Bharti Airtel - RAN energy saving with AI and ML Case Study | GSMA

Bharti Airtel – RAN energy saving with AI and ML

Asia Pacific | Climate Action | Energy efficiency
Bharti Airtel – RAN energy saving with AI and ML

Airtel aims to achieve Net Zero by 2050 through energy-efficient initiatives such as setting up green data centres, adopting advanced technologies, and using renewable energy extensively. Energy efficiency is a core aspect of Airtel’s commitment to environmental conservation, decarbonising operations, and meeting stakeholder expectations. It also enhances the company’s reputation and ensures reliable services for customers. The expansion of 5G, with its low latency, high capacity, and fast connectivity, promises to be more energy-efficient than its predecessor. 

Energy costs account for about 29% of annual Network OPEX, with approximately 80% attributed to RAN network elements. Therefore, improving energy efficiency in the RAN is crucial, as even small improvements can lead to significant reductions in energy OPEX. Airtel has partnered with solution providers to develop an Artificial Intelligence and Machine Learning (AI/ML) energy-saving solution aimed at conserving energy by dynamically extending cell sleep windows during low-traffic periods. 

A flowchart titled "Solution Framework" shows 4G/5G signals going to BBU and EMS, sending data and commands to an "AI/ML tool for Energy Savings." A legend explains icons for capacity, coverage, BBU (BaseBand Unit), and EMS (Element Management System).

This solution uses AI and machine learning algorithms to analyse cell-level traffic patterns and identify optimal time windows for implementing energy-saving measures dynamically, while maintaining user experience. It is a vendor-agnostic implementation compatible with both 4G and 5G technologies and represents a significant step toward achieving the Net-Zero sustainability goal. Initially targeting approximately ₹100 Crore in annual OPEX savings across around 200,000 sites, the solution also has the potential to unlock up to 8-10% in annual energy savings in the future.  

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