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Since the advent of mobile communication, the growth in demand for wireless communication devices and associated spectrum needs has been unstoppable. As a result, due to limited spectrum availability and historically inefficient management of assigned frequencies, spectrum sharing has steadily grown in importance and become a necessary solution to various capacity constraints. To support new developments in spectrum sharing, research in spectrum monitoring and spectrum utilization have become most valuable. GNU Radio offers a compelling opportunity to quickly develop and prototype new research in spectrum monitoring, sharing, and related radio frequency research that can support future deployments. GNU Radio’s packaged capabilities combined with its compatibility with a multitude of Software Defined Radio (SDR) hardware OEMs allow spectrum sharing research to be conducted nimbly and rapidly. To improve spectrum sharing and management, this research used GNU Radio in conjunction with Ettus USRP SDRs to collect I/Q data across the CU Boulder campus in regular intervals over 4 weeks, to monitor changes in the power levels recorded across 1 indoor and 10 outdoor locations. The results show that a simple sensor consisting of an SDR and a Raspberry Pi is capable of tracking changes in Wi-Fi signal strengths measured in outdoor environments. With calibration and careful hardware design such a platform could also be used for broader spectrum monitoring applications.
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Anomaly Detection with Autoencoders for Spectrum Sharing and Monitoring
Abstract—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.
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- PAR ID:
- 10519014
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 978-1-6654-1066-3
- Page Range / eLocation ID:
- 37 to 42
- Subject(s) / Keyword(s):
- Wireless communication Machine learning algorithms Frequency-domain analysis Machine learning Data collection Classification algorithms Anomaly detection spectrum sharing Wi-Fi LTE.
- Format(s):
- Medium: X
- Location:
- Arlington, VA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CQR54764.2022.9918589
