Advanced Deep Learning Models for 6G: Overview, Opportunities and Challenges
The advent of the sixth generation of mobile communications (6G) ushers in an era of
heightened demand for advanced network intelligence to tackle the challenges of an …
heightened demand for advanced network intelligence to tackle the challenges of an …
Explanation-guided backdoor attacks on model-agnostic rf fingerprinting
Despite the proven capabilities of deep neural networks (DNNs) for radio frequency (RF)
fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the …
fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the …
Improving rf-dna fingerprinting performance in an indoor multipath environment using semi-supervised learning
MKM Fadul, DR Reising, LP Weerasena… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Internet of Things (IoT) deployments are expected to reach 75.4 billion by 2025. Roughly
70% of all IoT devices employ weak or no encryption, thus putting them and their connected …
70% of all IoT devices employ weak or no encryption, thus putting them and their connected …
Sensitivity Analysis of RFML Applications
Performance of radio frequency machine learning (RFML) models for classification tasks
such as specific emitter identification (SEI) and automatic modulation classification (AMC) …
such as specific emitter identification (SEI) and automatic modulation classification (AMC) …
Deepsweep: Parallel and scalable spectrum sensing via convolutional neural networks
Spectrum sensing is an essential component of modern wireless networks as it offers a tool
to characterize spectrum usage and better utilize it. Deep Learning (DL) has become one of …
to characterize spectrum usage and better utilize it. Deep Learning (DL) has become one of …
Joint estimation of IQ imbalance and PA nonlinearity: An iterative scheme
R Liu, X Xu, X Qin - Digital Signal Processing, 2024 - Elsevier
Power amplifier (PA) nonlinearity and in-phase and quadrature-phase (IQ) imbalance in the
transmitter are of critical concern in modern communication systems due to their significant …
transmitter are of critical concern in modern communication systems due to their significant …
Efficient feature extraction of radio-frequency fingerprint using continuous wavelet transform
M Mohammed, X Peng, Z Chai, M Li, R Abayneh… - Wireless …, 2024 - Springer
In securing wireless communication, radio-frequency (RF) fingerprints, rooted in physical-
layer security, are seriously affected by various types of noise. As a result, effective RF …
layer security, are seriously affected by various types of noise. As a result, effective RF …
Multi-periodicity dependency Transformer based on spectrum offset for radio frequency fingerprint identification
Radio Frequency Fingerprint Identification (RFFI) has emerged as a pivotal task for reliable
device authentication. Despite advancements in RFFI methods, background noise and …
device authentication. Despite advancements in RFFI methods, background noise and …
Software Defined Radio, a perspective from education
The evolution of communication systems has brought about a paradigm shift, particularly in
radiocommunications, where software has increasingly taken precedence over hardware …
radiocommunications, where software has increasingly taken precedence over hardware …
Causal Learning for Robust Specific Emitter Identification over Unknown Channel Statistics
P Tang, Y Xu, G Ding, Y Jiao, Y Song… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Specific emitter identification (SEI) is a device identification technology that extracts radio
frequency (RF) fingerprint from received signals. However, channel effects on RF fingerprint …
frequency (RF) fingerprint from received signals. However, channel effects on RF fingerprint …