“Seek and Classify: End-to-end joint multi-signal detection and classification using deep learning”

The rise in the use of wireless communication has led to the problem of spectrum scarcity in licensed bands. The popularity of Internet of Things (IoT) requires innovative solutions that maximize the use of available spectrum to support the increasing number of connected devices. This thesis tackles two significant problems in wireless communication: the need for efficient spectrum sensing techniques and the scarcity of large, diverse raw in-phase (I) and quadrature (Q) datasets.

The ability to detect and classify modulation of the signals efficiently can enable cognitive radio to monitor the spectrum activity in real time and utilize unused frequencies. In this work, we propose an end-to-end framework to detect and classify narrowband signals from wideband IQ samples. The end-to-end framework reduces the sensing time for narrowband signals by 10 times. The training strategy proposed in the framework to train modulation classifiers on extracted signals increases the classification accuracy from 41% to 99%.

Given the dynamic nature of wireless channels, comprehensive datasets gathered from various environments are crucial for the success of data-driven solutions. To tackle this issue, we have designed a systematic process to collect and centralize large-sized wireless samples by using the Nebraska Experimental Testbed of Things (NEXTT) testbed. The result is an accumulation of 123 TB of raw IQ samples across a frequency range of 54 to 2,590 MHz from three distinct wireless environments.

Committee:
Dr. Mehmet Can Vuran, Chair
Dr. Stephen Scott
Dr. Shuai Nie