Vehicle-to-everything (V2X) is expected to become one of the main drivers of 5G business in the near future. Dedicated network slices are envisioned to satisfy the stringent requirements of advanced V2X services, such as autonomous driving, aimed at drastically reducing road casualties. However, as V2X services become more mission-critical, new solutions need to be devised to guarantee their successful service delivery even in exceptional situations, e.g. road accidents, congestion, etc. In this context, we propose π-ROAD, a deep learning framework to automatically learn regular mobile traffic patterns along roads, detect non-recurring events and classify them by severity level. π-ROAD enables operators to proactively instantiate dedicated Emergency Network Slices (ENS) as needed while re-dimensioning the existing slices according to their service criticality level. Our framework is validated by means of real mobile network traces collected within 400 km of a highway in Europe and augmented with publicly available information on related road events. Our results show that π-ROAD successfully detects and classifies non-recurring road events and reduces up to 30% the impact of ENS on already running services.

The Cloud-RAN (C-RAN) paradigm is envisioned to increase the efficiency of future mobile networks by moving the computational resources needed at the Remote Radio Heads (RRH) to the cloud infrastructure. In this work, we provide a framework that optimizes the number of allocated virtual resources by considering both the computational requirements of the RRH and the Quality of Service of users, which could experience loss of service due to reassociations between the RRH and the virtual machines. The provided optimization framework is supported by data coming from a real mobile network of a middle-sized European city, which provides an estimate for the computational loads coming from the RRH. We evaluate the performance of the framework in different scenarios, analyzing the impact of different forecasting algorithms as well as different look-ahead intervals for the predictions (short-term / long-term). The results obtained by our framework can be used to assist network operators in the optimization of C-RAN resources and shed some light on the interplay between forecasting errors and overall performance.

We analyze a city-wide dataset of 4G mobile network traffic obtained directly from user-side logs, allowing fine-grained analyses of different application services over time and space. We group applications in classes and analyze their traffic patterns: the analysis reveals great heterogeneity in the usage of different applications and in their space/time correlations, with important implications for future networking services such as network slicing and resource allocations.