TeraVM AI RSG

What is the AI RSG?

AI RSG is an AI enabler for Intelligent SON applications and r/xApps development. It is an AI enablement tool focussing on data generation for AI training and test.

TeraVM AI RSG can simulate up to 10,000 user equipment (UEs) and several thousand cells (between 1,000 and 5,000) per reference server. It can be deployed in Docker containers within cloud environments or on dedicated servers. The scale depends on the specific use case, with the ability to generate data at various granularities, from one minute to daily intervals. The tool’s scalability matrix helps define parameters for different scenarios.

Users can import real-world maps with streets, buildings, and network configurations to create UE profiles that simulate movements, handovers, and resource requests. The more real the network scenarios and user behaviours are the more ‘what if’ scenarios can be generated to train the AI apps. Traffic demand profiles can also be generated, enabling UEs to switch between idle and active states while consuming varying amounts of Physical Resource Blocks (PRBs). Layer 1 radio conditions are modeled and fed into the Layer 2 scheduler, which assigns PRBs based on the Channel Quality Indicator (CQI).

Three pillars of the AI RSG

• Hybrid Data Lakes
  Users can import real-world maps with streets, buildings, and network configurations to create UE profiles that simulate movements, handovers, and resource requests
• RAN Scenario Generator
  Train AI and ML apps through exposure to RAN Scenarios with real traffic
• App Validation Test & RAN Digital Twin
  Test the effectiveness of app decisions in realistic network environment

Challenges when deploying the RIC

Users can import real-world maps with streets, buildings and network configurations to create UE profiles that simulate movements, handovers and resource requests. Traffic demand profiles can also be generated, enabling UEs to switch between idle and active states while consuming varying amounts of Physical Resource Blocks (PRBs). Layer 1 radio conditions are modeled and fed into the Layer 2 scheduler, which assigns PRBs based on the Channel Quality Indicator (CQI). AI RSG supports various scheduling types and allows users to introduce anomalies like interference to study their effects on signal-to-noise ratio (SNR) and throughput.