NETSCOUT: How AI Is Reshaping the Radio Access Network

For years, AI supported mobile networks from the edges — planning, troubleshooting, automation, and assurance. Now AI is moving into the radio access network (RAN) itself, where coverage, capacity, latency, energy use, and subscriber experience are shaped in real time. For CSPs preparing for 5G maturity and 6G, this shift changes how the network is optimized, operated, and monetized.

What was announced

NETSCOUT outlines three complementary approaches to embedding AI into the RAN: AI-for-RAN, AI in the radio chipset, and AI-and-RAN. AI-for-RAN runs inside the RAN to improve performance and efficiency — from baseband systems and configuration agents to channel estimation and cell-edge throughput models. AI in the radio chipset moves inferencing directly into programmable cores inside the radio, enabling faster edge decisions. AI-and-RAN colocates RAN software and AI workloads on the same edge compute platform, forcing CSPs to balance performance, capacity, power consumption, and total cost of ownership.

According to NETSCOUT, the clearest near-term momentum is with AI-for-RAN. Early pilots have shown double-digit KPI improvements, including in cell-edge throughput, and most capabilities can be introduced via software upgrades on existing platforms.

Why this matters

For CIOs, CTOs, and network operations leaders at communications service providers, the RAN is one of the most dynamic and expensive parts of the mobile ecosystem. Traditional optimization methods alone will not scale to manage the next wave of complexity as 5G matures and 6G approaches. AI-for-RAN offers a realistic path from experimentation to operational impact without a full infrastructure refresh.

AI-for-RAN is perhaps not the entire journey toward an AI-native RAN, but it is the approach that is working now

Technical details

• AI-for-RAN: AI embedded in baseband systems and agents handling configuration, energy savings, and maintenance.
• Interference management: predict and reduce interference before it degrades service quality.
• Traffic-aware tuning: adjust radio parameters as traffic shifts across cells.
• Energy efficiency: identify when network resources can be safely dialed down during quieter periods.
• Channel estimation and link adaptation: stronger, more consistent experience for users at the cell edge.
• Predictive maintenance: detect configuration issues, equipment degradation, or emerging service-impacting patterns.
• AI in radio chipset: AI inferencing directly inside the radio via programmable cores.
• AI-and-RAN: shared edge compute platform for RAN software and AI workloads.
• Deployment: many capabilities delivered via software upgrades on existing platforms.

Softprom and NETSCOUT

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

This content was prepared as part of the Softprom DistriFlow project — an automated system for monitoring and adapting vendor news. Original source: original article.