标题： 基于意图的无线网络调度器用于RAN切片：学习处理不同的网络场景 显示英文标题

标题： Intent-based Radio Scheduler for RAN Slicing: Learning to deal with different network scenarios

摘要： 未来移动网络具有在不同要求的各种应用之间分配可用无线资源的复杂任务。 无线接入网络切片使能够通过隔离并为每组应用使用专用资源来创建不同的逻辑网络。 在此场景中，无线资源调度（RRS）负责在切片之间分配可用的无线资源，以满足其服务级别协议（SLA）要求，在优先考虑关键切片的同时最小化意图违反的数量。 此外，确保RRS能够处理高度多样化的网络场景是至关重要的。 最近的几篇论文提出了基于机器学习的RRS的进展。 然而，场景和切片种类受到限制，这阻碍了在真实网络中部署后对模型泛化能力的可靠结论。 本文提出了一种基于意图的RRS，在无线接入网络（RAN）切片背景下使用多智能体强化学习。 所提出的方法在可用无线资源无法满足所有切片时保护高优先级的切片。 它使用迁移学习来减少所需的训练步骤数量。 所提出的方法和基线方法在包含不同切片类型、信道轨迹、活动切片数量和用户设备（UEs）以及UE特性的不同网络场景中进行了评估。 所提出的方法在保护优先级更高的切片方面优于基线方法，相比基线方法提高了40%，而在考虑所有切片时，相比基线方法提高了20%。 结果表明，通过使用迁移学习，所需训练步骤的数量可以减少八倍而不影响性能。 显示英文摘要

摘要： The future mobile network has the complex mission of distributing available radio resources among various applications with different requirements. The radio access network slicing enables the creation of different logical networks by isolating and using dedicated resources for each group of applications. In this scenario, the radio resource scheduling (RRS) is responsible for distributing the radio resources available among the slices to fulfill their service-level agreement (SLA) requirements, prioritizing critical slices while minimizing the number of intent violations. Moreover, ensuring that the RRS can deal with a high diversity of network scenarios is essential. Several recent papers present advances in machine learning-based RRS. However, the scenarios and slice variety are restricted, which inhibits solid conclusions about the generalization capabilities of the models after deployment in real networks. This paper proposes an intent-based RRS using multi-agent reinforcement learning in a radio access network (RAN) slicing context. The proposed method protects high-priority slices when the available radio resources cannot fulfill all the slices. It uses transfer learning to reduce the number of training steps required. The proposed method and baselines are evaluated in different network scenarios that comprehend combinations of different slice types, channel trajectories, number of active slices and users' equipment (UEs), and UE characteristics. The proposed method outperformed the baselines in protecting slices with higher priority, obtaining an improvement of 40% and, when considering all the slices, obtaining an improvement of 20% in relation to the baselines. The results show that by using transfer learning, the required number of training steps could be reduced by a factor of eight without hurting performance.