V2N QoS Prediction Using Statistical Link Modeling

The patent applies preliminary action by predicting future QoS parameters before they actually occur. The system uses machine learning models to forecast data rate, latency, and loss rate for upcoming time intervals, enabling applications to proactively adapt their behavior in advance rather than reacting after QoS degradation occurs. This resolves the contradiction by providing both accurate prediction (improving reliability) and early warning (reducing adaptation time loss).

The patent implements dynamics by using dynamic QoS prediction that adapts to changing vehicular conditions. The system continuously updates predictions based on current vehicle position, speed, and network conditions, allowing the QoS parameters to dynamically adjust rather than relying on static reactive thresholds. This enables the system to maintain reliability while reducing the time needed to adapt to new conditions.

The patent applies segmentation by dividing the QoS prediction into separate independent models for each parameter (data rate, latency, loss rate) and potentially for different time intervals or vehicular conditions. This allows the system to achieve high prediction precision for each specific parameter while managing overall computational complexity through modular processing. Each segmented model can be optimized independently, reducing the burden of computing all parameters simultaneously.

The patent uses parameter changes by selecting and modeling only the most critical QoS parameters (data rate, latency, loss rate) rather than attempting to predict all possible network parameters. The system changes the level of detail in predictions based on application requirements, using full statistical distribution when high precision is needed and potentially simplified metrics when computational resources are constrained. This balances measurement precision with device complexity.

The patent applies universality by designing a QoS prediction system that serves multiple vehicular applications simultaneously through a unified prediction framework. The same prediction infrastructure supports different applications (autonomous driving, platooning, tele-operated driving) with their diverse QoS requirements, rather than requiring separate prediction systems for each application. This reduces overall system complexity while maintaining high adaptability, as the universal system can be configured to provide appropriate prediction accuracy and time intervals for each application type.

The patent uses an intermediary approach by introducing a dedicated QoS prediction server or network entity that acts as a mediator between the vehicular applications and the complex network conditions. This intermediary handles the computationally intensive statistical modeling and machine learning operations, providing simplified prediction results to applications. This reduces the complexity burden on individual vehicles while enabling sophisticated QoS prediction capabilities across the V2N communication system.