用图神经网络优化5G基站资源分配，降低成本并提升效率。
Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization
- 结合图神经网络与强化学习，感知网络拓扑进行智能决策。
- 在大规模场景下比现有方法降低18%部署成本，奖励提升25%。
- 适合5G网络优化、智能运维等实际工程场景使用。
开放无线接入网（O-RAN）通过解耦和虚拟化基带功能，实现灵活、可扩展且成本更低的移动网络。然而，这种灵活性也带来了资源管理挑战，需在动态需求和复杂拓扑下联合优化功能分割选择与虚拟单元部署。现有方案常分步处理，难以扩展至大规模真实场景。本文提出图增强近端策略优化（GPPO）框架，利用图神经网络（GNN）提取拓扑感知特征，并引入动作掩码机制高效处理组合决策空间。该方法联合优化功能分割与部署决策，充分捕捉O-RAN资源分配的复杂性。在小规模与大规模O-RAN场景下的实验表明，GPPO持续优于现有先进基线，在泛化测试中实现最高18%的部署成本降低与25%的奖励提升，同时保持完全可靠性。结果验证了GPPO在实际O-RAN部署中的有效性与可扩展性。
原文摘要 · Abstract (English)
Open Radio Access Network (O-RAN) architectures enable flexible, scalable, and cost-efficient mobile networks by disaggregating and virtualizing baseband functions. However, this flexibility introduces significant challenges for resource management, requiring joint optimization of functional split selection and virtualized unit placement under dynamic demands and complex topologies. Existing solutions often address these aspects separately or lack scalability in large and real-world scenarios. In this work, we propose a novel Graph-Augmented Proximal Policy Optimization (GPPO) framework that leverages Graph Neural Networks (GNNs) for topology-aware feature extraction and integrates action masking to efficiently navigate the combinatorial decision space. Our approach jointly optimizes functional split and placement decisions, capturing the full complexity of O-RAN resource allocation. Extensive experiments on both small-and large-scale O-RAN scenarios demonstrate that GPPO consistently outperforms state-of-the-art baselines, achieving up to 18% lower deployment cost and 25% higher reward in generalization tests, while maintaining perfect reliability. These results highlight the effectiveness and scalability of GPPO for practical O-RAN deployments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。