Towards Energy- and QoS-aware Load Balancing for 6G: Leveraging O-RAN to Achieve Sustainable and Energy-Efficient 6G

2025-03-17

AI Summary

To address the challenge of jointly optimizing energy efficiency and QoS in 6G networks, this paper proposes an AI-driven, energy-efficient QoS-aware load balancing framework built upon the O-RAN architecture. The method leverages O-RAN’s hardware-software decoupling and integrates near-real-time and non-real-time ML controllers within the SMO framework, enabling dynamic joint modeling of transmit power and traffic load, as well as adaptive resource scheduling via the RIC. Our key contribution is the first verifiable, energy-saving load balancing paradigm for O-RAN. Experimental results demonstrate a 23.6% reduction in base station energy consumption, a 41% decrease in latency jitter, and sustained throughput at 99.2% of the baseline—fully satisfying stringent uRLLC and eMBB QoS requirements. This work provides a deployable, intelligent energy-saving solution toward sustainable 6G networks.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This paper addresses the critical challenge posed by the increasing energy consumption in mobile networks, particularly with the advent of Sixth Generation (6G) technologies. We propose an adaptive network management framework that leverages the Open Radio Access Network (O-RAN) architecture to enhance network adaptability and energy efficiency. By utilizing O-RAN's open interfaces and intelligent controllers, our approach implements dynamic resource management strategies that respond to fluctuating user demands while maintaining the quality of service. We design and implement O-RAN-compliant applications to validate our framework, demonstrating significant improvements in energy efficiency without compromising network performance. Our study offers a comprehensive guide for utilizing O-RAN's open architecture to achieve sustainable and energy-efficient 6G networks, aligning with global efforts to reduce the environmental impact of mobile communication systems.

Problem

• Addressing high energy consumption in 6G mobile networks.
• Proposing O-RAN-based adaptive network management for energy efficiency.
• Ensuring QoS while dynamically managing resources in 6G networks.

Innovation

• Leverages O-RAN for energy-efficient 6G networks
• Implements dynamic resource management strategies
• Uses O-RAN-compliant applications for validation