Welcome to Autonomous Network Operations: AI-Driven Optimization & Automation

Welcome to Autonomous Network Operations: AI-Driven Optimization & Automation, a course developed by the Telecommunications Education Center (TEC) to provide a practical, engineering-focused guide to applying artificial intelligence across the radio access network and mobile core.

A single 4G/5G network produces millions of performance counters, alarms, and log entries every hour across thousands of cells. No team can review all of it, and the static rules behind traditional Self-Organizing Networks cannot anticipate every combination of traffic, interference, and equipment condition. The result is familiar to anyone who has worked a shift in a network operations center: problems are found after customers feel them, and fixes are applied by hand, one cell at a time.

This course is about what replaces that approach. AIOps links data collection, analysis, decision-making, and action into a continuous loop, where the outcome of each action feeds back in as new data. You will work through how that loop is built and operated in a real mobile network, from the data pipelines that feed it to the policies that govern what it is allowed to do.

This course applies specifically to wireless 4G and 5G mobile networks, including the RAN and 5G core, rather than wireline or fixed networks.

No prior background in artificial intelligence is required.

Who This Course Is For

This course is intended for:

• RAN engineers, RF engineers, and performance optimization specialists
• Network operations center staff and engineering managers responsible for 4G/5G operations
• Network planning and capacity teams
• Professionals who have completed AI Foundations and Strategic Impact in Telecom and want to apply those concepts to network operations

The emphasis is on engineering judgment and operational decision-making rather than model development or programming. Working knowledge of mobile network fundamentals is assumed; experience with AI is not.

What You Will Learn

By the end of this course, you will be able to:

• Explain what AIOps is and how it advances beyond rule-based Self-Organizing Networks
• Describe the Observe, Orient, Decide, and Act closed-loop cycle and map it onto real network elements including gNBs, CU/DU splits, and 5G core functions
• Identify the main categories of telecom data and design pipelines that prepare them for AI models
• Differentiate the near-real-time and non-real-time RAN Intelligent Controller and explain the role of the A1 and E2 interfaces in O-RAN architectures
• Manage the AIOps model lifecycle, including deployment at the network edge versus the cloud, and recognize model drift before it affects service
• Apply AI to predictive maintenance, remaining useful life estimation, anomaly detection, and root cause analysis
• Evaluate where closed-loop automation should act autonomously and where policy enforcement and human oversight are required
• Describe AI approaches to capacity, traffic, spectrum, and energy optimization in the RAN
• Assess network autonomy using the TM Forum maturity model and explain the 6G vision of cognitive, self-evolving networks

Three lessons place you in the network operations center during a live incident, where you make the decisions an engineer on shift would make and see what follows from each choice.

For best performance:

• Use a modern browser such as Chrome, Edge, or Firefox
• Complete the course on a desktop or laptop when possible

Time Commitment

The estimated time to complete this course is approximately 90 minutes, depending on your pace and level of review.

Completion and Certificate

Upon successful completion, you will receive a certificate of completion.

Certificates are generated automatically once all course requirements are met.

Technical Support

For questions related to course access or technical issues, contact:

tec@wia.org