Advancing EMC Analysis With GAN-Driven Signal Classification and Waveform Modulation

Abstract

This study advances Electromagnetic Compatibility (EMC) by investigating how electromagnetic interference (EMI) from Radio Frequency (RF) sources affects digital interconnects. Unlike traditional analyses centered on Continuous Wave (CW) signals, we adopt an RF-focused approach using S-parameter data and consistent RF power to emphasize steady-state responses. This method eliminates the need for time-domain conversions, allowing for more accurate analysis. Our research introduces a novel image-based classification system that accurately assesses signal safety based on steady-state responses. By leveraging a Generative Adversarial Network (GAN) trained on ‘safe’ and ‘unsafe’ signal images, our system can effectively recognize and distinguish between these two states. The GAN’s ability to generate realistic signal patterns enhances classification accuracy, especially when empirical data is limited. This approach has been validated through multiple transformations to ensure robustness and reliability. The findings offer significant improvements in EMC analysis and provide practical guidelines for designing robust digital interconnects. These advancements contribute to enhancing the reliability and security of electronic devices in environments with high RF interference, making them better suited for real-world commercial applications where signal integrity is critical.