ML-Based Channel Prediction in Wireless Systems

The patent applies preliminary action by predicting future channel states before actual transmission occurs. The machine learning model processes past channel information to generate predictions for future time points, allowing the system to proactively adapt to channel changes rather than reactively correcting errors. This enables transmission parameters to be optimized in advance based on predicted channel conditions.

The patent implements feedback mechanisms by continuously monitoring actual channel measurements and comparing them with predicted values. The system uses measurement information from multiple time points as input to the prediction model, creating a closed-loop system where past observations inform future predictions, and prediction accuracy is continuously improved through accumulated training data.

The patent applies universality by designing a multi-functional prediction system that handles multiple tasks: channel state prediction, measurement information collection, model training data accumulation, and performance evaluation. The same infrastructure serves both operational prediction and model improvement functions, reducing overall system complexity despite the advanced capabilities provided.

The patent segments the prediction system into distinct functional modules: a prediction model module that performs channel state prediction, a measurement information management module that collects and organizes training data, and an evaluation module that assesses prediction performance. This modular architecture simplifies implementation and maintenance while enabling accurate predictions.

The patent ensures continuity of useful action by continuously collecting measurement information at multiple time points and continuously training the prediction model with accumulated data. The system maintains an ongoing process of learning from channel variations, ensuring that the prediction model adapts to changing mobility patterns rather than becoming obsolete as channel conditions evolve.

The patent applies dynamics by making the prediction model adaptive to changing channel conditions. The system dynamically updates prediction parameters based on observed channel variations and mobility patterns. The prediction mechanism adjusts to different mobility scenarios by learning from historical data, enabling accurate predictions even when channel changes accelerate.