Secure Machine Learning Based RF Signal Classification for Wireless Systems

Author:
Description:
To monitor the activity over a radio frequency (RF) channel and coordinate its access among heterogeneous wireless systems, network administrators and/or users must be able to identify observed transmissions rapidly and accurately. Recent research shows that deep neural networks (DNNs) can identify the underlying waveform of an RF signal based on the in-phase/quadrature (I/Q) samples without decoding them. Our research starts with DNN designs in the context of spectrum sharing, focusing on Wi-Fi, LTE-LAA, and 5G NR-U systems that coexist over the unlicensed 5 GHz bands. First, we consider recurrent neural network (RNN) architectures, exploiting their capability to capture sequential features. We examine several variations of RNNs, including Simple RNNs, Long Short-term Memory (LSTM) networks, and Gated Recurrent Units (GRU) networks, and apply them in designing protocol classifiers. To further improve the classification accuracy, we expand these RNN designs into a bidirectional structure, enabling the RNN cell to learn temporal dependencies in both forward and backward directions. This bidirectionality significantly augments the volume of information and context accessible to the neural network. We further advance our designs to incorporate multi-layer RNNs, enabling the classifier to capture temporal correlations across multiple time scales, thereby amplifying the network's computational capability. Lastly, we propose additional enhancements to mitigate the overfitting issue in RNN training, including regularization techniques, recurrent weight constraints, and rate halving strategies. Next, we harness the distinctive features embedded within the waveform of each wireless signal. Specifically, we exploit Fourier analysis of the I/Q sequences to further improve the classification accuracy. By applying Short-time Fourier Transform (STFT), additional information in the frequency domain can be extracted. Using segments of the received samples as input, a Convolutional Neural Network (CNN) and a RNN are combined and ...
Publisher:
The University of Arizona.
Contributors:
Krunz, Marwan ; Li, Ming ; Tandon, Ravi
Year of Publication:
2024
Document Type:
Electronic Dissertation ; text ; [Doctoral and postdoctoral thesis]
Language:
en
Subjects:
Artificial Intelligence ; Digital Signal Processing ; Machine Learning ; Wireless System Security ; Wireless/Digital Communications
DDC:
006 Special computer methods (computed)
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author. ; http://rightsstatements.org/vocab/InC/1.0/
Relations:
Zhang, Wenhan. (2024). Secure Machine Learning Based RF Signal Classification for Wireless Systems (Doctoral dissertation, University of Arizona, Tucson, USA). ; http://hdl.handle.net/10150/672475
URL:
http://hdl.handle.net/10150/672475
Content Provider:
The University of Arizona: UA Campus Repository  Flag of United States of America