Anomaly Detection with Autoencoders for Spectrum Sharing and Monitoring
Stefan Tschimben, Kevin K. Gifford · 2022
Demand for wireless communication devices has been growing continuously since the advent of mobile communication. Even though spectral efficiency and throughput keep increasing, consumer demand continues to seemingly outpace that growth. Spectrum sharing is becoming a more attractive solution to solving various capacity constraints as the resulting perceived spectrum scarcity can mostly be attributed to inefficient spectrum management. However, increasingly complex sharing arrangements come with an increased risk of interference. This makes it necessary to address such events in a timely manner. At the same time, research into using machine learning for solving issues such as signal classification, decision-making processes, and anomaly detection in wireless communication has been growing. To support machine learning research in anomaly detection for wireless communications, this research uses IQ data to train two autoencoders for anomaly detection in shared spectrum: a Long Short-Term Memory (LSTM) and a Deep Autoencoder. These algorithms are used to successfully identify anomalies in the time and frequency domain of recorded IQ data in the form of unauthorized LTE transmissions on top of Wi-Fi communication.