AI-NATIVE RAN: FROM SON TO SELF-EVOLVING NETWORKS

For years, SON has been one of the most powerful tools to optimize RAN performance. I’ve seen it evolve from rule-based automation to more sophisticated closed-loop mechanisms across multi-vendor environments. And yes, it delivered real value. But with 3GPP Release 18, we are entering a different era. Not an evolution… a shift. The conversation is no longer about automating predefined actions. It’s about enabling networks to learn, adapt, and evolve. In my experience working with cSON solutions, one thing has always been clear:

• Traditional SON relies heavily on predefined thresholds and engineered logic, which limits its adaptability to highly dynamic environments.
• Optimization outcomes often depend on how well scenarios were anticipated during design, not on real-time understanding of network behavior.
• Multi-vendor integration introduces additional complexity, making consistent automation harder to scale.

Now, AI-native RAN is changing the rules:

• AI models can identify patterns that were never explicitly defined, enabling a deeper level of network awareness.
• Decision-making can shift from reactive optimization to predictive and proactive actions.
• Continuous learning loops allow the network to improve over time without constant human tuning.

But here is the real question:

Are we ready to trust the network to make its own decisions? Because moving to AI-native RAN is not just a technology upgrade. It is a mindset change.

From control → to trust From configuration → to learning From optimization → to autonomy

And maybe the most important shift of all: From engineers defining the network… to engineers training the network. Release 18 is not the end of SON.

It is the beginning of something bigger. What’s your view? Are we evolving SON… or replacing it?

#5G #ORAN #AIinTelecom #RANOptimization #SMO #FutureOfRAN #TelecomInnovation