In light of the finite nature of the wireless spectrum and the increasing demand for spectrum
use arising from recent technological breakthroughs in wireless communication, the problem …

Deep learning methods have been very successful at radio frequency fingerprinting tasks,
predicting the identity of transmitting devices with high accuracy. We study radio frequency …

With the popularization of the Internet of Things (IoT), its security has become increasingly
prominent. Radio-frequency fingerprinting (RFF) is a promising approach to identify a …

We present complex-valued Convolutional Neural Networks (CNNs) for RF fingerprinting
that go beyond translation invariance and appropriately account for the inductive bias with …

With the development of wireless communication technology, the electromagnetic
environment has become more and more complex. Conventional signal identification …

In IoT networks, authentication of nodes is primordial and RF fingerprinting is one of the
candidates as a non-cryptographic method. RF fingerprinting is a physical-layer security …

Deep learning-based classification algorithms offer no performance guarantees when
deployed on testing data not generated by the same process as the training data. Such out …

Deep learning has increasingly been used to identify minerals. However, deep learning can
only be used to identify minerals within the distribution of the training set, while any mineral …

RF Fingerprinting techniques aim to authenticate a wireless emitter by the imperfections due
to these components. It can be useful for authentication and network management for the …

NOTICE OF RETRACTION: This article has been retracted from the ACM Digital Library
because of Author Misrepresentation. The ACM published paper used an earlier work …