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Found 10 papers, 1 papers with code
RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio Applications
Index Terms-machine learning, reinforcement learning, wireless communications, dynamic spectrum access, OpenAI gym
Probability-Reduction of Geolocation using Reconfigurable Intelligent Surface Reflections
In this paper, we explore the possibility of using a reconfigurable intelligent surface in order to disrupt the ability of an unintended receiver to geolocate the source of transmitted signals in a 5G communication system.
Scaled-Time-Attention Robust Edge Network
We demonstrate that STARE is applicable to a variety of applications with improved performance and lower implementation complexity.
When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning
Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals.
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 technologies started to sufficiently mature in applications related to wireless communications.
Training Data Augmentation for Deep Learning Radio Frequency Systems
Looking into the Radio Frequency Machine Learning (RFML) field of Automatic Modulation Classification (AMC) as an example of a tool used for situational awareness, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data necessary to achieve desired performance levels.
Investigating a Spectral Deception Loss Metric for Training Machine Learning-based Evasion Attacks
However, while these methodologies consider creating adversarial signals that minimize communications degradation, they have been shown to do so at the expense of the spectral shape of the signal.
Effects of Forward Error Correction on Communications Aware Evasion Attacks
Recent work has shown the impact of adversarial machine learning on deep neural networks (DNNs) developed for Radio Frequency Machine Learning (RFML) applications.
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments
Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network.
Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications
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.
