Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Adversarial attacks (1)
- Artificial intelligence (1)
- Artificial neural networks (1)
- Automatic modulation recognition (1)
- Classification (1)
-
- Classifiers (1)
- Computational modeling (1)
- Cyclostationarity (1)
- Datasets (1)
- Deep learning (1)
- Defensive distillation (1)
- Digitally modulated signals (1)
- Feature extraction (1)
- Generative adversarial networks (1)
- Iterative methods (1)
- Machine learning (1)
- Model poisoning (1)
- Modulation (1)
- Neural networks (1)
- Next generation networking (1)
- Next generation networks (1)
- Perturbation methods (1)
- Quadratures (1)
- Random variables (1)
- Signal classification (1)
- Signal generation (1)
- Signal processing (1)
- Solid modeling (1)
- Spectrum allocation (1)
Articles 1 - 2 of 2
Full-Text Articles in Signal Processing
Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner
Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner
Electrical & Computer Engineering Faculty Publications
This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …
Defending Ai-Based Automatic Modulation Recognition Models Against Adversarial Attacks, Haolin Tang, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Yanxiao Zhao
Defending Ai-Based Automatic Modulation Recognition Models Against Adversarial Attacks, Haolin Tang, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Yanxiao Zhao
Engineering Technology Faculty Publications
Automatic Modulation Recognition (AMR) is one of the critical steps in the signal processing chain of wireless networks, which can significantly improve communication performance. AMR detects the modulation scheme of the received signal without any prior information. Recently, many Artificial Intelligence (AI) based AMR methods have been proposed, inspired by the considerable progress of AI methods in various fields. On the one hand, AI-based AMR methods can outperform traditional methods in terms of accuracy and efficiency. On the other hand, they are susceptible to new types of cyberattacks, such as model poisoning or adversarial attacks. This paper explores the vulnerabilities …