AI-Driven Transmission Networks: Optimization & Automation
Course Overview
This course explores the transformative role of Artificial Intelligence (AI) in modern transmission networks, equipping participants with the knowledge and skills to leverage AI for enhanced network performance, efficiency, and reliability. As AI adoption in telecommunications accelerates, professionals must understand how to apply machine learning, predictive analytics, and anomaly detection algorithms to optimize network operations.
Participants will delve into AI-driven network optimization, focusing on dynamic network resource allocation, traffic prediction, and capacity planning to enhance service delivery and reduce network congestion. The course also covers network orchestration and management, illustrating how AI enables automating link failure detection and recovery, fault detection and diagnosis, and signal quality enhancement to improve reliability.
A major emphasis is placed on deploying AI models for predictive maintenance, leveraging reinforcement learning to enable adaptive modulation and self-optimizing transmission networks. Additionally, the course examines AI’s role in network slicing, ensuring efficient resource allocation in multi-service environments.
Emerging technologies such as smart antennas and intelligent reflecting surfaces and federated learning for distributed networks will be explored, demonstrating how AI can enhance network performance while maintaining data privacy. Through hands-on labs and software demonstrations, participants will gain practical experience in applying AI techniques to real-world optical transmission challenges, reinforcing their ability to implement AI-driven automation strategies in transmission networks.
Target Audience
- Telecom Network Engineers and Planners
- Optical and Transport Network Specialists
- NOC and Operations Teams
- Telecom Regulators and Policy Makers
- AI and Data Science Professionals in Telecom
- Vendors and Solution Providers
Duration & Training Format
- Classroom: 5 days
- LIVE Virtual: 35 hours
*Note:
- A minimum of 8 or more participants is required for a Classroom session to commence.
- A minimum of 6 or more participants is required for a LIVE Virtual session to commence.
- LIVE Virtual courses can be conducted for 5 hours or 7 hours daily. Please note that the number of training days will be extended if you opt for 5 hours daily.
Upcoming Course Dates
7 – 11 Jul 2025 (Mon – Fri), GMT +08:00
If you are keen on attending the above scheduled course, please register your interest via our course enquiry form.
Course Objectives
At the end of this course, participants will be able to:
- Understand how AI and ML are revolutionizing telecommunications
- Learn to apply AI/ML for network optimization, fault management, and predictive maintenance
- Explore AI-driven solutions for optical transmission and DWDM technologies
- Analyze real-world case studies of AI applications in telecom
- Develop hands-on expertise in deploying AI models for network and transmission scenarios
- AI system for a specific telecom scenario, thus demonstrating a practical understanding of the course content
Course Outline
- Introduction to AI in Telecommunications
- Overview of AI/ML in Telecommunications
- Definition and Scope of AI in Telecom
- Evolution of AI Applications in the Telecom Industry
- The Role of AI in Next-Generation Networks (5G, 6G)
- Network Orchestration and Management
- AI in Edge Computing and IoT Integration
- AI and SDN (Software-Defined Networking)
- SDN Architecture Overview
- AI for Dynamic Network Resource Allocation
- AI in Optical and Wireless Transmission Technologies
- Adaptive Modulation using AI
Software Demonstration
- Open Network Automation Platform (ONAP)
- TensorFlow or PyTorch
- NS3 Simulation Tool
- AI for Network Optimization
- Traffic Prediction and Capacity Planning Using AI
- Time-series Forecasting for Traffic Trends
- Demand Forecasting with ML Models
- AI-Driven Network Slicing for 5G/6G
- Fundamentals of Network Slicing
- AI for Resource Allocation across Slices
- Intelligent Routing Algorithms and Congestion Management
- Adaptive Routing based on Real-time Data
- AI Techniques for Congestion Control
- Introduction to Reinforcement Learning for Network Optimization
- Basics of Reinforcement Learning
- Applications in Routing and Bandwidth Allocation
Software Demonstration
- Scikit-learn
- TensorFlow Agents
- Gurobi Optimizer
- AI in Optical Transmission
- AI Applications in DWDM and Optical Power Budgeting
- Power Budget Estimation using ML Models
- Dynamic Wavelength Assignment using AI
- Automating Link Failure Detection and Recovery Using AI
- AI Models for Fault Localization in Optical Networks
- Algorithms for Automated Failover Mechanisms
- Signal Quality Enhancement Through ML Techniques
- Noise Reduction and Distortion Correction
- AI for Signal Regeneration
- Overview of AI in Planning and Dimensioning Optical Networks
- Tools and Methodologies for Optical Network Design
- AI-enabled Tools for Network Panning
Software Demonstration
- OptSim
- TensorFlow
- MATLAB
- Fault Management and Predictive Maintenance
- Fault Detection and Diagnosis in Network Equipment
- AI Techniques for Fault Classification
- Real-Time Fault Detection Algorithms
- Predictive Analytics for Transmission Hardware
- Understanding Failure Patterns
- AI Models for Predicting Hardware Lifespan
- Real-Time Monitoring Using AI-Enabled Tools
- AI-driven Monitoring Dashboards
- Event Correlation using ML Techniques
- Introduction to Anomaly Detection Algorithms
- Supervised and Unsupervised Anomaly Detection
- AI for Real-time Alerts
Software Demonstration
- Elasticsearch with Kibana
- PyCaret
- AWS Lookout
- Advanced AI Applications and Future Trends
- AI in 6G: Smart Antennas and Intelligent Reflecting Surfaces
- AI for Beamforming and MIMO Optimization
- Role of AI in Intelligent Reflecting Surfaces
- AI-Driven Cybersecurity in Telecom Networks
- AI for Intrusion Detection Systems
- AI-enhanced DDoS Protection
- Exploring Federated Learning for Distributed Networks
- Concepts of Federated Learning in Telecom
- Applications in Privacy-preserving AI
- Ethical Considerations and Challenges in AI Adoption
- Addressing Biases in AI Models
- Regulatory Compliance and Transparency
Software Demonstration
- MATLAB
- Snort or Zeek with AI Plugins Detection
- TensorFlow Federated
Note: A Certificate of Completion will only be issued upon achieving at least 75% attendance for the course.
Pre-requisites
- Basic understanding of telecommunications networks (e.g., LTE, 5G)
- Familiarity with networking concepts and protocols (e.g., TCP/IP, MPLS)
- Basic programming knowledge (Python preferred)
- Foundational understanding of AI/ML concepts
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