As the journey of 5G standardization is coming to an end, academia and industry have already begun to consider the sixth-generation (6G) wireless networks, with an aim to meet the service demands for the next decade. Deep learning-based RF fingerprinting (DL-RFFP) has recently been recognized as a potential solution for enabling key wireless network applications and services, such as spectrum policy enforcement and network access control. The state-of-the-art DL-RFFP frameworks suffer from a significant performance drop when tested with data drawn from a domain that is different from that used for training data. In this paper, we propose ADL-ID, an unsupervised domain adaption framework that is based on adversarial disentanglement representation to address the temporal domain adaptation for the RFFP task. Our framework has been evaluated on real LoRa and WiFi datasets and showed about 24% improvement in accuracy when compared to the baseline CNN network on short-term temporal adaptation. It also improves the classification accuracy by up to 9% on long-term temporal adaptation. Furthermore, we release a 5-day, 2.1TB, large-scale WiFi 802.11b dataset collected from 50 Pycom devices to support the research community efforts in developing and validating robust RFFP methods.

翻译：随着5G标准化进程即将结束，学术界与工业界已开始展望第六代（6G）无线网络，旨在满足未来十年的业务需求。基于深度学习的射频指纹识别（DL-RFFP）近期被公认为实现频谱策略执行与网络接入控制等关键无线网络应用与服务的潜在方案。现有最先进的DL-RFFP框架在测试数据与训练数据分布不同时，会出现显著的性能下降。本文提出ADL-ID——一种基于对抗解耦表示的无监督域自适应框架，以解决RFFP任务中的时序域适应问题。该框架在真实LoRa与WiFi数据集上进行了评估，在短时序适应任务中相较于基线CNN网络准确率提升约24%，在长时序适应任务中分类准确率最高提升9%。此外，我们发布了从50台Pycom设备采集的5天、2.1TB大规模WiFi 802.11b数据集，以支持研究社区开发与验证鲁棒RFFP方法。