Learning Cellular Network Connection Quality with Conformal
This addresses the challenge of unreliable network quality maps for mobile network operators and researchers, though it is incremental as it builds on existing conformal prediction methods.
The paper tackles the problem of quantifying uncertainty in cellular network speed predictions by developing a novel conformal prediction technique to create uncertainty maps from noisy, unevenly distributed Ookla data, which enables targeted data collection to improve prediction reliability.
In this paper, we address the problem of uncertainty quantification for cellular network speed. It is a well-known fact that the actual internet speed experienced by a mobile phone can fluctuate significantly, even when remaining in a single location. This high degree of variability underscores that mere point estimation of network speed is insufficient. Rather, it is advantageous to establish a prediction interval that can encompass the expected range of speed variations. In order to build an accurate network estimation map, numerous mobile data need to be collected at different locations. Currently, public datasets rely on users to upload data through apps. Although massive data has been collected, the datasets suffer from significant noise due to the nature of cellular networks and various other factors. Additionally, the uneven distribution of population density affects the spatial consistency of data collection, leading to substantial uncertainty in the network quality maps derived from this data. We focus our analysis on large-scale internet-quality datasets provided by Ookla to construct an estimated map of connection quality. To improve the reliability of this map, we introduce a novel conformal prediction technique to build an uncertainty map. We identify regions with heightened uncertainty to prioritize targeted, manual data collection. In addition, the uncertainty map quantifies how reliable the prediction is in different areas. Our method also leads to a sampling strategy that guides researchers to selectively gather high-quality data that best complement the current dataset to improve the overall accuracy of the prediction model.