A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer
This paper presents a systematic and comprehensive survey that reviews the latest research
efforts focused on machine learning (ML) based performance improvement of wireless …
efforts focused on machine learning (ML) based performance improvement of wireless …
Evaluating adversarial evasion attacks in the context of wireless communications
B Flowers, RM Buehrer… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Recent advancements in radio frequency machine learning (RFML) have demonstrated the
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …
Specific emitter identification using convolutional neural network-based IQ imbalance estimators
Specific Emitter Identification is the association of a received signal to a unique emitter, and
is made possible by the naturally occurring and unintentional characteristics an emitter …
is made possible by the naturally occurring and unintentional characteristics an emitter …
Signal processing-based deep learning for blind symbol decoding and modulation classification
Blindly decoding a signal requires estimating its unknown transmit parameters,
compensating for the wireless channel impairments, and identifying the modulation type …
compensating for the wireless channel impairments, and identifying the modulation type …
Automatic modulation classification using compressive convolutional neural network
The deep convolutional neural network has strong representative ability, which can learn
latent information repeatedly from signal samples and improve the accuracy of automatic …
latent information repeatedly from signal samples and improve the accuracy of automatic …
Survey of Research on Application of Deep Learning in Modulation Recognition
Y Sun, W Wu - Wireless Personal Communications, 2023 - Springer
Modulation recognition is an important research branch in the field of communication, which
is widely used in civil and military fields. The classic methods depend on decision theory …
is widely used in civil and military fields. The classic methods depend on decision theory …
Clustering learned CNN features from raw I/Q data for emitter identification
LJ Wong, WC Headley, S Andrews… - MILCOM 2018-2018 …, 2018 - ieeexplore.ieee.org
Specific Emitter Identification (SEI) is the act of matching a received signal to an emitter
using a database of radio frequency (RF) features belonging to known transmitters. SEI …
using a database of radio frequency (RF) features belonging to known transmitters. SEI …
Rml22: Realistic dataset generation for wireless modulation classification
V Sathyanarayanan, P Gerstoft… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Application of Deep learning (DL) to modulation classification has shown significant
performance improvements. The focus has been model centric, where newer architectures …
performance improvements. The focus has been model centric, where newer architectures …
An rfml ecosystem: Considerations for the application of deep learning to spectrum situational awareness
While deep learning (DL) technologies are now pervasive in state-of-the-art Computer
Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have …
Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have …
The rfml ecosystem: A look at the unique challenges of applying deep learning to radio frequency applications
While deep machine learning technologies are now pervasive in state-of-the-art image
recognition and natural language processing applications, only in recent years have these …
recognition and natural language processing applications, only in recent years have these …