Time-Series-Based Model and Validation for Prediction of Exposure to Wideband Radio Frequency Electromagnetic Radiation

Abstract

Strength of wideband electromagnetic radiations (EMRs) generated by telecommunication systems is a time-varying process typically characterized by a seasonal behavior due to daily and weekly variations of traffic in mobile networks summed to a stationary background generated by constant-power broadcasting systems. Besides a few papers showing the existence of peak and off-peak hours in the daily exposure, there is a significant lack of scientific literature investigating how exposure changes over time, although this is key to the three main objectives: 1) to determine the time intervals when exposure levels are expected to be the highest, 2) to assess worst case exposure and ensure that measurements are run in a conservative scenario to comply with limits enforced by national laws and European guidelines, and 3) to highlight variations in traffic to allocate network resources dynamically to optimize performance. We present a time-series analysis model that makes use of measured data to build a predictor of the exposure to wideband EMR. The main result is that exposure can be predicted by a model composed of three components capturing the average exposure, the daily and half-daily periodicity (seasonal behavior), and the correlations among residuals [modeled by an AR(2) for weekdays and Sunday, and by an AR(1) for Saturday, respectively]. We observed the strength of the periodic components differs more on weekdays compared to weekends. The forecasting model can be particularly useful to predict when exposure is expected to be the highest during the day, so that measurements carried out at that time will be conservative with respect to measurements run at other times of the day, and will therefore serve as the worst case for the assessment of compliance with exposure limits.