Massive-MIMO (M-MIMO) allows to schedule users with high gain narrow beams and to reduce intra-cell inter-beam interference. Close to cell edge, users may experience high interference from beams of neighboring cells which degrades their performance. This paper introduces low complexity inter-cell interference coordination between neighboring cells for M-MIMO systems implementing Grid of Beams (GoB) using Radio Environment Maps (REMs). The REMs are designed using the Kriging with the Covariance Tapering spatial interpolation technique and are created for the serving and the interfering beams. The interference coordination is introduced as constraints to the schedulers that avoid simultaneous scheduling of users served by highly interfering beams from the neighboring cells. The coordination decision is based on information retrieved from REMs and its quality depends on the REMs’ precision. Around 72 percent performance gain in terms of mean user throughput is achieved by the REM based coordination with respect to a baseline solution without coordination, and around 40 percent gain with respect to a state of the art solution that implements coordination using information at beam level resolution.

Recently, the monitoring of the radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks has received a great deal of attention. In this work, a set of 70 microenvironments (MEs) located in urban and rural areas, are selected in France under on the one hand the French Beyond5G project and on the other hand the 5G expOsure, causaL effects and rIsk perception through citizen engagement (GOLIAT) EU project. The purpose of this study is to assess the RF-EMF DL exposure in residential areas, downtowns, business areas, train stations, and public transport rides. For that, we employ the personal ExpoM-RF4 dosimeter placed inside a backpack to perform the measurements in different MEs. To take into consideration the effect of the presence of the human body near the dosimeter, we propose a correction approach that is mainly based on comparing the measurements given by ExpoM-RF4 to the ones provided by a reference system using the Tektronix real-time spectrum analyzer (RTSA) far from the body. Then, we use metrics, such as quadratic mean, standard deviation, and median of the electric (E) field to carry out a comparative study between different MEs with different RF bands. It was found that the RF-EMF exposure levels for all MEs are well below the maximum allowable exposure limit prescribed by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). In addition, we perform clustering analyses using the K-Means technique to group the MEs with comparable exposure levels. The results show that the exposure level is low, but generally higher in MEs located in Paris than in the other considered areas (i.e., Massy and three villages namely Igny, Bures-sur-Yvette and Gif-Sur-Yvette). For example, we observe that outdoor MEs can be grouped into three clusters, where the average total E fields (ATEFs) are 0.77 V/m, 0.35 V/m, and 0.08 V/m for the MEs belonging to the first, second and third clusters, respectively. Note that the first cluster here mainly contains the MEs located in Paris. This can be explained by the important number of antennas deployed in that area to serve the huge amount of users. We also observe few locations with exceptions confirming the presence of heterogeneous environments in the vicinity of some areas. For instance, three MEs in Paris among fifteen have an exposure level similar to Massy MEs in outdoor areas. The clustering of MEs located in transport station, and moving transport also show similar results. Finally, we see that the ATEF in a shopping center and a university in Paris have the highest exposure values comparing to others located in Massy and villages. This also due to the considerable number of deployed antennas in Paris.

With the ever-increasing demand for high-speed and low-latency wireless communications, new generation of base stations, i.e., the fifth generation (5G), has been deployed recently. However, this deployment has raised growing concerns regarding the potential public health implications associated with exposure to radio-frequency electromagnetic fields (RF-EMF). According to the type of sources, RF-EMF exposure can be catergorized into uplink (device-to-base station) and downlink (base station-to-device) transmissions. In this paper, we aim to analyze uplink exposure induced by mobile devices operating on 5G and previous generations. A total of 380 measurement points are selected for evaluating three types of services, i.e., voice call, video call, file uploading, in the Greater Paris region. Data collected from those measurements cover technologies in 2G, 3G, 4G, and 5G, where 5G refers to non-standalone (NSA) in the current paper. We compare the average transmitted (TX) power, throughput, connection duration, and energy utilization efficiency for different services and frequency bands. To assess the impact of 5G, we compared measurements with the 5G functionality enabled and disabled, respectively. The results show that mobile phones emit higher power during data-intensive services. Additionally, mobile phones operating on 5G emit lower TX power than 4G. Furthermore, the UL exposure level also depends on the the duration of emission. For a given file size to upload, the duration depends on the throughput. Consequently, we also investigated the emitted energy consumption per bit transmitted, referred to as radiated energy per bit transmitted (REBT). The comparison reveals that REBT in file uploading is 9.65 mJ/Mb for 4G and 5.1 mJ/Mb for 5G, indicating a significant decrease in radiated energy consumption with the adoption of 5G technology. This research enhances our understanding of uplink RF-EMF exposure associated with 5G and provides crucial insights for public health risk assessments.

With the increasing use of wireless communication systems, assessment of exposure to radio-frequency electromagnetic field (RF-EMF) has now become very important due to the rise of public risk perception. Since people spend more than 70% of their daily time in indoor environments, including home, office, and car, the efforts devoted to indoor RF-EMF exposure assessment has also increased. However, assessment of indoor exposure to RF-EMF using a deterministic approach is challenging and time consuming task as it is affected by uncertainties due to the complexity of the indoor environment and furniture structure, existence of multiple reflection, refraction, diffraction and scattering, temporal variability of exposure, and existence of many obstructions with unknown dielectric properties. Moreover, it is also affected by the existence of uncontrolled factors that can influence the indoor RF-EMF exposure such as the constant movement of people and random movement of furniture and doors as people are working in the building. In this study, a statistical approach is utilized to characterize and model the total indoor RF-EMF down-link (DL) exposure from all cellular bands on each floor over the length of a wing since the significance of distance is very low between any two points on each floor in a wing and the variation of RF-EMF DL exposure is mainly influenced by the local indoor environment. Measurements were conducted in three buildings that are located within a few hundred meters vicinity of two base station sites supporting several cellular technologies (2G, 3G, 4G, and 5G). We apply the one-sample Kolmogorov–Smirnov test on the measurement data, and we prove that the indoor RF-EMF DL exposure on each floor over the length of a wing is a random process governed by a Gaussian distribution. We validate this proposition using leave-one-out cross validation technique. Consequently, we conclude that the indoor RF-EMF DL exposure on each floor over the length of a wing can be modeled by a Gaussian distribution and, therefore, can be characterized by the mean and the standard deviation parameters.

The prediction of the electric (E) field plays an important role in the monitoring of the radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks. In this paper, we present an approach to extrapolate the E field in an urban area using artificial neural network. We first apply a moving average method over a sliding window to average out the EMF random fluctuations and remove the noise produced during the drive test recording of E field measurements along the route. Using public accessed datasets, i.e., cartoradio and OpenStreetMap, we then extract relevant features, including the ones that have a relation with the number of active antennas and those used by Bertoni-Walfisch propagation model. By applying the Gram-Schmidt Orthogonalization procedure, we select the best subset of the extracted features as inputs to the artificial neural network (ANN). In this work, two disjoint subsets are selected for the learning and testing phases to evaluate the performance of our proposal in the extrapolation of the E field measurements and to quantify the uncertainty generated by the proposed predictor due to the dynamicity usage of cellular networks and geolocalization inaccuracies.

Wireless communications are increasingly used today. Despite such use, there is a significant perception of risk which makes exposure monitoring a significant concern today. The work described in this article was carried out within the framework of the European SEAWave project and the French Beyond5G project. The exposure assessment was evaluated using a personal exposimeter (MVG EMF Spy) whose compactness and ease of use make it more suitable and portable than a system combining measuring probes and spectrum analyzers. Measurements were carried out on the cellular frequency bands used by 2G, 3G, 4G, and 5G, as well as that of Wi-Fi, in different modes of public transportation (RER, metro, tramway, bus, and train) circulating in the Paris region. The measurements have been analyzed by frequency band, type of public transportation, and type of environment encountered. For each set of measurements (e.g., metro lines, tramways), the mean, standard deviation, skewness, and kurtosis were evaluated and analyzed. For all exposure measurements taken in the 700, 800, 900, 1800, 2100, 2600, and 3500MHz frequency bands, the overall average values are 0.39, 0.43, 0.30, 0.21, 0.18, 0.24 and 0.18Vm ^-1 , respectively. These measurements have, in all cases, a significant dispersion as shown by the ratios of standard deviations to mean values. The well-known K-means clustering technique was applied to these four parameters for different subsets of data. The number of clusters k = 3 has been chosen based on the analysis of the optimal value of k for the current dataset. Our analysis indicates that the first group’s members display the highest mean values with moderate variance and the lowest values for the third and fourth moments. The second cluster is distinguished by points with large mean and variance, accompanied by moderate skewness and kurtosis. Conversely, the third group comprises points with the smallest mean and variance values, yet the largest measurements for the third and fourth moments.