Monojit Samaddar of VIAVI discusses the critical role of AI RAN Scenario Generator (AI RSG) in AI-native 5G and 6G networks. AI RSG utilizes digital twin technology to enable continuous testing and validation of AI models in complex and dynamic network conditions, ensuring accurate decision-making, reliable performance, and reducing operational complexity. This technology is vital for maintaining consistent AI performance, preventing "AI drift," and preparing for unexpected events in next-generation network deployments.
The increasing integration of AI into 5G and upcoming 6G networks demands robust validation to ensure consistent performance and prevent AI model degradation. AI RSG directly addresses this by providing a controlled environment for testing and refining AI, crucial for the autonomous management of complex network operations. As networks become more AI-centric, the ability to accurately simulate and predict performance across diverse scenarios will be a key differentiator in service quality and operational efficiency. Moving forward, watch for further collaborations between network infrastructure providers and AI testing solution developers, particularly as 6G trials begin and the need for high-fidelity simulation intensifies.
The concept of using AI and digital twins to manage and optimize next-generation wireless networks is gaining traction. According to a white paper from VIAVI Solutions (undated, but consistent with recent releases), AI is shifting from an optimization tool to a core component of future telecom networks, especially in 6G, where AI-native architectures will provide intelligence for every decision. This transition, however, presents challenges related to AI model drift and reliability in dynamic real-world conditions.
5G Technology World (undated) also highlighted the importance of RAN digital twins in migrating to AI-native 6G architectures, emphasizing that AI models are prone to drift because they reflect past conditions while networks evolve. This publication notes that a hybrid data strategy, combining emulated traffic and real-world data, is crucial for continuous recalibration and testing of AI algorithms.
Further demonstrating this trend, Springer Nature published a chapter in February 2026 on "GENATWIN," a Generative AI-based Network Digital Twin framework. Developed by researchers from Samsung R&D India and other institutions, GENATWIN uses conditional generative adversarial networks (cGANs) to synthesize realistic network behavior for scalable AI evaluation in Beyond 5G (B5G) networks. This framework aims to bridge the gap between developing AI/ML models and deploying them confidently by simulating nuanced, real-time conditions that traditional methods struggle to replicate.
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