Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods
This addresses latency control in access networks for improved network performance, though it appears incremental as it applies an existing RL method to a specific domain problem.
The paper tackles latency management in next-generation Ethernet passive optical networks (NG-EPON) by proposing a reinforcement learning-based bandwidth allocation scheme, achieving an average latency of less than 1ms under both fixed and dynamic traffic loads.
A novel intelligent bandwidth allocation scheme in NG-EPON using reinforcement learning is proposed and demonstrated for latency management. We verify the capability of the proposed scheme under both fixed and dynamic traffic loads scenarios to achieve <1ms average latency. The RL agent demonstrates an efficient intelligent mechanism to manage the latency, which provides a promising IBA solution for the next-generation access network.