Unveiling Radio Resource Utilization Dynamics of Mobile Traffic through Unsupervised Learning

Understanding mobile traffic dynamics is a key issue to properly manage the radio resources in next generation mobile networks and meet the stringent requirements of emerging heterogeneous services, such as enhanced mobile broadband, autonomous driving, and extended reality (just to name a few). However, radio resource utilization patterns of real mobile applications are mostly unknown. This paper aims at filling this gap by tailoring an unsupervised learning methodology (i.e. K-means), able to identify similar radio resource utilization patterns of mobile traffic from an operating mobile network. Our analysis is based on datasets referring to residential and campus areas and containing wireless link level information (e.g., scheduling, channel conditions, transmission settings, and duration) with a very precise level of granularity (e.g., 1 ms). Obtained results reveal the properties of groups of sessions with similar characteristics, expressed in terms of bandwidth demands and application level requirements.