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[2602.09206] EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN
Summary
The paper presents EExApp, a GNN-based reinforcement learning application designed to optimize energy consumption in 5G Open Radio Access Networks (O-RAN) while ensuring quality of service (QoS).
Why It Matters
As the deployment of 5G networks increases, so does their energy consumption, which poses environmental and economic challenges. EExApp addresses these issues by utilizing advanced machine learning techniques to enhance energy efficiency without compromising service quality, making it relevant for sustainable technology development.
Key Takeaways
- EExApp optimizes energy consumption in 5G O-RAN through a dual-actor-dual-critic reinforcement learning model.
- The application effectively balances energy efficiency and QoS using a bipartite Graph Attention Network.
- Real-world testing shows EExApp significantly reduces energy use while maintaining service quality compared to existing methods.
- The source code for EExApp is publicly available, promoting transparency and further research.
- The study highlights the importance of integrating AI solutions in telecommunications for sustainable development.
Electrical Engineering and Systems Science > Systems and Control arXiv:2602.09206 (eess) [Submitted on 9 Feb 2026 (v1), last revised 24 Feb 2026 (this version, v2)] Title:EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN Authors:Jie Lu, Peihao Yan, Huacheng Zeng View a PDF of the paper titled EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN, by Jie Lu and 2 other authors View PDF HTML (experimental) Abstract:With over 3.5 million 5G base stations deployed globally, their collective energy consumption (projected to exceed 131 TWh annually) raises significant concerns over both operational costs and environmental impacts. In this paper, we present EExAPP, a deep reinforcement learning (DRL)-based xApp for 5G Open Radio Access Network (O-RAN) that jointly optimizes radio unit (RU) sleep scheduling and distributed unit (DU) resource slicing. EExAPP uses a dual-actor-dual-critic Proximal Policy Optimization (PPO) architecture, with dedicated actor-critic pairs targeting energy efficiency and quality-of-service (QoS) compliance. A transformer-based encoder enables scalable handling of variable user equipment (UE) populations by encoding all-UE observations into fixed-dimensional representations. To coordinate the two optimization objectives, a bipartite Graph Attention Network (GAT) is used to modulate actor updates based on both critic outputs, enabling adaptive trade-offs between power savings and QoS...
