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rApps and xApps need to be controlled and orchestrated well in the open radioaccess network (O-RAN) so that they can deliver a guaranteed networkperformance in a complex multi-vendor environment. This paper proposes a novelintent-driven intelligent control and orchestration scheme based onhierarchical reinforcement learning (HRL). The proposed scheme can orchestratemultiple rApps or xApps according to the operator's intent of optimizingcertain key performance indicators (KPIs), such as throughput, energyefficiency, and latency. Specifically, we propose a bi-level architecture witha meta-controller and a controller. The meta-controller provides the targetperformance in terms of KPIs, while the controller performs xApp orchestrationat the lower level. Our simulation results show that the proposed HRL-basedintent-driven xApp orchestration mechanism achieves 7.5% and 21.4% increase inaverage system throughput with respect to two baselines, i.e., a single xAppbaseline and a non-machine learning-based algorithm, respectively. Similarly,17.3% and 37.9% increase in energy efficiency are observed in comparison to thesame baselines.

Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning