# EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN

arXiv: https://arxiv.org/abs/2602.09206 · HTML: https://mlsanity.com/paper/2602.09206/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research](https://mlsanity.com/paper/2307.00319/) (2023)
- [Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs](https://mlsanity.com/paper/2012.03213/) (2020)
- [Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2208.09956/) (2022)
- [Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources](https://mlsanity.com/paper/2301.06166/) (2023)
- [Managing O-RAN Networks: xApp Development from Zero to Hero](https://mlsanity.com/paper/2407.09619/) (2024)
- [Kairos: Energy-Efficient Radio Unit Control for O-RAN via Advanced Sleep Modes](https://mlsanity.com/paper/2501.15853/) (2025)
- [Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning](https://mlsanity.com/paper/2509.14343/) (2025)
- [ScalO-RAN: Energy-aware Network Intelligence Scaling in Open RAN](https://mlsanity.com/paper/2312.05096/) (2023)
- [Design and Evaluation of Deep Reinforcement Learning for Energy Saving in Open RAN](https://mlsanity.com/paper/2410.14021/) (2024)
- [Energy Saving in 6G O-RAN Using DQN-based xApp](https://mlsanity.com/paper/2409.15098/) (2024)
- [Integrated Sensing and Communication in Distributed Antenna Networks](https://mlsanity.com/paper/2210.14880/) (2022)
- [Carrier Aggregation Enabled Integrated Sensing and Communication Signal Design and Processing](https://mlsanity.com/paper/2309.14008/) (2023)
- [xDiff: Online Diffusion Model for Collaborative Inter-Cell Interference Management in 5G O-RAN](https://mlsanity.com/paper/2508.15843/) (2025)

## Built on by (most cited first, 1 of 1)

- [Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation](https://mlsanity.com/paper/2605.10648/) (2026)

## Abstract (arXiv)

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. We have implemented EExAPP and deployed it on a real-world 5G O-RAN testbed with live traffic, commercial RU and smartphones. Extensive over-the-air experiments and ablation studies confirm that EExAPP significantly outperforms existing methods in reducing the energy consumption of RU while maintaining QoS. The source code is available at https://github.com/EExApp/EExApp.