S6221A AI RAN Simulation Toolset

Wireless networks are shifting from static hardware to intelligent, software-defined systems. As connectivity and complexity outpace traditional management, AI-native architectures are emerging. By embedding data-driven intelligence across the network stack, AI Radio Access Networks (AI RAN) improve efficiency and adaptability for next-generation performance.

With the Keysight S6221A AI RAN Simulation Toolset you can:

• Create realistic, high-scale and high-fidelity network models to simulate complex RAN scenarios.
• Generate structured datasets containing large volumes of high-quality and trustworthy data for AI model training, testing, and benchmarking.
• Benchmark the performance of AI models to ensure performance targets are achieved and continuously improved.
• Provide actionable performance metrics and KPIs to guide optimization and validation of AI model performance.

AI RAN Testing Challenges

Key challenges in developing and validating AI RAN solutions revolve around data quality, scalability, and performance validation.

• First, generating high-quality and curated datasets is critical to ensure reliable AI model training and validation across diverse, high-fidelity RAN scenarios.
• Second, the use of digital twin environments introduces challenges in simulating large-scale, realistic network conditions for benchmarking and inferencing without impacting live networks.
• Finally, validating AI algorithm performance improvements requires robust testing frameworks capable of measuring real-world impact, explainability, and generalization across varying network conditions, ensuring that AI-driven enhancements truly translate to better RAN efficiency and user experience.

AI RAN Validation Key Use Cases

Validate AI models and systems in network environments:

• High scale validation ensures that the system operates correctly and efficiently under extreme conditions, such as nationwide deployment, requiring vast amounts of data, traffic, and network elements.
• High fidelity focuses on testing complex algorithms in near-real-world scenarios, like dense urban areas with intricate channel conditions, demanding highly granular data that mirrors actual network behavior at the bit or symbol level.
• Module validation targets individual algorithm components before full system integration, using real-time data from emulators and user devices to verify performance on a smaller scale, such as a single link in a hardware testbed.

Together, these layers of validation provide a comprehensive framework for ensuring robust, scalable, and accurate AI deployment in network systems.