CCEN 747 Native AI for Networks
This course explores the different AI technologies that can be used as an essential tool to design, configure, and operate telecom networks. The course is divided into six comprehensive modules. The course begins with an introduction to the wide range of AI technologies and their specific relevance and application in Telecom networks. After that, the discussion will move to the exploration of machine-learning techniques suitable for solving network-related problems. This includes the application of supervised, unsupervised, and deep learning models specifically designed to optimize network traffic, predict network load, detect anomalies in real time, etc. The third part of this course will focus on how to implement reinforcement learning algorithms to manage and optimize resource allocation, power control, and bandwidth in dynamic network environments, among other Telecom use cases. The fourth part of this course will be dedicated to studying the principles of generative AI and how these technologies can be used to create new data, simulate network scenarios, and generate solutions to complex network problems, enhancing both network design and operation. The fifth part of the course will be focused on the capabilities of large language models and how these AI systems can be leveraged to automate network management tasks. The last part of the course will introduce multi-agent systems, highlighting how these can be leveraged to coordinate complex tasks among multiple AI agents, optimizing overall network efficiency and reliability, especially in large-scale deployments.
The course will be conducted in a seminar-oriented format. Through the nature of this course, which involves theoretical studies, hands-on projects, and case studies, students will learn to design AI-driven models for real-world telecom network applications.