Why AI Needs RF Engineers

Artificial Intelligence is rapidly becoming part of every conversation in telecommunications. From anomaly detection to predictive optimization and autonomous networks, AI is expected to transform how we operate mobile networks. But there’s one question I don’t hear often enough: Who teaches AI what a good engineering decision looks like?

Many people assume that once enough data is available, AI will naturally learn how to optimize a network. I don’t think it’s that simple. A radio network isn’t just a collection of KPIs. It’s a system built on trade-offs. Improving one metric can easily degrade another. Increasing handover margins may reduce unnecessary handovers but increase call drops. Shifting traffic away from a congested cell may improve throughput while creating new coverage challenges. Maximizing energy savings can negatively impact user experience if traffic predictions are inaccurate. None of these decisions has a universally correct answer. They depend on context. And context comes from engineering experience. This is why I believe AI doesn’t replace RF engineers. It amplifies them.

The algorithms may identify patterns faster than any human. They may process millions of measurements in seconds. They may even recommend better actions than traditional rule-based systems. But someone still has to define the objectives. Someone has to understand the trade-offs. Someone has to determine what “better” actually means. That knowledge doesn’t come from data alone. It comes from years of designing, optimizing, troubleshooting, and learning how real networks behave under real operating conditions. This is exactly what I’ve experienced throughout my own career. I started optimizing individual cells. Later, working with cSON, I learned how to transform optimization expertise into repeatable algorithms. Today, developing automation capabilities for SMO and rApps, I see the next evolution.

We’re no longer asking engineers to make every decision manually. We’re asking them to design the logic that allows AI to make good decisions consistently and responsibly. To me, that’s one of the most exciting changes happening in our industry. The future won’t belong to AI alone. And it won’t belong to engineers working without AI. It will belong to engineers who know how to translate years of RF expertise into intelligent, explainable, and trustworthy automation. Because before AI can optimize a network… It first needs someone who truly understands how that network works.

What’s your perspective? Do you see AI replacing RF expertise, or becoming the next tool that extends it?

#AI #AIRAN #RAN #RANOptimization #NetworkAutomation #SMO #rApps #OpenRAN #5G #Telecommunications #EngineeringLeadership