TiDoSGAN: Enhanced Generative Adversarial Network for NFC Restoration

Recent advances in near-field communication (NFC) technology aim to significantly improve the range and robustness of NFC connections. Traditionally, NFC communication requires precise alignment between the transmitting and receiving antennas, which typically operate within a few centimeters. This limitation has restricted the wide applicability of NFC in various contexts. In this work, we address these challenges by leveraging generative adversarial networks (GANs) to effectively restore signals affected by attenuation and distortion. By employing a GAN-based model suitable for wireless reception signal processing, we enhance the integrity of NFC signals, enabling robust communication at longer distances with less precise alignment. The proposed method demonstrates that the signal restoration at a distance of $5\times $ the reference distance is very similar to the signal at the reference distance, while preserving the clock recovery and decoding performance of the receiver at the reference distance and maintaining security with a shared key approach. This article provides a comprehensive analysis of the integration of GAN techniques into NFC systems, offering insights into the implications of signal restoration and practical deployment considerations.