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 …
Deep learning techniques can classify spectrum phenomena (eg, waveform modulation) with accuracy levels that were once thought impossible. Although we have recently seen …
Wireless communications has greatly benefited in recent years from advances in machine learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …
Automatic modulation classification (AMC) is used in intelligent receivers operating in shared spectrum environments to classify the modulation constellation of radio frequency …
S Kokalj-Filipovic, R Miller - arXiv preprint arXiv:1902.06044, 2019 - arxiv.org
While research on adversarial examples in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation …
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are …
S Kokalj-Filipovic, R Miller… - 2019 IEEE Global …, 2019 - ieeexplore.ieee.org
While research on adversarial examples (AdExs) in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and …
Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the …
D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in …