O-RAN specifications reshape RANs with function disaggregation and open interfaces, driven by RAN Intelligent Controllers. This enables data-driven management through AI/ML but poses trust challenges due to human operators' limited understanding of AI/ML decision-making. Balancing resource provisioning and avoiding overprovisioning and underprovisioning is critical, especially among the multiple virtualized base station(vBS) instances. Thus, we propose a novel Federated Machine Reasoning (FLMR) framework, a neurosymbolic method for federated reasoning, learning, and querying. FLMR optimizes CPU demand prediction based on contextual information and vBS configuration using local monitoring data from virtual base stations (vBS) on a shared O-Cloud platform.This optimization is critical, as insufficient computing resources can result in synchronization loss and significantly reduce network throughput. In the telecom domain, particularly in the virtual Radio Access Network (vRAN) sector, predicting and managing the CPU load of vBSs poses a significant challenge for network operators. Our proposed FLMR framework ensures transparency and human understanding in AI/ML decisions and addresses the evolving demands of the 6G O-RAN landscape, where reliability and performance are paramount. Furthermore, we performed a comparative analysis using \textit{DeepCog} as the baseline method. The outcomes highlight how our proposed approach outperforms the baseline and strikes a better balance between resource overprovisioning and underprovisioning. Our method notably lowers both provisioning relative to the baseline by a factor of 6.

翻译：O-RAN规范通过功能解耦与开放接口重塑了无线接入网（RAN），其驱动力来自RAN智能控制器。这使得通过人工智能/机器学习（AI/ML）实现数据驱动的管理成为可能，但由于人类操作员对AI/ML决策过程的理解有限，也带来了信任挑战。平衡资源供给、避免过度供给与供给不足至关重要，尤其是在多个虚拟化基站（vBS）实例之间。为此，我们提出了一种新颖的联邦机器推理（FLMR）框架，这是一种用于联邦推理、学习与查询的神经符号方法。FLMR基于共享O-Cloud平台上虚拟基站（vBS）的本地监测数据，利用上下文信息与vBS配置来优化CPU需求预测。此优化至关重要，因为计算资源不足可能导致同步丢失并显著降低网络吞吐量。在电信领域，特别是虚拟无线接入网（vRAN）领域，预测和管理vBS的CPU负载对网络运营商而言是一项重大挑战。我们提出的FLMR框架确保了AI/ML决策的透明度与人类可理解性，并满足了6G O-RAN场景中对可靠性和性能至关重要的动态需求。此外，我们使用\textit{DeepCog}作为基线方法进行了对比分析。结果表明，我们提出的方法优于基线，并在资源过度供给与供给不足之间取得了更好的平衡。我们的方法相较于基线，显著地将供给不足与过度供给的程度降低了6倍。