The O-RAN Alliance promotes the integration of intelligent autonomous agents to control the Radio Access Network (RAN). This improves flexibility, performance, and observability in the RAN, but introduces new challenges, such as the detection and management of conflicts among the intelligent autonomous agents. A solution consists of profiling the agents before deployment to gather statistical information about their decision-making behavior, then using the information to estimate the level of conflict among agents with different goals. This approach enables determining the occurrence of conflicts among agents, but does not provide information about the impact on RAN performance, including potential service degradation. The problem becomes more complex when agents generate control actions at different timescales, which makes conflict severity hard to predict. In this paper, we present a novel approach that fills this gap. Our solution leverages the same data used to determine conflict severity but extends its use to predict the impact of such conflicts on RAN performance based on the frequency at which each agent generates actions, giving more weight to faster applications, which exert control more frequently. Via a prototype, we demonstrate that our solution is viable and accurately predicts conflict impact on RAN performance.

翻译：O-RAN联盟倡导引入智能自主代理来控制无线接入网络（RAN）。这提升了RAN的灵活性、性能和可观测性，但也带来了新的挑战，例如智能自主代理之间冲突的检测与管理。一种解决方案是在部署前对代理进行特征分析，以收集其决策行为的统计信息，进而利用这些信息估算具有不同目标的代理之间的冲突程度。该方法能够确定代理间冲突的发生，但无法提供关于冲突对RAN性能（包括潜在的服务质量下降）影响的信息。当代理在不同时间尺度上生成控制动作时，问题变得更为复杂，这使得冲突的严重程度难以预测。本文提出了一种填补这一空白的新方法。我们的解决方案利用与确定冲突严重程度相同的数据，但进一步将其用于预测此类冲突对RAN性能的影响，预测基于每个代理生成动作的频率，并对控制更频繁的快速应用赋予更高权重。通过原型验证，我们证明该方案切实可行，并能准确预测冲突对RAN性能的影响。