# Prediction of RF-EMF Exposure by Outdoor Drive Test Measurements  
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396-406. [[https://doi.org/10.3390/telecom3030021](https://doi.org/10.3390/telecom3030021)]([https://doi.org/10.3390/telecom3030021](https://doi.org/10.3390/telecom3030021))  
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exposure in urban public trans. In [10, 11], surrogate models are proposed to assess exposure induced by 4G networks. In [12], stochastic geometry is used as a powerful mathematical tool to model the distribution of RF-EMF exposure strengths induced by 5G networks. The simulation and mathematical modeling tools clearly bring advantages in terms of cost reduction. However, the validity of models is questioned due to the simplification of environments or propagation models. To overcome those limitations, new methods, such as artificial neural network (ANN) models, provide potential solutions from a data-driven point of view. Many machine learning or artificial intelligence (AI) related works have been dedicated to channel propagation, such as path loss prediction [13, 14]. In those papers, distance from the receiver to the source is the key parameter. Moreover, other features are extracted from the physical environment and fed to AI algorithms to predict propagation channel characteristics and perform optimization [15, 16]. Different from the prediction of propagation characteristics from single transmitters, EMF exposure deals with aggregated received powers from multiple BSs. To the best of the authors' knowledge, the applications of AI in the field of RF-EMF exposure are still very new but are receiving growing interest. Very rare works have started to exploit AI to predict, on the one hand, the power emitted by a mobile phone (equivalent to UL exposure) [17, 18] and, on the other hand, the DL exposure [19, 20, 21]. Regarding the UL exposure, the ANN model of [17] and the machine learning models of [18] are fed with easily available parameters such as the DL connection indicators, e.g., reference signal received power (RSRP), and other information related to the environment. They considered realistic data by carrying out indoor measurement campaigns and outdoor drive test measurements, respectively. In [19], machine learning is exploited for the estimation of DL and UL EMF exposure in multi-source indoor WiFi scenarios. In [20], the DL exposure map in indoor environments is reconstructed using convolutional networks. Both proposed models [19, 20] were validated on data collected from simulations. Indeed, ANN-based predictions either require prior knowledge of measurements or are only valid using simulations. Predicting the DL RF-EMF exposure based on realistic measurement data and only requiring publicly accessible information is not well addressed. In our previous work [21], we studied the possibility of utilizing an ANN model to predict exposure levels from simulated measurement data in an urban outdoor environment. In the present work, we intend to extend our work in [21] by conducting real-life drive test measurements in an outdoor urban environment. Broadband isotropic measurements of the E-field are performed over the frequency band from 700 to 2700 MHz in Paris. After data analysis, we performed RF-EMF spatial reconstruction by considering two different ANN models. The first one is a fully connected ANN model, where we take into account the \(N\) nearest BSs ignoring the corresponding operator. The second one is a hybrid model consisting of locally connected blocks, where each block is dedicated to a given operator with \(N\) nearest BSs as inputs. More common inputs consist of the position of the receiver and the time. Finally, the comparison between the two models, as well as the impact of the number of neighboring BSs (\(N\)) on the prediction accuracy, are addressed.