreme sparsity of the THz channel we propose to model each meta-surface as an electronically steerable reflector, by using only two parameters, regardless of its size. This assumption, although suboptimal in a general multiuser setup, allows for a significant complexity reduction when optimizing the environment and, despite its simplicity, is able to provide high communication rates. We derive a set of asymptotic results providing insight on the system behavior when both the number of antennas at the transmitter and the meta-surfaces area grow large. For the optimization we propose an algorithm based on the Newton-Raphson method and a simpler, yet effective, heuristic approach based on a map associating meta-surfaces and users. Through numerical results we provide insights on the system behavior and we assess the performance limits of the network in terms of supported users and spatial density of the meta-surfaces.