$π$-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

Abstract

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 nonrecurring road events and reduces up to 30% the impact of ENS on already running services.

Fast and accurate edge resource scaling for 5G/6G networks with distributed deep neural networks

“…Additionally, slicing has been a fruitful field of application for online convex optimization [12], [26], [13]. Moreover, reinforcement learning [27], [28] for resource orchestration has been recently applied on the problem On the other hand, DNNs [10], [5], [24], [8], [18] with early exit(s), is a recent and promising research avenue with many under-exploited applications. Moreover, a key quantity is the amount of information transmitted to the remote layers.…”