Telco Automation & Intelligence.

Modern Mobile Communication Systems are growing in complexity due to the proliferation of 6G use cases, network slicing, and GenAI/AI. Operators must simultaneously meet diverse Service Level Agreements (SLAs) across slices (e.g. enhanced mobile broadband vs. ultra-reliable low-latency), optimize spectrum and power, and handle dynamic interference and traffic variations. Traditional approaches to network management rely on static configurations and specialized algorithms for each task (e.g. separate functions for handover optimization, slicing, interference mitigation). Even emerging network architectures (O-RAN, AI-RAN) use multiple specialized applications (e.g. xApp) for specific optimization tasks, leading to siloed solutions that lack a unified intelligence. This fragmented approach struggles to coordinate conflicting objectives (for instance, when different network slices compete for shared resources) and cannot easily adapt to unforeseen scenarios. 

In this course, we will study the next generation mobile network from the architectural perspective and its evolution from legacy networks toward autonomous networks with built-in network automation and intelligence. In particular, we will review how telecommunication networks are evolved with the emergence of GenAI/AI (e.g. DeepSeek and OpenAI) and cloud-native computing (e.g. Kubernetes) to significantly increase the efficacy and agility of network management and operations allowing operators to dynamically optimize networks based on the high-level objectives (e.g. reduce energy consumption). In this regard, we will design, build and deploy autonomous agents powered by Large Language Models (LLMs) for real-time decision-making on a live cloud-native 5G network. We will also discuss how future autonomous networks can self-synthesize themselves to compose new capabilities and knowledge and as a result evolve over time and space. Finally, we review real world scenarios in both public and private telecommunication networks and how they could benefit from autonomous networking. The course is a mix of theory and lab sessions. 

Learning outcomes:

• Be able to flexibly design a 5G network architecture.
• Be able to automate 5G network deployment.  
• Be able to develop 5G network intelligence in a form of O-RAN xApp/rApps.