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. We present 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.

About Nadia Chukhno

Nadia is a Post-Doc Researcher at the Networks Data Science group in IMDEA Networks Institute under the supervision of Prof. Marco Fiore. Her current research activity focuses on machine learning for network traffic forecasting.

This event will be conducted in English