Page 301 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

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Item Details
The solution introduces an AI-native digital twin platform that mirrors
the live telecom network in real time, enabling intelligent forecasting of 4.3 - 5G
traffic and load conditions. By integrating real-time and historical data,
the platform simulates network behaviour and dynamically optimizes
configurations to reduce energy consumption without compromising
Key Aspects of Solu- service quality. AI models drive autonomous decision-making, adjusting
tion parameters such as power states, bandwidth, and antenna configurations,
while factoring in sustainability goals. The architecture is modular and
production-ready, leveraging tools like FastAPI, Kafka, and optional LEXI
platform integration for scalability. Designed with green AI principles,
the system ensures efficient energy use across both the network and its
own AI infrastructure.

Technology Energy Optimization, Network Intelligence, Digital Twin, Predictive
Keywords Modelling, AI in Telecom

Private (Collected from operational telecom network environments) and
Data Availability
Synthetic rule-based data for Training
Metadata (Type of Time-series data, network telemetry, performance logs, configuration
Data) data, KPIs

AI models are trained on historical network behaviour and performance
Model Training and data to predict usage trends, detect anomalies, and recommend ener-
Fine-Tuning
gy-saving actions

Testbeds or Pilot Early-stage pilots in simulated telecom environments with representative
Deployments traffic and infrastructure

Code repositories NA

2 Use Case Description

2�1 Description

In modern telco infrastructure, energy consumption across network elements—from RAN to
transport to core - is one of the most critical operational challenges. Despite the integration of
more efficient hardware and renewable energy sources, the dynamic and distributed nature
of networks results in inefficiencies that are difficult to predict and manage. With 5G, edge
computing, and virtualization increasing the complexity, real-time visibility and control over
energy usage has become both a necessity and a challenge.

This use case introduces an AI-driven digital twin platform that simulates, monitors, and
optimizes energy consumption across the entire telco network. By creating virtual replicas
of network assets both passive (cooling, power supplies) and active (base stations, routers,
compute nodes) and integrating real-time telemetry from these components, we can build
a learning system that predicts energy demand, identifies inefficiencies, and simulates
optimization strategies under various network load conditions.

The core of this solution is a set of modular AI/ML models that continuously learn from historical
and live data streams to simulate power usage and recommend optimizations. The simulation
layer can analyze load patterns, adjust resource allocations, and propose scheduling or

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