Pattern-Based Wireless Ranging for Reliable Vehicle Digital Key Connectivity

The system dynamically changes communication parameters by selecting different wireless technologies (Bluetooth, Wi-Fi, cellular, UWB) based on real-time signal conditions, distance, and environmental factors. This allows the system to adapt to varying connection requirements without permanently increasing complexity, as only the necessary technologies are activated at any given time.

The patent implements a dynamic connection management system that continuously monitors signal strength, distance, and network conditions to automatically switch between different wireless communication technologies. The machine learning model dynamically adjusts the selection of communication protocols based on current conditions, enabling the system to maintain optimal connection reliability while managing complexity through adaptive rather than static configurations.

The system performs preliminary actions by pre-training the machine learning model with historical connection data and environmental parameters before actual use. This allows the model to make accurate predictions with minimal real-time computation, as the heavy computational work has already been done during the offline training phase. The model stores learned patterns that can be quickly applied during runtime without requiring intensive processing.

The machine learning model serves itself by continuously learning from actual connection outcomes and automatically improving its prediction accuracy over time. The system uses feedback from successful and failed connections to refine the model's parameters, reducing the need for external intervention or recalibration. This self-improving capability allows the system to maintain high selection accuracy while minimizing the computational overhead required for model maintenance.