QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2512.19696v1
• Date: Fri, 21 Nov 2025 16:10:28 GMT
• Title: QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning
• Authors: Sebastian Racedo, Brigitte Jaumard, Oscar Delgado, Meysam Masoudi,
• Abstract summary: Open Radio Access Network (O RAN) disaggregates conventional RAN into interoperable components, enabling flexible resource allocation, energy savings, and agile architectural design.<n>We address the limitation by relaxing the fixed mapping and performing dynamic service function chain (SFC) provisioning with on the fly O CU selection.<n>We formulate the problem as a Markov decision process and solve it using GRLDyP, i.e., a graph neural network (GNN) assisted deep reinforcement learning (DRL).<n>The proposed agent jointly selects routes and the O-CU location (from candidate sites) for each incoming service flow to minimize network energy
• Score: 1.0732935873226022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open Radio Access Network (O RAN) disaggregates conventional RAN into interoperable components, enabling flexible resource allocation, energy savings, and agile architectural design. In legacy deployments, the binding between logical functions and physical locations is static, which leads to inefficiencies under time varying traffic and resource conditions. We address this limitation by relaxing the fixed mapping and performing dynamic service function chain (SFC) provisioning with on the fly O CU selection. We formulate the problem as a Markov decision process and solve it using GRLDyP, i.e., a graph neural network (GNN) assisted deep reinforcement learning (DRL). The proposed agent jointly selects routes and the O-CU location (from candidate sites) for each incoming service flow to minimize network energy consumption while satisfying quality of service (QoS) constraints. The GNN encodes the instantaneous network topology and resource utilization (e.g., CPU and bandwidth), and the DRL policy learns to balance grade of service, latency, and energy. We perform the evaluation of GRLDyP on a data set with 24-hour traffic traces from the city of Montreal, showing that dynamic O CU selection and routing significantly reduce energy consumption compared to a static mapping baseline, without violating QoS. The results highlight DRL based SFC provisioning as a practical control primitive for energy-aware, resource-adaptive O-RAN deployments.

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