Wireless Positioning Information Exchange for Adaptive AI/ML Models

The patent implements dynamic model selection where the network entity chooses different AI/ML models based on real-time environmental conditions and positioning requirements. The system dynamically adjusts model parameters and selects appropriate models from a set, enabling adaptation to varying wireless environments without requiring complete retraining, thus improving both accuracy and generalization.

The patent modifies model training parameters and configuration settings based on specific positioning scenarios and available resources. By adjusting training parameters such as learning rate, batch size, and model complexity levels, the system optimizes model performance for different environments while maintaining the ability to generalize across various wireless communication scenarios.

The patent pre-trains multiple AI/ML models on different environmental scenarios and stores them in a repository before actual positioning operations. When positioning is needed, the system selects from pre-trained models rather than performing training in real-time, significantly reducing training time while maintaining high accuracy for various environments.

The patent performs partial training or fine-tuning rather than complete retraining of models for each specific scenario. By using transfer learning and only training necessary portions of the model based on available data and requirements, the system achieves improved accuracy while minimizing computational resource consumption and training time.

The patent divides the model management system into modular components including a model repository, selection module, and training module. Each component handles specific functions independently, making the complex system of multiple models manageable through structured organization and separation of concerns.

The patent implements feedback mechanisms where the network entity monitors positioning performance and environmental conditions to automatically select appropriate models. The system uses performance feedback to adjust model selection and training priorities, reducing manual management complexity while maintaining high adaptability across different scenarios.