Channel-Robust RF Fingerprinting via Adversarial and Triplet Losses

Radio frequency fingerprints (RFFs), arising from inherent hardware imperfections, serve as distinctive features for device identification. The location- and time-dependent nature of the wireless channel directly affects RFF-based device identification, making it challenging under different channel conditions. This is primarily because the training and test datasets containing RFFs may not overlap within the same feature-space domain. In this work, the mentioned issue is addressed as a domain adaptation problem. For this objective, we propose the use of a triplet-learning-based domain-adversarial neural network within a hybrid framework named TripletDANN. We leverage the triplet loss, enabling the network to focus exclusively on device-specific latent representations under different channel conditions, while employing an adversarial loss to prevent the network from exploiting channel-specific characteristics. With this aim, data aggregation is performed together with channel labeling. The generalization capability of TripletDANN is evaluated on previously unseen test data collected across different locations under two distinct scenarios. Raw I/Q signals of 15 Wi-Fi devices are used as a case study. The proposed TripletDANN model achieves up to $88.52\%$ average device classification accuracy across the different data collection locations. On average, TripletDANN attains up to a $5\%$ performance improvement over its counterpart model. Moreover, data augmentation is employed to improve the overall performance, and a highest accuracy of $96.71\%$ is achieved on experimentally collected test data from an unseen location.