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RAIK: Regional analysis with geodata and crowdsourcing to infer key performance indicators

Key Performance Indicators (KPIs) are important measures of the quality of service in cellular networks. There are multiple efforts by cellular carriers and 5G standardization to leverage the KPIs to minimize drive tests (MDT) and self-organize the network for optimal performance via user feedback. Such an approach accounts for user devices in the field of their operation according to their normal usage and circumvents a number of costs (e.g., manpower, equipment) traditionally covered by the carrier, either directly or through a third party. In this paper, we build a Regional Analysis to Infer KPIs (RAIK) framework to establish a relationship between geographical data and user data using crowdsourced measurements. To do so, we use a neural network and crowdsourced data obtained by user equipment (UE) to predict the KPIs in terms of the reference signal's received power (RSRP) and path loss estimation. Since these KPIs are a function of terrain type, we provide a two-layer coverage map by overlaying a performance layer on a 3-dimensional geographical map. As a result, we can efficiently use crowdsourced data (to not overextend user bandwidth and battery) and infer KPIs in areas where measurements have not or can not be performed. For example, we show that RAIK can use only geographical information to predict the KPIs in areas that lack signal quality data with a negligible mean squared error, a seven-fold reduction in error from state-of-the-art solutions.