Softwarized Resource Management and Allocation With Autonomous Awareness for 6G-Enabled Cooperative Intelligent Transportation Systems

Cooperative intelligent transport system (C-ITS) is one emerging application scenario in 6G. Within the content of 6G, softwarization is the dominant attribute of networks. 6G networks are required to have the intelligence and autonomy attributes, too. With softwarization and autonomy, not only the network capable of flexibly managing softwarized resources can be achieved, but also the network can learn and adapt itself with respect to the dynamic networking environment. However, multiple issues stand in the way of developing 6G networks, requiring to be addressed. In this paper, the softwarized resource management and allocation with autonomy and intelligence awareness in 6G networks for C-ITS application is researched. Firstly, key enabling technologies and problem model of 6G-enabled C-ITS are described. Then, an architecture design enabling to achieve the intelligent and softwarized resource management and allocation per service request, abbreviated as ReMaAl-AutoNet, is proposed. The proposed architecture design, based on reinforcement learning (RL), can realize the intelligent resource management and allocation by undergoing the training. Afterwards, simulations are illustrated to validate the proposed ReMaAl-AutoNet architecture. For instance, the successful ratio of ReMaAl-AutoNet has an advantage of over ten percentages than the direct counterpart without training.

Automated summary

This paper researches the issue of softwarized resource management and allocation in 6G networks for C-ITS application and proposes an architecture design based on reinforcement learning (RL) to achieve intelligent and softwarized resource management and allocation. Simulation results demonstrate that the proposed architecture has a higher success ratio advantage compared to the direct counterpart without training.