ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation

TL;DR

This paper introduces ADL-ID, an adversarial disentanglement learning framework for wireless device fingerprinting that significantly improves domain adaptation performance in RF fingerprinting tasks across different temporal datasets.

Contribution

It presents a novel unsupervised domain adaptation method based on adversarial disentanglement for RF fingerprinting, addressing temporal domain shifts in wireless device identification.

Findings

24% accuracy improvement on short-term temporal adaptation

up to 9% accuracy increase on long-term adaptation

Release of a large-scale WiFi dataset for research community

Abstract

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.