AI+ Telecommunications

Executive Summary

• In-depth exploration of AI applications in telecom, covering 5G, QoS/QoE, network optimization, predictive maintenance, cybersecurity, NLP, and IoT.

• Hands-on projects and capstone for real-world AI integration in telecom operations.

• Goal: Equip participants to leverage AI to drive innovation, optimize networks, and enhance service delivery.

Course Prerequisites

• Basic knowledge of telecom concepts and technologies.

• Programming skills (preferably Python).

• Familiarity with data analysis.

• Prior AI experience is optional.

Module 1: Introduction to AI in Telecom

• AI Fundamentals: ML, NLP, Computer Vision; definition, types, applications, and historical evolution.

• Telecom Applications: AI in network management, customer service, and operational efficiency.

• Impact of AI: Predictive maintenance, improved efficiency, reliability, and user experience.

• Case study & hands-on: ML algorithm for customer behavior classification.

Module 2: Data Engineering for Telecom AI

• Foundations: Types of telecom data (CDRs, network, customer, billing, location).

• Structured vs unstructured data processing.

• Data pipeline design, cleaning, and feature engineering for AI applications.

• Tools: SQL vs NoSQL, Apache Spark, Hadoop, TensorFlow.

• Case study & hands-on: SK Telecom Big Data Analytics & dashboard creation.

Module 3: AI for 5G Networks

• 5G fundamentals, architecture, and AI integration for network optimization.

• AI applications: Network slicing, edge computing, self-optimizing networks (SON).

• Hands-on: Simulate network slicing and resource allocation.

Module 4: AI in Network Optimization

• Predictive analytics, anomaly detection, load balancing, resource allocation.

• QoS and QoE monitoring for service quality and user experience.

• Hands-on: Traffic management simulations & predictive maintenance modeling.

Module 5: AI for Network Security

• Common threats: DDoS, phishing, insider threats.

• AI-based threat detection, IDS, fraud detection, and data encryption.

• Hands-on: Build a simple intrusion detection system.

• Case studies: Load balancing, traffic prediction, latency reduction, and configuration optimization.

Module 6: AI for Customer Experience

• Chatbots & virtual assistants, sentiment analysis, customer segmentation.

• QoS monitoring, churn prediction, recommendation systems, proactive support.

• Hands-on: Customer journey mapping, analyze AI-driven chatbots implementation.

Module 7: IoT Integration in Telecom

• IoT fundamentals, role of telecom networks, data analytics for connected devices.

• IoT security, connectivity management, scalability challenges, predictive maintenance.

• Hands-on: Predictive maintenance model for IoT devices.

• Case studies: Smart city applications, feedback loop integration.

Module 8: AI-Integrated NOCs

• Transition to predictive NOC operations using AI.

• Automating escalations, root cause analysis, closed-loop automation with SDN.

• Designing AI-ready network architectures & change management strategies.

• Case studies: AI assistants in NOCs, Nokia network optimization.

Module 9: Ethical Considerations & Bias in AI

• Ethical AI deployment, bias identification, transparency, accountability, and privacy compliance.

• AI regulations, workforce impacts, responsible deployment practices.

• Hands-on: Develop ethical AI project checklist.

• Case studies: Addressing biased algorithms in customer service applications.

Module 10: Capstone Project

• Apply knowledge from all modules to a real-world telecom AI project.

• Integrate AI solutions for network optimization, predictive maintenance, IoT, or customer experience.

✅ Program Objective:
Prepare telecom professionals to leverage AI across networks, operations, customer experience, IoT, and security, while ensuring ethical deployment, reliability, and innovation.