AI Model Adjustment in Wireless Communication Systems

The patent implements dynamic model adjustment by continuously monitoring model effectiveness metrics and automatically triggering adjustment operations when effectiveness degrades below thresholds. This transforms the static AI model execution into a dynamic system that adapts to changing wireless environments through real-time effectiveness evaluation and automated model retraining or switching.

The patent establishes a feedback mechanism where model effectiveness is continuously evaluated based on prediction accuracy, loss functions, and performance metrics. This feedback loop triggers automated model adjustment operations when effectiveness deteriorates, creating a closed-control system that maintains model reliability through continuous monitoring and self-correction.

The patent adjusts model parameters dynamically by changing the frequency and type of model adjustment operations based on effectiveness degradation rates. When effectiveness slowly degrades, less intensive adjustments are performed; when rapid degradation is detected, more frequent adjustments are triggered. This parameter-based control optimizes the balance between maintaining effectiveness and minimizing adjustment overhead.

The patent implements partial model adjustment by selectively adjusting only certain model components or parameters rather than complete retraining, and by performing adjustments at optimized frequencies based on effectiveness thresholds. This partial action approach maintains model effectiveness while reducing the computational overhead compared to full frequent retraining.