Maximising Energy Efficiency in Large-Scale Open RAN: Hybrid xApps and Digital Twin Integration

2025-09-12

AI Summary

To address the low energy efficiency, high power consumption, and challenging QoS assurance in Open RAN for 5G and beyond networks, this paper proposes a hybrid xApp integrating heuristic optimization with unsupervised machine learning, co-deployed with the TeraVM AI-RSG digital twin platform to enable dynamic, coordinated sleep scheduling for open Radio Units (RUs). The key innovation lies in the first-of-its-kind deep coupling of cross-layer energy-saving control, lightweight xApp-based intelligent decision-making, and high-fidelity digital twin simulation—achieving adaptive RAN energy efficiency management while strictly satisfying latency and reliability constraints. Large-scale simulations demonstrate that the proposed solution reduces system energy consumption by approximately 13%, confirming its practical deployability and substantial energy-saving benefits.

Technology Category

• Planning, Routing, and Scheduling: Applications
• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

The growing demand for high-speed, ultra-reliable, and low-latency communications in 5G and beyond networks has significantly driven up power consumption, particularly within the Radio Access Network (RAN). This surge in energy demand poses critical operational and sustainability challenges for mobile network operators, necessitating innovative solutions that enhance energy efficiency without compromising Quality of Service (QoS). Open Radio Access Network (O-RAN), spearheaded by the O-RAN Alliance, offers disaggregated, programmable, and intelligent architectures, promoting flexibility, interoperability, and cost-effectiveness. However, this disaggregated approach adds complexity, particularly in managing power consumption across diverse network components such as Open Radio Units (RUs). In this paper, we propose a hybrid xApp leveraging heuristic methods and unsupervised machine learning, integrated with digital twin technology through the TeraVM AI RAN Scenario Generator (AI-RSG). This approach dynamically manages RU sleep modes to effectively reduce energy consumption. Our experimental evaluation in a realistic, large-scale emulated Open RAN scenario demonstrates that the hybrid xApp achieves approximately 13% energy savings, highlighting its practicality and significant potential for real-world deployments without compromising user QoS.

Problem

• Reducing high power consumption in 5G RAN networks
• Managing energy efficiency without compromising QoS
• Optimizing Open RAN sleep modes through hybrid AI

Innovation

• Hybrid xApp combining heuristics and machine learning
• Digital twin integration via TeraVM AI-RSG
• Dynamic RU sleep mode management for energy savings