Deep Learning-based Wireless Fingerprint Representation Learning and Device Identification

With the rapid proliferation of Internet of Things (IoT) devices, ensuring secure wireless communication has become increasingly critical. Traditional cryptographic methods for device authentication face challenges such as key tampering and high computational overhead. This paper proposes a deep learning-based approach for wireless fingerprint representation learning and device identification, leveraging a neural network-driven synchronization module to enhance feature extraction and robustness. The method effectively addresses the limitations of traditional synchronization techniques, achieving high accuracy and noise resilience, thereby providing a more secure and efficient solution for wireless device authentication in diverse environments.