论文标题
学习安排（leasch）：5G Mac层中无线电资源调度的深入加强学习方法
Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer
论文作者
论文摘要
网络管理工具通常是从一代到另一代的继承。这是成功的，因为这些工具已被检查并定期更新以适合新的网络目标和服务要求。不幸的是，新的网络服务将使这种方法过时，并手工制作新工具或升级当前工具可能会导致复杂的系统，这些系统将极为难以维护和改进。幸运的是，AI的最新进展提供了新的有前途的工具，可以帮助解决许多网络管理问题。遵循这一有趣的趋势，当前文章提出了Leasch，这是一种深入的增强学习模型，能够在5G网络的MAC层中解决无线电资源调度问题。 Leasch是在沙箱中开发和培训的，然后部署在5G网络中。与许多关键性能指标中常规基线方法相比，实验结果证明了皮带的有效性。
Network management tools are usually inherited from one generation to another. This was successful since these tools have been kept in check and updated regularly to fit new networking goals and service requirements. Unfortunately, new networking services will render this approach obsolete and handcrafting new tools or upgrading the current ones may lead to complicated systems that will be extremely difficult to maintain and improve. Fortunately, recent advances in AI have provided new promising tools that can help solving many network management problems. Following this interesting trend, the current article presents LEASCH, a deep reinforcement learning model able to solve the radio resource scheduling problem in the MAC layer of 5G networks. LEASCH is developed and trained in a sand-box and then deployed in a 5G network. The experimental results validate the effectiveness of LEASCH compared to conventional baseline methods in many key performance indicators.
