A Domain-Aware Framework for Interpretable and Resilient Propagation Models: Enabling Digital Twins for Wireless Networks

In the rapidly evolving landscape of wireless networks, accurate and resilient propagation models are essential to achieve optimal performance and reliability. This paper presents a novel domain-aware framework for interpretable and resilient propagation models. The proposed approach represents an innovative architecture framework that is not only interpretable but can also deal with training data size scarcity. Bridges domain knowledge with machine learning. The proposed approach leverages a combination of domain expertise, analytical modeling, and customized neural networks to construct interpretable models that excel in both identical distribution and non-identical distribution test-train dataset scenarios. Through a comprehensive analysis, we demonstrate the proposed approach's ability to adapt and refine models in response to real-world variations, ensuring consistent, high-quality performance. The proposed framework not only enhances our understanding of complex systems but also paves the way for the creation of digital twins for wireless networks. Furthermore, the root mean square error of the performance metric for the proposed approach is reported as 6.97 dB, further confirming its effectiveness in accurately predicting the results of wireless propagation.