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

• URL: http://arxiv.org/abs/2509.10499v1
• Date: Mon, 01 Sep 2025 08:53:04 GMT
• Title: Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization
• Authors: Duc-Thinh Ngo, Kandaraj Piamrat, Ons Aouedi, Thomas Hassan, Philippe Raipin-Parvédy,
• Abstract summary: Open Radio Access Network (O-RAN) architectures enable flexible, scalable and cost-efficient mobile networks by disaggregating and virtualizing baseband functions.<n>This flexibility introduces significant challenges for resource management, requiring joint functional split selection and unit placement optimization under dynamic demands and complex topologies.<n>We propose a novel Graph-mented Proximal Policy Optimization framework that leverages Graph Graph Networks (GNNs) for topology-aware feature extraction and masking action.
• Score: 0.9143713488498514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Distributed Training Architecture For Combinatorial Optimization [0.0]
  We propose a distributed graph neural network (GNN)-based training framework for optimization.<n>Experiments are conducted on both real large-scale social network datasets and synthetically generated high-complexity graphs.<n>Our framework outperforms state-of-the-art approaches in both solution quality and computational efficiency.
  arXiv  Detail & Related papers  (2025-11-12T12:22:10Z)
• Generative Diffusion Models for Resource Allocation in Wireless Networks [77.36145730415045]
  We train a policy to imitate an expert and generate new samples from the optimal distribution.<n>We achieve near-optimal performance through the sequential execution of the generated samples.<n>We present numerical results in a case study of power control.
  arXiv  Detail & Related papers  (2025-04-28T21:44:31Z)
• Scalable spectral representations for multi-agent reinforcement learning in network MDPs [13.782868855372774]
  A popular model for multi-agent control, Network Markov Decision Processes (MDPs) pose a significant challenge to efficient learning. We first derive scalable spectral local representations for network MDPs, which induces a network linear subspace for the local $Q$-function of each agent. We design a scalable algorithmic framework for continuous state-action network MDPs, and provide end-to-end guarantees for the convergence of our algorithm.
  arXiv  Detail & Related papers  (2024-10-22T17:45:45Z)
• DiffSG: A Generative Solver for Network Optimization with Diffusion Model [75.27274046562806]
  Generative diffusion models are popular in various cross-domain applications.<n>These models hold promise in tackling complex network optimization problems.<n>We propose a new framework for generative diffusion models called Diffusion Model-based Solution Generation.
  arXiv  Detail & Related papers  (2024-08-13T07:56:21Z)
• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning [5.1435595246496595]
  This paper introduces a novel generative AI (GAI)-driven, unified semi-supervised learning architecture for optimizing resource allocation and network slicing in O-RAN.<n>Termed Generative Semi-Supervised VAE-Contrastive Learning, our approach maximizes the weighted user equipment (UE) throughput and allocates physical resource blocks (PRBs) to enhance the quality of service for eMBB and URLLC services.
  arXiv  Detail & Related papers  (2024-01-16T22:23:27Z)
• A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration [12.914011030970814]
  Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) over commodity platforms to offer low-cost deployment. In this paper, a joint O-RAN/MEC orchestration using a Bayesian deep reinforcement learning (RL)-based framework is proposed.
  arXiv  Detail & Related papers  (2023-12-26T18:04:49Z)
• Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks [11.89735327420275]
  This work investigates the sum-rate of flexible networks without static time scheduling. Motivated by the recent success of Graph Networks Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net.
  arXiv  Detail & Related papers  (2023-01-20T12:49:21Z)
• Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images [28.560068780733342]
  A novel context aggregation network (CATNet) is proposed to improve the feature extraction process. The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid ( SCP), and hierarchical region of interest extractor (HRoIE)
  arXiv  Detail & Related papers  (2021-11-22T08:55:25Z)
• Deep Learning-based Resource Allocation For Device-to-Device Communication [66.74874646973593]
  We propose a framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication. A deep learning (DL) framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Our simulation results confirm that near-optimal performance can be attained with low time, which underlines the real-time capability of the proposed scheme.
  arXiv  Detail & Related papers  (2020-11-25T14:19:23Z)
• Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks [119.81868223344173]
  This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure. The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
  arXiv  Detail & Related papers  (2020-06-26T14:20:48Z)
• Deep Adaptive Inference Networks for Single Image Super-Resolution [72.7304455761067]
  Single image super-resolution (SISR) has witnessed tremendous progress in recent years owing to the deployment of deep convolutional neural networks (CNNs) In this paper, we take a step forward to address this issue by leveraging the adaptive inference networks for deep SISR (AdaDSR) Our AdaDSR involves an SISR model as backbone and a lightweight adapter module which takes image features and resource constraint as input and predicts a map of local network depth.
  arXiv  Detail & Related papers  (2020-04-08T10:08:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.