AI is playing an increasingly transformative role in 5G and telecommunications networks, enabling advanced capabilities such as predictive optimization and real-time traffic management. Autonomous fault detection and anomaly detection features enhance network maintenance by identifying issues early, leading to more robust performance and reducing the need for human intervention. While these advancements improve performance and reliability, they also introduce significant energy demands, underscoring the importance of AI-driven solutions to enhance the energy efficiency of 5G networks and support broader SDGs.
In his presentation “How can AI enable green operation of cloud network infrastructure” at the 2024 AI for Good Global Summit, Guirong Wang, General Manager of the Science and Technology Innovation Department at China Telecom, shared several AI-enabled tools and initiatives that are setting new industry standards for green infrastructure and network operations. Through the 5G Co-construction and Sharing initiative, China Telecom and China Unicom collaborated to build the world’s largest and fastest 5G network, leading to annual electricity savings exceeding 20 billion kilowatt hours. This demonstrates how co-construction and sharing collaborations can enhance network efficiency, reduce costs and accelerate 5G deployment.
As Summer Chen, Vice President of ZTE Corporation, discusses in the AI for Good article “AI’s Role in Digital Transformation,” AI and 5G technologies enable city management systems to collect vast amounts of data, such as gas, water and infrastructure usage to enhance urban safety and performance. Network slicing allows smart cities to create dedicated virtual networks on shared physical infrastructure, enabling tailored connectivity based on real-time demand. This capability supports the dynamic allocation of bandwidth among emergency services, public transport and IoT devices, helping to reduce energy waste and optimize operational efficiency.
ITU’s Focus Group on Autonomous Networks has played a critical role in identifying critical gaps in the standardization of autonomous networks, while also exploring evolution in future networks, real-time responsive experimentation, dynamic adaptation to future environments, technologies and use cases. Key outcomes include its work on concepts such as zero management networks, which provides a fully autonomous network management solution with human oversight, and network orchestration, which automates complex, multi-step processes across diverse network domains and IT systems.
This case study is an excerpt from the AI for Good flagship report produced by UNU-CPR, Unlocking AI's Potential to Serve Humanity.
