Keysight S6221A AI RAN Simulation Toolset
Highlights
- 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.
Wireless networks are rapidly evolving from static, hardware-driven architectures toward intelligent, software-defined systems capable of self-optimization. As modern networks introduce massive connectivity, diverse service requirements, and complex radio environments, traditional rule-based management becomes insufficient. This has driven the shift toward AI-native network architectures, where data-driven intelligence is embedded throughout the network stack, paving the way for AI Radio Access Networks (AI RAN), the next generation of radio access networks that leverage AI to enhance efficiency, adaptability, and performance.
Wireless networks are rapidly evolving from static, hardware-driven architectures toward intelligent, software-defined systems capable of self-optimization. As modern networks introduce massive connectivity, diverse service requirements, and complex radio environments, traditional rule-based management becomes insufficient. This has driven the shift toward AI-native network architectures, where data-driven intelligence is embedded throughout the network stack, paving the way for AI Radio Access Networks (AI RAN), the next generation of radio access networks that leverage AI to enhance efficiency, adaptability, and performance.
