Concept Drift Detection

Technical Explanation

An AI model trained in summer foliage or stationary conditions experiences severe performance drops when the radio environment changes (e.g. seasonal tree defoliation, new buildings, or sudden rush-hour congestion). Rel-18 defines drift detection metrics that compare real-time inference distributions against baseline training distributions, generating alerts before throughput degrades.

Key Functions

• Tracks statistical divergence between real-time input data and training datasets
• Computes inference confidence scores to detect degraded model performance
• Triggers automated RRC signaling to request model retraining or reconfiguration
• Prevents silent throughput degradation caused by non-stationary radio environments