RF Wireless System Digital Twins And Automation For AI

Validating modern RF systems through physical prototyping and over-the-air testing alone can be costly, time-consuming, and insufficient for capturing the countless real-world scenarios devices encounter. This presentation explores how Remcom's integrated XFdtd® and Wireless InSite® workflow enables engineers to create accurate RF digital twins that extend validation far beyond the limits of laboratory testing.

Learn how automated simulation bridges the gap between component-level antenna performance and system-level wireless behavior. The workflow begins with XFdtd to model antenna geometries and generate Huygens surfaces and S-parameters, then seamlessly transfers those results into Wireless InSite for site-specific propagation analysis across complex indoor, urban, and non-terrestrial network (NTN) environments. Using the device's true near-field characteristics, engineers can accurately evaluate performance under challenging conditions, including varying device orientations, signal blockage, and multipath propagation.

The presentation also demonstrates how this automated pipeline produces large, labeled datasets that correlate antenna characteristics with key channel metrics such as path loss, delay spread, and angle of arrival/departure (AoA/AoD). These high-quality datasets provide the foundation for developing and training AI and machine learning models that improve RF system optimization, wireless chipset algorithms, and next-generation communications technologies.

Whether you're designing advanced antennas, optimizing wireless devices, or developing AI-driven RF solutions, discover how digital twin technology can reduce testing requirements, accelerate design cycles, and deliver more comprehensive system validation across realistic operating environments.