AI-Based mechanism for the Predictive Resource Allocation of V2X related Network Services

Abstract

5G architectures will utilize the virtualization of the network functions (VNF) and the use of Multi-access edge computing (MEC) to gain multiple benefits such as simpler service orchestration, while simultaneously covering diverse use cases even with strict performance requirements. 5G service orchestration mechanisms will need to allow more efficient and flexible network deployment and operations in a resource-efficient and delay-sensitive manner. A field that is expected to be greatly boosted by these advances, is Cellular Vehicle to Everything communications. 5G will enable cooperative, connected and automated mobility services, which are often are safety critical while also having stringent delay requirements. This paper, proposes a mechanism that predicts the future position of a vehicle moving in both urban and/or highway environments. Based on this knowledge, it decides on the optimal position of VNFs so that the allocation of network resources can be preemptively requested. The objective of this mechanism is to ensure the uninterrupted, continuous connections of the vehicles, resulting in minimal or no service interruption time while ensuring an optimal utilization of Edge Cloud and MEC resources.