Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization

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 have limitations 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 multitopology evaluations, while maintaining perfect reliability. These results highlight the effectiveness and scalability of GPPO for practical O-RAN deployments.