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Showing 1 to 8 of 8 for “"Radio Frequency Machine Learning"”.
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The Importance of Data in RF Machine Learning
While the toolset known as Machine Learning (ML) is not new, several of the tools available within the toolset have seen revitalization with improved hardware, and have been applied across several domains in the last two decades. Deep Neural Network (DNN) applications have contributed to …
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Real-World Considerations for RFML Applications
Radio Frequency Machine Learning (RFML) is the application of ML techniques to solve problems in the RF domain as an alternative to traditional digital-signal processing (DSP) techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a …
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Adversarial RFML: Evading Deep Learning Enabled Signal Classification
Deep learning has become an ubiquitous part of research in all fields, including wireless communications. Researchers have shown the ability to leverage deep neural networks (DNNs) that operate on raw in-phase and quadrature samples, termed Radio Frequency Machine Learning (RFML), to synthesize new …
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Foundations of Radio Frequency Transfer Learning
The introduction of Machine Learning (ML) and Deep Learning (DL) techniques into modern radio communications system, a field known as Radio Frequency Machine Learning (RFML), has the potential to provide increased performance and flexibility when compared to traditional signal processing techniques …
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Sensitivity Analysis of RFML-based SEI Algorithms
Radio Frequency Machine Learning (RFML) techniques for the classification tasks of Specific Emitter Identification (SEI) and Automatic Modulation Classification (AMC) have seen rapid improvements in recent years. The applications of SEI, a technique used to associate a received signal to an …
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One Size Does Not Fit All: Optimizing Sequence Length with Recurrent Neural Networks for Spectrum Sensing
… more important than ever before. In the field of Radio Frequency Machine Learning (RFML), techniques like deep neural networks and reinforcement learning have been used to develop more complex spectrum sensing systems that are not reliant on expert features. Architectures like Convolutional Neural …
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Intelligently Leveraging Multi-Channel Image Processing Neural Networks for Multi-View Co-Channel Signal Detection
… this challenge, researchers have explored machine learning and deep learning approaches to generalize solutions for real-world sensing problems. In this thesis, we focus on two key issues in RF signal detection using deep learning. Firstly, we tackle the problem of increasing signal …
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Enhancing Communications Aware Evasion Attacks on RFML Spectrum Sensing Systems
Recent innovations in machine learning have paved the way for new capabilities in the field of radio frequency (RF) communications. Machine learning techniques such as reinforcement learning and deep neural networks (DNN) can be leveraged to improve upon traditional wireless communications methods …