rApps and xApps need to be controlled and orchestrated well in the open radio access network (O-RAN) so that they can deliver a guaranteed network performance in a complex multi-vendor environment. This paper proposes a novel intent-driven intelligent control and orchestration scheme based on hierarchical reinforcement learning (HRL). The proposed scheme can orchestrate multiple rApps or xApps according to the operator's intent of optimizing certain key performance indicators (KPIs), such as throughput, energy efficiency, and latency. Specifically, we propose a bi-level architecture with a meta-controller and a controller. The meta-controller provides the target performance in terms of KPIs, while the controller performs xApp orchestration at the lower level. Our simulation results show that the proposed HRL-based intent-driven xApp orchestration mechanism achieves 7.5% and 21.4% increase in average system throughput with respect to two baselines, i.e., a single xApp baseline and a non-machine learning-based algorithm, respectively. Similarly, 17.3% and 37.9% increase in energy efficiency are observed in comparison to the same baselines.

翻译：在开放无线接入网（O-RAN）中，需要对rApp和xApp进行良好的控制与编排，以确保其在复杂的多供应商环境下交付有保障的网络性能。本文提出了一种基于分层强化学习（HRL）的新型意图驱动智能控制与编排方案。该方案可根据运营商优化特定关键绩效指标（KPI）（如吞吐量、能效和时延）的意图，对多个rApp或xApp进行编排。具体而言，我们提出了一种包含元控制器和控制器的双层架构。元控制器以KPI形式提供目标性能，而控制器在较低层级执行xApp编排。仿真结果表明，与单一xApp基线及非机器学习算法这两类基准方案相比，所提出的基于HRL的意图驱动xApp编排机制平均系统吞吐量分别提升了7.5%和21.4%。同时，相较于相同基准方案，能效分别提升了17.3%和37.9%。