UE Beam Prediction for Proactive Spatial Filter Changes

The patent applies preliminary action by having the UE predict spatial filter changes before they actually occur. The UE uses machine learning models to anticipate beam adjustments needed due to mobility or environmental changes, and proactively reports these predictions to the network entity. This allows the network to prepare and apply beam adjustments in advance, reducing latency and improving communication efficiency during handovers or beam switches.

The patent applies self-service by enabling the UE to autonomously perform beam prediction using its own mobility information, sensor data, and machine learning models. The UE independently generates spatial filter change predictions and reports them to the network without requiring complex network-side processing or coordination. This distributes the computational burden to the UE, which is better equipped to handle it, while keeping the network side relatively simple.

The patent applies the extraction principle by isolating only the critical spatial filter change prediction information from the full set of possible UE data. Instead of reporting complete channel state information or all sensor data, the UE extracts and reports only the specific prediction results related to spatial filter changes. This minimized information set reduces signaling overhead while still providing the network with the essential data needed to maintain signal quality during beam transitions.