Enabling efficient resource allocation for applications in vehicular systems using Zero-touch management for Smart Mobility

Future applications such as vehicular applications require higher Quality of Service (QoS) standards than current applications do. For future mobile networks to support these applications and deal with the ever-growing number of connected devices, network operators will have to develop new Management and Orchestration (MANO) techniques to facilitate this. Zero-Touch Management (ZTM) offers a solution to this problem by stepping away from manual MANO towards fully automated MANO. In this paper, we discuss how vehicular applications such as collision avoidance can benefit from the deployment of ZTM system and the creation of Artificial Intelligence (AI) supported network intelligence for efficient resource allocation. In this way, network operators can offer the ultra-low latency requirements for collision avoidance to exist in the most cost-efficient way. To make the deployment of ZTM possible, real-life proof of concepts is necessary to illustrate the benefits of AI supported network intelligence. The data gathered from these testbeds can be used to further develop the Deep Reinforcement Learning (DRL) models used in the network intelligence.