What Does AI-Native RAN Actually Mean?

(A quick conversation between 𝙈𝙚 and 𝙏𝙚𝙘𝙝 𝙀𝙭𝙥𝙡𝙤𝙧𝙚𝙧 📶)

𝙏𝙚𝙘𝙝 𝙀𝙭𝙥𝙡𝙤𝙧𝙚𝙧 📶: Hey Mohamed, everyone’s throwing around the term AI-Native RAN lately.
Is this just hype, or is something fundamentally changing in the radio network?

𝙈𝙚 (𝙈𝙤𝙝𝙖𝙢𝙚𝙙):
It’s a fair question — and I’ve seen both the buzz and the reality.
Let’s be honest — a lot of people say “AI” when they really mean automation scripts.
But AI-Native RAN is something more strategic.
It’s about designing the RAN with AI in the loop from the start — not just adding AI later.

𝙏𝙚𝙘𝙝 𝙀𝙭𝙥𝙡𝙤𝙧𝙚𝙧 📶: Okay, but practically speaking… how is AI used in RAN today?

𝙈𝙚 (𝙈𝙤𝙝𝙖𝙢𝙚𝙙):
Let me give you a real example.
A few months ago, we were troubleshooting handover failures in a dense urban cluster.
The usual way?
🧑‍💻 Manual drive tests, spreadsheet exports, hours of engineering.
But with the right data pipeline, AI can learn beam-level KPIs, HO success rates, and user mobility traces.
Then it predicts failures and recommends tuning — even in real time, through the RIC.

That’s not automation — that’s adaptive intelligence.

𝙏𝙚𝙘𝙝 𝙀𝙭𝙥𝙡𝙤𝙧𝙚𝙧 📶: So is this what O-RAN meant by near-RT RIC?

𝙈𝙚 (𝙈𝙤𝙝𝙖𝙢𝙚𝙙):
Exactly.

In the O-RAN architecture, intelligence is layered:

• 🔁 Non-RT RIC (cloud/core): trains AI models, handles long-term learning
• ⚡ Near-RT RIC (CU/edge): applies policies, takes fast decisions (<1s)
• ⚙️ xApps & rApps: plug-in apps for functions like beam optimization, power control, load balancing

The big shift?
📦 Operators can build their own AI logic — no more full black-box from vendors.

𝙏𝙚𝙘𝙝 𝙀𝙭𝙥𝙡𝙤𝙧𝙚𝙧 📶: So is AI-Native RAN just a software upgrade?

𝙈𝙚 (𝙈𝙤𝙝𝙖𝙢𝙚𝙙):
Not at all.

To be truly AI-native, the network must be built with AI hooks and closed loops:

• 🔍 Granular observability — real-time access to PHY/MAC/RLC data
• 🧠 Model integration points — APIs to inject inferences
• ♻️ Closed-loop design — monitor, act, verify, repeat

Even the radios might need upgrades — to support beam KPIs per TRP, predictive scheduling, or uplink analytics for FWA or RedCap use cases.

𝙏𝙚𝙘𝙝 𝙀𝙭𝙥𝙡𝙤𝙧𝙚𝙧 📶: Have you seen AI-Native RAN in the field?

𝙈𝙚 (𝙈𝙤𝙝𝙖𝙢𝙚𝙙):
Yes — and here are real examples:

• 🇰🇷 One MNO in Asia used xApps to cut energy usage by 18% in dense clusters.
• 🇮🇳 Another used ML to predict crowd hotspots, pre-loading beam templates before festivals.
• 🧠 I’ve personally worked with teams on handover prediction & uplink scheduling, using AI-inferred device trajectories.

Before: days of manual tuning.
Now? It learns silently in the background.

𝙏𝙚𝙘𝙝 𝙀𝙭𝙥𝙡𝙤𝙧𝙚𝙧 📶: So what should operators do to get ready?

𝙈𝙚 (𝙈𝙤𝙝𝙖𝙢𝙚𝙙):

Here’s my roadmap:
✅ Start with clean, contextualized RAN data
✅ Deploy a non-RT RIC platform
✅ Build internal ML Ops for telco
✅ Pilot 1–2 xApps — like energy saving or beam selection
And most importantly — move away from closed RANs.
The future is open, intelligent, AI-native.

🎓 Check  our online recorded course to learn more about the technology.