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

TL;DR

EExApp is a deep reinforcement learning framework that optimizes energy efficiency in 5G O-RAN by jointly managing radio unit sleep scheduling and resource slicing, demonstrating significant energy savings while maintaining QoS in real-world tests.

Contribution

The paper introduces EExApp, a novel DRL-based xApp with a dual-actor-critic PPO architecture and transformer encoding, specifically designed for energy-efficient 5G network management.

Findings

EExApp reduces energy consumption of radio units significantly.

EExApp maintains quality-of-service during energy optimization.

Over-the-air tests validate superior performance over existing methods.

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. 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.