O-RAN SMO MIMO Configuration via AI/ML

The patent segments the MIMO configuration management into separate functional modules: an AI/ML module for intelligent decision-making, a configuration module for parameter management, and a feedback module for performance monitoring. This segmentation allows each module to handle specific aspects of complexity independently, making the overall system more manageable while maintaining high data rates through optimized MIMO configurations.

The patent implements a feedback mechanism where performance data from MIMO operations is collected and fed back to the AI/ML module. This feedback loop enables continuous optimization of MIMO configurations based on actual performance, allowing the system to adapt to changing network conditions and maintain optimal data rates while automatically managing complexity through learned patterns and predictions.

The patent applies preliminary action by pre-training AI/ML models offline using historical data and simulated network conditions. These pre-trained models are then deployed in the network to make rapid configuration decisions without requiring intensive real-time computation. This approach enables the system to achieve high spectral efficiency through optimized MIMO configurations while significantly reducing the computational energy required during actual network operation.

The patent uses copying by creating simplified representations or copies of complex network scenarios through data analytics and simulation. Instead of running complex AI/ML models on every real-time network decision, the system uses pre-generated models and templates that capture essential patterns, allowing for fast configuration optimization with minimal computational overhead while maintaining high spectral efficiency.