A data quality validation framework to keep wireless AI models accurate, stable, and safe. It applies to three pillars: Integrity, Diversity, and Similarity; to block error propagation, ensure robust training across varied conditions, and catch distribution shifts between training and deployment data. By validating both synthetic and real datasets, the framework supports “train-once, deploy-anywhere” scaling while reducing the risk of catastrophic forgetting during model updates.
