Spectrum sensing is essential for enabling optimal spectrum usage and ensuring data security, especially with the proliferation of IoT devices. Spectrum sensing involves detecting and characterizing intentional RF emissions called overt, and unintentional RF emissions called emanations. The thesis focuses on two pivotal aspects of spectrum sensing: characterization of overt specifically modulation classification and characterization of emanations.
Three distinct works are presented on modulation classification. DL has been successfully used recently. The focus, however, has been model-centric, with attempts to improve performance on the standard synthetic dataset RML16. The quality of the training dataset impacts model performance on real data. A hybrid approach is taken by leveraging wireless domain knowledge to improve dataset quality. The first two works respectively leverage domain knowledge of wireless channel conditions and SNR to improve dataset quality. Over-the-air (OTA) data captured using USRP radios are used in these works.
In the first work, studies are done to understand the performance impact due to the disparity of probability distribution between training and test data within the context of channel conditions. This is studied for OTA data collected in channels emulating LOS, NLOS, and AWGN. In the second work, signal processing advances in blind SNR estimation are leveraged to improve DL performance on modulation classification. A training methodology is introduced that partitions OTA data into subsets of different SNR levels. For the third work, shortcomings such as errors and ad-hoc choice of parameters are identified in RML16. A new realistic benchmark dataset RML22 is provided with the errors corrected and the choice of parameters justified. Thorough mathematical derivations are provided for the wireless models used to generate data. Performance impact due to artifacts and model parameterization is studied using the RML22 data generation framework.
For the second paradigm of detection and characterization of emanations, an HW agnostic solution is proposed. Prior work focussed on profiling specific HW but scalability led to the need for a HW-agnostic solution. Emanations are detected by scanning for the signature of harmonics from leakages of clock signals. A signal processing algorithm is provided to remove artifacts and estimate the pitch of harmonics that characterizes the emanation. IQ data is collected from the source of emanations placed inside a sanitized shield room using Signal Hound SDR. Results for anomaly detection using emanation patterns are presented for the use cases compromising data security: damaged electronic peripherals, and illegal copying of data to external storage devices.
