Towards AI-Driven on-Demand Routing in 6G Wide-Area Networks

In upcoming sixth generation (6G) networks, it is a critical challenge to support a plethora of innovative services across wide-area networks. To realize the dedicated QoS provisioning and meet the diverse quality of service (QoS) requirements of services in terms of criteria like bandwidth, delay, jitter and loss ratio, we proposed an AI-driven on-demand routing framework to support the diverse end-to-end QoS Provisioning in large-scale wide-area networks. Specifically, we make further efforts on solving the instability and non-convergence issues of the AI-driven routing algorithm and enhance it with the assistance of expert knowledge on traffic engineering and the latest advance on reinforcement learning. Furthermore, the simulation results show that our algorithm outperforms other benchmark routing algorithms with efficient learning and a significant reduction in delay, jitter and loss ratio by the traffic data sets of the real-world wide-area networks.