Recently, automatic modulation classification techniques using deep neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based or feature-based techniques. While likelihood-based and feature-based techniques are effective, making classification decisions directly on the raw IQ samples removes the need for expertly crafted transformations and feature extractions. In practice, RF environments are typically very dense, and a receiver must first detect and isolate each signal of interest before classification can be performed. The errors introduced by this detection and isolation process will affect the accuracy of deep neural networks making automatic modulation classification decisions directly on raw IQ samples. The importance of defining upper limits on estimation errors in a detector is highlighted, and the negative effects of over-estimating or under-estimating these limits is explored. Additionally, to date, most of the published research has focused on synthetically generated data. While large amounts of synthetically generated data is generally much easier to obtain than real-world signal data, it requires expert knowledge and accurate models of the real world, which may not always be realistic. The experiments conducted in this work show how augmented real-world signal captures can be successfully used for training neural networks used in automatic modulation classification on raw IQ samples. It is shown that the quality and duration of real world signal captures is extremely important when creating training datasets, and that signal captures made from a single transmitter with one receiver can be broadly applicable to other radios through dataset augmentation. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/83560 |
| Date | 15 June 2018 |
| Creators | Hauser, Steven Charles |
| Contributors | Electrical Engineering, Michaels, Alan J., Beex, Aloysius A., Williams, Ryan K., Headley, William C. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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