Page 304 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

or carbon intensity makes it both intelligent and sustainable. By integrating high-fidelity
simulation, real-time telemetry, and closed-loop automation, the platform ensures superior
network efficiency, cost savings, and measurable support for carbon neutrality goals all
within a scalable framework that can adapt to varied network environments and technologies.

Use Case Status: In development with prototype demonstrations in test environments

Partners

• Tata Elxsi - Lead implementer and system designer
• Tata Elxsi + Academic Partners - Develop and fine-tune inference models
• Academic Institutions – Support with AI/ML research findings and support model
explainability and transparency
• Telecom Operators – Provide operational data and deployment testbeds
• Energy Efficiency Consultants – Validate energy-saving outcomes and benchmarks

2�2 Benefits of Use Case
• This use case promotes the adoption of intelligent and sustainable infrastructure by
driving innovation in network operations and management. It supports the development
of modern industrial systems that are more efficient, adaptable, and forward-looking.
• The system also helps optimize the consumption of energy resources in
telecommunications operations, reducing waste and increasing the sustainability of core
infrastructure.
• Most importantly, the use case contributes to the reduction of carbon emissions
and overall energy consumption in a major industrial sector. This directly supports
environmental resilience and climate-conscious operational practices across large-scale
telecom deployments.

2�3 Future Work

To advance this AI-driven energy optimization platform from concept to wide-scale deployment,
the immediate next step involves scaling pilot testing within live operator environments. These
controlled field deployments will validate the accuracy of energy consumption predictions and
the impact of optimization strategies under real-world network conditions. Key metrics such
as power savings, QoS maintenance, and system responsiveness will be monitored closely to
refine the models.

As part of the system’s evolution, we plan to expand the AI capabilities to support fault prediction
and autonomous self-optimization. This includes enabling the platform to not only forecast
potential failures in network components but also to preemptively adjust configurations to
avoid service degradation, enhancing both energy efficiency and service reliability.

Another major focus area is the automation of response actions, reducing the manual workload
for network operators. By integrating closed-loop control mechanisms, the system will be able
to act on its own recommendations, such as reallocating bandwidth, switching off idle units,
or shifting tasks to greener windows based on defined policies and confidence thresholds.

Improving the fidelity of the simulation layer is also crucial. Future iterations will support a
broader range of configuration scenarios, including more complex interdependencies across
RAN, transport, and edge layers. Higher simulation accuracy will help operators plan network
upgrades and energy strategies with greater confidence.

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