Measurement-Based Outage Probability Estimation for Mission-Critical Services

An accurate estimation of the service quality that the user will experience along a route can be extremely useful for mission-critical services. Based on availability and reliability estimations, it can provide the network with in-advance information on the potential critical areas along the route. If such estimation is based on empirical/statistical or site-specific estimations, both of which are typically used for cellular network planning, it will lead to significant uncertainty in the estimation, as we demonstrate in this paper. Instead, if estimations are based on previously collected measurements, the uncertainty can be significantly reduced. In this paper, we analyze the achievable accuracy of such a data-driven estimation which aggregates measurements from multiple user equipment (UEs) moving along the same route by averaging the measured signal levels over a route segment. We evaluate the estimation error for both empirical/statistical, site-specific and data-driven estimations for measurements collected in urban areas. Based on the demonstrated advantage of data-driven estimation, and the relevance of including context information that we proved in a previous paper, we discuss and analyze how the estimation error can be reduced even further by predicting the Mean Individual Offset (MIO) that each specific UE will observe with respect to the average. To this end, we propose and evaluate a technique for MIO correction that relies on observing a time series of signal level samples when the UE starts a mission-critical service. By observing 100–300 m of real-time samples along the route results show that the overall estimation error can be reduced from 5–6 dB to 4 dB using MIO correction. Finally, using the obtained results, we illustrate how the signal level estimations can be used to estimate the outage probability along the planned route.