Radio Access Networks (RANs) are critical infrastructures that mobile operators continuously upgrade to accommodate increasing data traffic demands, stricter performance requirements, and evolutions in radio technologies. RAN updates can affect carrier-level Key Performance Indicators (KPIs) that are the foundational input to data-driven models for network management. However, to date, no study has systematically examined the dynamics of RAN deployments, and little is known about the actual prevalence of RAN updates or their impact on Machine Learning (ML) models for network automation. This paper presents a first characterization of RAN updates in a nationwide operational infrastructure composed of over 500,000 carriers. A network-side vantage point lets us (i) investigate the type and frequency of RAN modifications, (ii) assess the impact of such changes on a primary KPI for network management, i.e., the traffic volume served by individual carriers, and (iii) verify the final effects on a classical downstream ML application, i.e., traffic prediction. Our results reveal that RAN updates take place with notable frequency, e.g., occurring every few days even in medium-sized cities. Also, they affect in a significant way the demands at a considerable fraction of pre-existing carriers, where they can curb the accuracy of ML traffic forecasting models.

The rollout of 5G represents a significant advancement in the telecommunications industry, offering the potential for markedly enhanced speeds, reduced latency, and improved connectivity. Considering these anticipated advantages, it is interesting to understand the progressive adoption of the new technology by operators and their subscribers. In this paper, we analyze the evolution and current operation of the nation-wide 5G network of Orange, a leading mobile operator in France. By inspecting longitudinal data about (i) the over-five-year-long development of the country-wide 5G radio access infrastructure and (ii) the last two years of 5G traffic demands, we unveil how the operator has planned the deployment of the 5G radio access and characterize the actual usage patterns of the available 5G infrastructure. We also investigate the recent introduction of a 5G Standalone (SA) commercial service and its adoption by the mobile subscribers. We show that by mid 2025, the 5G network under study has achieved substantial coverage of populated areas and the operator has very recently started adding capacity layers to its 5G radio access. However, our investigation reveals that such massive infrastructure deployment efforts are not matched by a commensurate adoption of the technology by the end users, as the 5G capacity -especially for SA- stays largely underutilized.

The rollout of 5G promises major improvements in speed, latency, and connectivity over previous-generation radio access technologies. Our study analyzes Orange's nationwide 5G network in France, combining longitudinal data on infrastructure deployment with data traffic patterns. Early results show that while 5G coverage has steadily expanded and is presently reaching the vast majority of the user population, adoption by mobile subscribers remains limited, leaving much of the new capacity underutilized.