Real-World Considerations for Deep Learning in Spectrum Sensing
| dc.contributor.author | Hauser, Steven Charles | en |
| dc.contributor.committeechair | Michaels, Alan J. | en |
| dc.contributor.committeechair | Beex, Aloysius A. | en |
| dc.contributor.committeemember | Williams, Ryan K. | en |
| dc.contributor.committeemember | Headley, William C. | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2018-06-16T08:00:26Z | en |
| dc.date.available | 2018-06-16T08:00:26Z | en |
| dc.date.issued | 2018-06-15 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | With the increasing prevalence of wireless devices in every day life, communicating between them can become more difficult because the devices must contend with each other to send and receive information. Being able to communicate in a variety of environments can be challenging and, while devices can be pre-configured for certain situations, devices that are able to automatically adjust how they communicate are more reliable and robust. The research presented in this thesis will contribute to solving this challenge by considering machine-learning based, radio frequency signal processing algorithms that are able to automatically group different communication signals. Being able to automatically group different signals is helpful because it can provide information about the wireless environment, allowing a device to make intelligent decisions based on what it detects is happening around it. However, before these algorithms can be successfully used in wireless devices, their limitations must be better understood. To this end, the work in this thesis will show how sensitive these algorithms are to imperfections in wireless devices. This work will also show how information from new environments can be captured and manipulated to allow these algorithms to scale for unseen environments and communication signals. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:15986 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/83560 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Machine learning | en |
| dc.subject | Spectrum Sensing | en |
| dc.subject | Neural Networks | en |
| dc.subject | Automatic Modulation Classification | en |
| dc.subject | Communication Systems | en |
| dc.title | Real-World Considerations for Deep Learning in Spectrum Sensing | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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