论文标题
使用强化学习方法在NG-EPON中用于延迟管理的智能带宽分配
Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods
论文作者
论文摘要
提出并证明了一种新型的NG-EPON智能带宽分配方案，并为延迟管理提供了证明。我们验证固定和动态交通负载方案下提出的方案的能力，以达到平均延迟<1ms。 RL代理展示了一种有效的智能机制来管理潜伏期，该机制为下一代访问网络提供了有希望的IBA解决方案。
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.
