How AI Is Reshaping the Radio Access Network

For years, AI has sat around the edges of the mobile network—supporting planning, troubleshooting, automation, and assurance. Now, AI is moving closer to the heart of the network itself: the radio access network (RAN), where coverage, capacity, latency, energy use, and subscriber experience are shaped in real time.

That shift matters.

The RAN is one of the most dynamic, demanding, and expensive parts of the mobile ecosystem. It must constantly adapt to changing traffic patterns, user locations, interference conditions, spectrum constraints, and service requirements. As 5G matures and operators look toward 6G, traditional optimization methods alone will not be enough to manage the next wave of complexity in the RAN. There are several approaches to consider with AI in the RAN: AI-for-RAN, AI in radio chipset, and AI-and-RAN.

AI-for-RAN

AI-for-RAN is the first approach for bringing intelligence to the RAN, where AI runs inside the RAN to improve RAN performance and efficiency. This includes AI embedded in baseband systems; AI agents handling configuration, energy savings, and maintenance tasks; and specialized models improving functions such as channel estimation, link adaptation, and cell-edge throughput.

In practice, AI-for-RAN offers support in several high-value ways to give RAN teams a better chance to act earlier, reduce truck rolls, and keep the network running more smoothly. RAN teams can proactively:

• Predict and reduce interference before it hurts service quality
• Adjust radio parameters as traffic shifts across cells
• Improve energy efficiency by identifying when network resources can be safely dialed down during quieter periods
• Support more accurate channel estimation and link adaptation, helping users at the cell edge get stronger, more consistent experiences
• Spot patterns that suggest a configuration problem, equipment degradation, or emerging service-impacting issue

AI In Radio Chipset

The second approach moves AI even closer to the network signal, where new radio chipsets with programmable cores are opening the door to AI inferencing directly inside the radio.

This could eventually help the network make faster, more precise decisions at the edge of the antenna, where milliseconds and signal quality matter for improving latency and quality of service. It is an important development, but still early, with adoption tied to chipset readiness, radio design, and operator deployment models.

AI-and-RAN

The third approach is AI-and-RAN, where RAN software functions and AI workloads run on the same edge compute platform. However, communications service providers (CSPs) must still weigh performance, capacity, efficiency, power consumption, and total cost of ownership. In this scenario, for some CSPs maximum performance will be the priority and for others energy efficiency and economics will carry more weight.

What Works Now for the RAN?

So, what is working now?

Research shows that the clearest near-term momentum is with AI-for-RAN. It is practical, measurable, and deployable without waiting for a full infrastructure refresh. Early pilots are already showing promising results, including reported double-digit improvements in key performance indicators (KPIs) such as cell-edge throughput.

Just as important, many of these capabilities—such as energy optimization, interference management, traffic-aware tuning, and predictive maintenance—can be introduced via software upgrades on existing platforms, giving CSPs a more realistic path from experimentation to operational impact.

Conclusion

In summary, that does not mean AI-and-RAN or AI inside the radio will not matter. Both could become highly strategic as networks evolve toward 6G. But their adoption will likely take longer because they depend on platform maturity, silicon evolution, modernization cycles, and a clearer business case.

AI-for-RAN gives CSPs a way to start capturing value now by:

• Improving performance
• Reducing manual intervention
• Making RAN more adaptive without forcing a complete rebuild

The first meaningful step is applying AI where it can improve today’s radio network performance, efficiency, and operational confidence. AI-for-RAN is perhaps not the entire journey toward an AI-native RAN—but it is the approach that is working now.

The NETSCOUT Advantage

As the leader in RAN technologies, NETSCOUT has helped 90 percent of the world’s tier-1 operators plan, design, monitor, and optimize their 2G, 3G, 4G LTE, and now 5G networks with RAN monitoring and troubleshooting tools that leverage subscriber traffic for real-time performance visualizations, complex KPI calculations, and executive reporting.

As the 5G network matures and as CSPs evolve their networks, they can rely on the NETSCOUT RAN solutions to support any AI-for-RAN initiatives.

Learn more about NETSCOUT RAN solutions and RAN AI-driven case studies.