Page 60 - AI for Good-Innovate for Impact

AI for Good-Innovate for Impact

10�2� Use case description

10�2�1� Description

Introduction: Conventional network operations in the telecommunications industry are fraught
with complexity, inefficiency, human error, and security vulnerabilities. They require significant
expertise and are labor—and time-intensive, making them prone to errors and vulnerable to
security attacks. As networks evolve towards 6G and beyond, there is a pressing need for more
efficient, reliable, and secure network orchestration and operations.

Solution Overview: This use case proposes an advanced solution that leverages cutting-edge
technologies such as Natural Language Processing (NLP), Machine Learning (ML), Artificial
Intelligence (AI), Digital Twins, and Extended Reality (XR) to revolutionize network operations
and orchestration.

Autonomous Network Operations: The solution simplifies the deployment and configuration
of network functions (NFs) by utilizing NLP and ML/AI. Operators can input commands in natural
language, transcending language barriers and reducing the need for specialized technical
knowledge.

Digital Twin for Resource Management: A digital twin of the Kubernetes cluster projects
resource utilization, helping operators manage infrastructure efficiently. This reduces the need
for overprovisioning, thereby saving costs and minimizing carbon emissions.

Extended Reality (XR) for Observability: XR provides responsive and interactive observability,
allowing operators to visualize and interact with network configurations and projected traffic.
This enhances decision-making and troubleshooting capabilities.

AI-Driven Security Measures: The solution continuously monitors network traffic for anomalies
using ML algorithms. AI modules enforce appropriate security policies in real time, and
simulated attacks on the digital twin help identify and fix vulnerabilities.

UN Goals:
• The solution aligns with UN SDG 11: Sustainable Cities and Communities by addressing
several key areas:
• Efficient Resource Utilization: AI-driven predictive analytics optimize network resource
usage, reducing unnecessary overprovisioning and operational costs.
• Environmental Impact: Improved efficiency in resource utilization directly translates to
lower carbon emissions, supporting environmental sustainability.
• Operational Resilience: Enhancing network security and efficiency ensures the reliable
operation of critical communications infrastructure, which is vital for sustainable urban
environments.

10�2�2� Future work

In the next phases, the focus will be on developing a robust proof of concept that integrates
NLP, ML/AI, Digital Twin, and XR technologies to demonstrate the feasibility of the solution.
Simultaneously, efforts will be made to contribute to industry standards, collaborating with
standardization bodies like the International Telecommunication Union (ITU). Pilot deployments
in real-world environments will follow, allowing testing of scalability, reliability, and performance.
Subsequently, outcomes will be documented through case studies, fostering partnerships

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