The good, the bad and the implications of profiling mobile broadband coverage

Pervasive coverage and continuous connectivity of Mobile Broadband (MBB) networks are common goals for regulators and operators. Given the increasing heterogeneity of technologies in the last mile of MBB networks, further support for seamless connectivity across multiple network types relies on understanding the prevalent network coverage profiles that capture different available technologies in an area. Correlating these coverage profiles with network performance metrics is of great importance in order to forestall disturbances for applications running on top of MBB networks. In this paper, we aim to profile MBB coverage and its performance implications from the end-user's perspective along critical transport infrastructures (i.e., railways in Norway). For this, we deploy custom measurement nodes on-board five Norwegian inter-city trains and we collect a unique geo-tagged dataset along the train routes. We then build a coverage mosaic, where we divide the routes into segments and analyze the coverage of individual operators in each segment. We propose and evaluate the use of hierarchical clustering to describe prevalent coverage profiles of MBB networks along the train routes and classify each segment accordingly. We further analyze the areas we classify with each profile and assess the packet-loss and HTTP download performance of the networks in those areas.