6G Vision: Machine Learning, Intelligent Surfaces, and Optical Networks Syllabus

Overview: This course provides a comprehensive exploration of 6G wireless technologies, integrating machine learning, intelligent surfaces, and optical networks. Over 9 weeks, learners engage with theoretical foundations and emerging applications shaping next-generation connectivity. Expect a time commitment of approximately 4–6 hours per week, combining video lectures, readings, and assessments. The curriculum builds progressively from core principles to advanced integration scenarios, preparing engineers and researchers for future telecom innovation.

Module 1: Introduction to 6G Vision and Key Enablers

Estimated time: 8 hours

• Historical evolution from 1G to 6G
• Key performance indicators of 6G networks
• Societal and industrial drivers for 6G adoption
• 6G use cases: holographic communications and pervasive sensing

Module 2: Machine Learning for 6G Networks

Estimated time: 12 hours

• ML-driven network slicing and automation
• Predictive maintenance and traffic modeling in 6G
• Federated learning for privacy-preserving systems
• AI-native optimization of radio resources

Module 3: Intelligent and Reconfigurable Surfaces

Estimated time: 8 hours

• Physics and design of intelligent reflecting surfaces
• Channel modeling with RIS integration
• Energy efficiency and coverage extension strategies
• Reconfigurable intelligent surfaces in urban environments

Module 4: Optical Networks and Convergence with 6G

Estimated time: 8 hours

• Role of fiber optics in 6G backhaul and fronthaul
• Coherent optical transmission technologies
• Integrated radio-optical network architectures

Module 5: 6G System Integration and Real-World Applications

Estimated time: 8 hours

• Convergence of AI, RIS, and optical networks in 6G
• Real-world deployment challenges and scalability
• Future applications: smart cities, immersive AR/VR, and sensing networks

Module 6: Final Project

Estimated time: 10 hours

• Design a conceptual 6G-enabled smart infrastructure
• Integrate machine learning, RIS, and optical networking components
• Submit a technical brief with performance analysis and deployment considerations

Prerequisites

• Familiarity with wireless communication fundamentals
• Basic understanding of networking concepts
• Background in signal processing or electromagnetics recommended

What You'll Be Able to Do After

• Explain the core principles and vision behind 6G networks
• Analyze how machine learning enhances 6G network optimization
• Evaluate the role of reconfigurable intelligent surfaces in coverage improvement
• Describe the integration of optical networks in 6G backhaul/fronthaul
• Design integrated 6G system concepts for future applications