Page 303 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

Impact of Network Digital Twin (NDT):

The Network Digital Twin (NDT) plays a central role by providing real-time synchronization
with the actual network, integrating telemetry, configurations, and faults across vendor-
agnostic sources. It incorporates AI modules for continuous simulation and feedback. The 4.3 - 5G
NDT comprises three key models: a physical topology model representing the network layout,
a behavioural model simulating node responses to varying conditions, and a policy model
outlining constraints and automation rules. This enables advanced inferences such as predictive
failure alerts, energy-saving opportunity mapping, pre/post-configuration impact analysis, and
identification of optimal node configurations to reduce energy use without degrading service.

Network Digital Twin (Data Twin) and AI Inference Relationship:
The Network Digital Twin (or Data Twin) works closely with AI inference engines by continuously
feeding them structured network data. Traffic load metrics help predict congestion and
guide dynamic resource scaling, while energy consumption logs reveal inefficiencies linked
to specific configurations. User behaviour profiles inform hotspot predictions and service
quality assessments. KPI trends like latency and throughput help monitor network health and
trigger configuration changes, and topological metadata enables AI to apply these decisions
intelligently such as offloading or rerouting without violating service-level agreements (SLAs).

AI-Driven Network Optimization:

AI-driven actions enable automated and intelligent network optimization. These include
dynamic resource scaling by activating or deactivating radio units and adjusting bandwidth
based on demand forecasts. Energy-efficient configuration updates are applied, such as
tuning power levels or beamforming strategies without compromising service quality. The
system also generates alerts and recommendations for predicted congestion or failures. AI
can propose topology adjustments like link rerouting to balance energy and performance.
Finally, post-action telemetry feeds into a feedback loop, continuously refining the AI models
for improved accuracy and adaptability.

Innovation and Uniqueness:

The solution stands out by offering dynamic intelligence through adaptive learning from real-
time and historical data, enabling accurate forecasting. It allows telecom operators to safely
experiment with “what-if” scenarios using the digital twin, fostering proactive decision-making
without impacting live operations. By combining deep telecom domain knowledge with AI-
driven predictions, it delivers contextual, interpretable energy optimization. Its cross-domain
applicability supports both fixed and mobile networks, ensuring broad relevance across the
telecom landscape.

Expected Impact:
This solution delivers significant energy savings across the telecom network without
compromising service quality, using AI-driven predictive models and a real-time digital twin
platform. Unlike traditional static or threshold-based energy-saving methods, it offers proactive
network scaling and adaptive configuration tailored to actual and forecasted demand. Its
ability to dynamically deactivate idle resources and shift workloads based on energy availability

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