An AI-Driven Framework for Enhancing Resilience in Propagation Models to Enable Digital Twin

The evolution of wireless cellular networks to support Digital Twins (DTs) requires robust propagation models. Traditional propagation modeling methods, though fundamental, lack the realism, completeness, and computational efficiency required for effective DT synchronization. This inadequacy underscores the need for models that can seamlessly integrate with real-world network dynamics. Therefore, this work critically examines the resilience of conventional machine learning based models and highlights their vulnerabilities to data scarcity and the inherent dynamism of wireless networks. To address these challenges, we propose an innovative approach that leverages a multi-stage GAN for the generation and augmentation of tabular synthetic data, coupled with an Attention through Segmentation training strategy. This strategy is based on partitioning the data distribution based on the histogram of important features and replacing the single complex model with multiple simpler models focused on specific parts of the histogram. This dual approach significantly improves the resilience of the model, and our evaluations in realistic scenarios show an impressive recovery of more than $90 \%$ performance loss compared to traditional models and this improvement is achieved with a notable reduction in model complexity. Our research marks a significant advancement in the development of resilient and efficient propagation models for the next generation of wireless networks.