The Critical Role of AI RSG in Advanced 5G and 6G Networks

Artificial intelligence (AI) is rapidly reshaping telecommunications, evolving from a tool for automating discrete tasks into a core capability for intelligent, context-aware decision-making. It is becoming central to network operations, especially in 5G and emerging 6G deployments, including technologies such as MU-MIMO.

So far, AI has mainly served as an add-on, optimizing existing systems through dynamic network slicing, improved resource management, and enhanced fault and security detection. However, next-generation 5G and 6G networks—built on AI-native architectures—are shifting AI to the core of network operations. This enables autonomous management of complex, heterogeneous Service Management and Orchestration (SMO) environments based on Open RAN and controlled via the RAN Intelligent Controller (RIC).

This evolution raises a key challenge: ensuring AI consistently makes accurate decisions at scale under dynamic network conditions. Achieving this depends on high-quality, real-world representative data and requires continuous testing, validation, and stress-testing of models to prevent drift and maintain resilience in changing and unforeseen scenarios.

Why AI RSG is Critical for Next-Gen Networks

TeraVM AI RAN Scenario Generator (AI RSG) forms the intelligence layer enabling truly AI-native 5G and 6G systems to scale with confidence. As network environments become increasingly complex—with massive device densities, heterogeneous services, and stringent performance requirements—traditional management approaches are no longer sufficient. AI RSG delivers real-time, autonomous optimization of network resources while maintaining consistent Quality of Experience (QoE) across highly dynamic conditions. It enables self-configuring, self-optimizing, and self-healing network behaviors, reducing operational complexity while significantly improving efficiency across spectrum, compute, and energy domains—including intelligent power management, dynamic energy allocation, and reduced consumption through adaptive workload distribution and infrastructure optimization.

• Access to reliable, high-quality data

AI performance is only as strong as the data behind it—poor data inevitably produces poor outcomes. In advanced 5G and emerging 6G networks, this makes high-quality, representative, and scalable data a critical requirement.

Today, AI models depend on live network data such as performance counters, call traces, and operational logs. While grounded in reality, this data is often delayed, fragmented, historical, and difficult to access—especially for third-party vendors constrained by privacy, security, and commercial limits. Synthetic data helps bridge scale and speed, but alone it lacks real-world fidelity. The strongest approach is a hybrid model combining real and synthetic data, orchestrated through an AI RSG.

By enabling a RAN digital twin, AI RSG replicates an operator’s network—covering topology, configurations, traffic, and environmental dynamics—while integrating live inputs. This produces high-fidelity, physics-aware data that continuously trains, tests, and validates AI models and RIC applications, ensuring resilience and adaptability in real-world and future network conditions.

• Assuring long-term performance of AI applications

Once AI is deployed in xApps, rApps, or SON (Self-Organizing Networks), the challenge shifts from training to maintaining consistent performance in a constantly evolving network. Because networks are dynamic, AI models must continuously adapt; otherwise, they face “AI drift,” where performance degrades as real-world conditions diverge from training data, leading to unintended or harmful outcomes.

A key risk arises when objectives are poorly bounded. For example, an AI optimizing energy efficiency may shut down large parts of the network—meeting its goal but severely disrupting service. This highlights the need for clear objectives and operational guardrails.

To address this, continuous validation in a controlled environment is essential. An AI RSG-powered digital twin allows AI decisions to be tested in a simulated replica of the live network, using a closed-loop process where actions are evaluated against full KPI impacts before deployment, enabling safe iterative learning. An App Validation Engine (AVE) adds further oversight by tracking long-term behavior, detecting convergence, stagnation, or divergence, and measuring performance against guardrail KPIs such as service quality and coverage. It also benchmarks results against an estimated “ceiling of improvement,” helping quantify AI effectiveness against optimal performance and enabling early detection and correction of drift.

• Engineered for the unexpected

Live networks face rare but high-impact events, requiring AI systems that are resilient under extreme conditions. A key role of a RAN digital twin is to prepare AI applications through structured “what-if” analysis.

Using an AI RSG, operators can safely simulate scenarios that are impossible to test at scale in live networks, including large-scale congestion, hardware failures, and cyberattacks such as DoS/DDoS. These simulations help evaluate resilience, response strategies, and network robustness. The same capability also supports planning use cases like optimal cell site placement and assessing RF propagation impacts from new structures such as high-rise buildings.

Beyond failure scenarios, AI RSG enables hypothesis testing for 5G evolution and early 6G development, including experimentation with technologies like dynamic spectrum sharing and realistic propagation modeling before physical infrastructure exists. Overall, AI RSG bridges lab development and real-world deployment by enabling safe, large-scale scenario testing—improving resilience, reducing R&D risk, and accelerating time to market. High-fidelity digital twins powered by RAN scenario generation provide a practical framework for optimizing performance across the full network lifecycle.

AI RSG: Core Capabilities and Benefits

The TeraVM AI RSG provides a digital twin framework to emulate diverse network topologies, mobility patterns, and traffic scenarios across real and synthetic environments. Supporting 4G, 5G, emerging 6G, and NTN, it enables advanced AI training for complex technologies such as massive MIMO—serving as a powerful virtual sandbox for RAN vendors, CSPs, and developers. It scales to 10,000 UEs and thousands of cells per server, with flexible deployment across Docker, Kubernetes, and major cloud platforms including AWS, Azure, and GCP.

Beyond scale, it generates realistic RAN datasets to train AI/ML models for SON, rApps, and xApps, supports Open RAN validation by benchmarking RIC performance, and enables AI-driven optimization of advanced network features. With highly configurable scenarios—spanning UE and cell types, spectrum bands, traffic profiles, and GIS-based real-world maps—along with controlled anomaly injection, it provides a scalable, realistic environment for accelerating innovation and enhancing network performance.

AI RSG Powering the Self-Aware Network Concept

VIAVI and DOCOMO recently demonstrated AI-driven radio access network (RAN) control for next-generation 6G systems, showcasing the Self-Aware Network concept using VIAVI’s digital twin and TeraVM AI RSG. The results show that reducing control overhead can boost system throughput by up to 20%, freeing valuable wireless resources for data transmission. This approach also reduces reliance on traditional network quality measurements and user equipment (UE) reporting by leveraging high-fidelity digital twins and AI-powered simulators grounded in real-world data—enabling more efficient, adaptive, and scalable network control.

Key Takeaways

The rise of AI-native 5G—and the even deeper reliance on AI in 6G—demands a complete reset in how network intelligence is designed, validated, and governed. Continuous testing through an AI RAN scenario generator, powered by a hybrid of real and synthetic data, is the only scalable way to ensure these systems remain reliable in real-world conditions. Embedded across the full AI lifecycle, it enables constant validation, ongoing refinement, and effective control of AI drift.

As networks evolve from 4G to increasingly complex 5G and 6G architectures, the value of such tools becomes even more evident. In an era where AI sits at the core of network operation, maintaining high quality of experience in a cost-efficient way is no longer feasible without AI RSGs. Their adoption is therefore not optional, but a necessary foundation for future-ready network design and operation.