Wireless systems are vulnerable to various attacks such as jamming and eavesdropping
due to the shared and broadcast nature of wireless medium. To support both attack and …

RF fingerprinting involves identifying characteristic transmitter-imposed variations within a
wireless signal. Deep neural networks (DNNs) that do not rely on handcrafting features have …

The universal availability of unmanned aerial vehicles (UAVs) has resulted in many
applications where the same make/model can be deployed by multiple parties. Thus …

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 …

Several factors are motivating the development of preventive, personalized, connected,
virtual, and ubiquitous healthcare services. These factors include declining public health …

We propose a framework called AirID that identifies friendly/authorized UAVs using RF
signals emitted by radios mounted on them through a technique called as RF fingerprinting …

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for
communication links in many current and emerging Internet of Things (IoT) applications …

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G
wireless standard promises autonomous network behavior and ultra-low-latency …

This letter proposes a transfer learning model for automatic modulation recognition (AMR)
with only a few modulated signal samples. The transfer model is trained with the audio …

Automatic modulation classification is an important component in many modern aeronautical
communication systems to achieve efficient spectrum usage in congested wireless …