An Extrapolation Approach for RF-EMF Exposure Prediction in an Urban Area Using Artificial Neural Network

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.