HCLTech continued as a key partner in Telecom Infra Project (TIP) for Accelerating RAN Intelligence across Network Ecosystems (ARIANE) extension project with core aim on energy management in Open RAN networks. The initiative the growing need to reduce RAN energy consumption while maintaining stable network performance in open, virtualized and multi-vendor O-RAN environments. As rising energy costs and sustainability targets become critical priorities for telecom operators, ARIANE-2 demonstrated how intelligent automation can optimize energy usage safely without compromising service quality or user experience.

As part of the ARIANE program, our team designed, developed and validated Energy Saving rApp capable of operating across simulated, Over-the-Air (OTA) and hybrid network environments. The solution leveraged AI-based energy efficient algorithms aligned with O-RAN principles to dynamically manage network resources based on traffic demand and operational conditions.

The rApp was validated across multiple infrastructure configurations to ensure scalability, reliability and repeatability, providing operators with confidence in deploying energy-aware automation in real-world O-RAN networks.

The primary objective was to establish a safe, automated and scalable approach to RAN energy optimization across O-RAN environments. Specifically, the program aimed to:

At HCLTech, we developed an Energy Saving rApp based on a flexible optimization framework with three complementary variants, designed to address diverse deployment scenarios.

Variant 1 leveraged a Reinforcement Learning (RL)–based model to autonomously learn and apply optimal energy-saving actions in complex Massive MIMO scenarios. This variant was targeted at large-scale simulated environments.

Variants 2 and 3 implemented deterministic, rule-based energy control logic suitable for OTA and mimic environments. These approaches offered predictable, explainable and easily deployable optimization behaviour, making them well-suited for validation, controlled rollouts and real-world deployment scenarios.

The ES rApp dynamically adjusted network parameters such as mMIMO antenna configurations and component activation during low-traffic periods. The solution was validated through extensive testing in collaboration with ecosystem partners across simulated environments and OTA indoor deployments, ensuring consistency, reliability and practical deployability.

ARIANE-2 delivered measurable energy savings while maintaining stable network performance across all test environments.

Energy Optimization Results:

Across all variants, the rApp maintained stable KPIs with no impact on QoS, confirming the safety and reliability of the optimization decisions.

Future work will extend ARIANE-2 beyond indoor lab environments to live field trials with communication service providers. We plan to expand testing across larger, real-world RAN deployments, refine optimization models using broader KPI datasets to further improve efficiency.

These next steps will strengthen HCLTech’s role in intelligent, sustainable RAN automation and support operators in reducing OPEX, lowering carbon footprint and accelerating automation adoption across open and virtualized RAN ecosystems.