The evolution of technology and gadgets has led to a significant increase in the number of transmitted signals, making RF sensing more complex than ever. Challenges such as signal interference and the lack of prior information about all signal parameters further complicate the task. To address 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 detection coverage by utilizing multiple resolution eigengram images derived from a bank of channelizers. These channelizers, varying in size, are adept at sensing different types of signals, such as low duration or low bandwidth signals. Channelizer deconfliction is a known challenge in RFML. We use YOLO, a deep learning algorithm, to deconflict the outputs from different channelizers to avoid overreporting. YOLO's ability to handle three channels makes it ideal for our study as we also use three channelizers.
While our approach is not dependent on YOLO, it provides a good testing ground for this study. To address signal overlap, we utilize an eigengram image capturing the overlap region between signals. By overlaying this eigengram onto the original, we create a composite image highlighting the overlap. We train another YOLO model using two channels, one for each eigengram, enabling detection even with over 50 percent overlap. This work is versatile and promising, extending to other signal visualizations. It has significant potential for wireless industry applications and sets the stage for further RFML research. / Master of Science / Due to the exponential growth in Radio Frequency (RF) signals over the last few decades, brought about by the proliferation of gadgets, signal detection has become more complex than ever. To address these complexities in signal sensing, adopting a dynamic approach that is not reliant on specific parameters or thresholds is essential. RF approaches using deep learning show great promise in tackling these challenges. Deep learning is the branch of machine learning based on artificial neural networks. An artificial neural network uses layers of interconnected nodes called neurons that work together to process and learn from the input data. The first part of this thesis addresses increasing signal coverage by leveraging different signal perspectives, each capturing unique characteristics. By combining these perspectives into a dataset, we train a deep learning model that incorporates the strengths of each view, resulting in maximum detection coverage. The novelty lies in innovative data preprocessing techniques and using YOLO to deconflict signal views with up to three channelizers. In the second part, we focus on detecting overlapped or occluded signals.
We utilize a new dimension of information describing interference regions between signals.
By integrating this overlap perspective, we enhance the dataset to identify instances of extensive signal overlap and determine their regions of coverage. This enhancement enables the deep learning network to identify patterns and effectively detect highly overlapped or completely occluded signals.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/120960 |
| Date | 19 August 2024 |
| Creators | Koppikar, Nidhi Nitin |
| Contributors | Electrical and Computer Engineering, Headley, William C., Jones, Creed Farris, Abbott, Amos L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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