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Energy optimization for RAN

URN C25.0.783
Topics AI (Artificial Intelligence), Autonomous networks, Ecosystem management

Driving sustainable 5G with dynamic energy optimization for cost-effective and eco-friendly RAN operations

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As telecom networks evolve toward 5G and beyond, ensuring high-quality, reliable service at reduced operational costs is increasingly challenging. 5G networks consume significantly more energy than previous generations. Radio Access Networks (RAN) consume significant energy, creating environmental and financial pressures on telecom operators. This project introduces AI-driven dynamic energy optimization for RAN sites, adjusting power usage based on real-time traffic and network demand, while desired service quality is maintained. By reducing energy consumption during off-peak hours and optimizing for peak demand, CSPs can lower costs and minimize their carbon footprint. By energy optimization, this project enhances network sustainability, making it a true win for both CSPs and the environment. Our solution aligns with TM Forum’s Green Network standards, enabling CSPs to achieve energy efficiency without compromising performance. This initiative supports sustainable business practices while reducing operational expenses, helping CSPs to meet regulatory requirements and environmental goals.

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Advanced SOLUTIONS, S.A. logo
Snowflake Inc. logo
Sutherland Global Services logo
T-Mobile USA logo
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