AI-enabled Wireless Communication

This course focuses on the integration of artificial intelligence (AI) with wireless communication systems, a key enabler of future 5G/6G networks. AI technologies such as machine learning (ML), deep learning (DL), and reinforcement learning (RL) are transforming the way wireless networks are optimized, operated, and managed.

• Introduction to AI-enabled Wireless Communication: Overview of AI and machine learning (ML) applications in wireless networks,
• Role of AI in Wireless Network Optimization: AI-powered optimization techniques for resource allocation, spectrum management, and traffic prediction,
• AI for Network Planning and Design: How AI can automate the network design process, including cell planning, network topology optimization, and coverage analysis,
• Machine Learning in the Physical Layer: Channel estimation, interference management, and signal detection using supervised, unsupervised, and reinforcement learning methods,
• AI for Mobility Management: Mobility prediction, handover decision making, and dynamic network adjustments based on user movement patterns,
• AI in Radio Resource Management: AI-based resource allocation methods such as beamforming, power control, and scheduling for better spectrum efficiency,
• Deep Learning and Cognitive Radio Networks: Deep learning techniques for spectrum sensing, cognitive radio management, and spectrum sharing,
• AI-enabled Security in Wireless Networks: Using AI for intrusion detection, anomaly detection, and predictive security measures to safeguard network integrity,
• Applications of AI in 5G/6G Networks: How AI enhances the capabilities of 5G/6G networks, such as network slicing, MIMO systems, and massive IoT deployments,
• Future Trends in AI-enabled Wireless Communication: The role of AI/ML in shaping the future of autonomous networks, edge computing, and smart cities,
• Real-World Case Studies: Practical examples of AI applications in commercial wireless networks, AI-enabled network management, and network performance improvement

• Electrical, Electronics, Telecommunications, and Computer Engineers
• Researchers and PhD/MSc students working in wireless communications, signal processing, or AI/ML
• Network Planning and Optimization Engineers
• R&D Professionals in Telecommunications, Defense, Aerospace, and Technology Companies
• Data Scientists and AI/ML Engineers interested in communication networks
• Technical Managers and Decision Makers seeking AI-driven network strategies
• Senior Undergraduate Students with background in communications or programming