Demo: Deep Learning-Assisted Physical Layer Key Generation for Secure UAV Communications

This demo presents a novel implementation of physical layer key generation (PLKG) in UAV communication systems by integrating deep learning for key reconstruction. Secure communication is a significant challenge in UAV networks due to their high mobility, frequent topology changes, and vulnerability to eavesdropping. Traditional cryptographic methods are inefficient in such dynamic environments because of high computational costs and latency. Our approach leverages multimodal learning to enhance resilience against Doppler effects and dynamic channel variations. The demonstration showcases a working prototype that extracts channel state information (CSI), predicts UAV trajectory, and reconstructs cryptographic keys with improved consistency and lower mismatch rates compared to conventional methods. Our system utilizes ESP32 microcontrollers for real-time CSI acquisition and Raspberry Pi 4 for deep learning-based processing. We provide a graphical user interface (GUI) that visualizes real-time CSI fluctuations and reconstructed key bits, demonstrating the framework’s resilience to dynamic channel variations.