Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization
This work addresses scalability and efficiency issues in O-RAN deployments, which is crucial for enabling flexible and cost-effective mobile networks, representing a strong specific gain in a domain-specific context.
The paper tackles the challenge of resource management in Open Radio Access Networks (O-RAN) by jointly optimizing functional split selection and virtualized unit placement under dynamic demands and complex topologies, achieving up to 18% lower deployment cost and 25% higher reward in generalization tests compared to state-of-the-art baselines.
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.