Research Article
Methodology for Characterizing Spectrum Data by Combining Quantitative and Qualitative Information
@INPROCEEDINGS{10.1007/978-3-030-98002-3_2, author={Vaishali Nagpure and Udayan Das and Cynthia Hood}, title={Methodology for Characterizing Spectrum Data by Combining Quantitative and Qualitative Information}, proceedings={Cognitive Radio Oriented Wireless Networks and Wireless Internet. 16th EAI International Conference, CROWNCOM 2021, Virtual Event, December 11, 2021, and 14th EAI International Conference, WiCON 2021, Virtual Event, November 9, 2021, Proceedings}, proceedings_a={CROWNCOM \& WICON}, year={2022}, month={3}, keywords={Spectrum measurements Knowledge graph Graph database Change detection}, doi={10.1007/978-3-030-98002-3_2} }- Vaishali Nagpure
Udayan Das
Cynthia Hood
Year: 2022
Methodology for Characterizing Spectrum Data by Combining Quantitative and Qualitative Information
CROWNCOM & WICON
Springer
DOI: 10.1007/978-3-030-98002-3_2
Abstract
Wideband spectrum data can provide information on how large portions of the spectrum are being used. Spectrograms are typically used to visualize this data. The interpretation of the spectrogram (e.g., identification of bands and patterns) is left to the user, requires significant domain knowledge and is extremely time consuming. In this paper, we present a methodology for combining quantitative and qualitative information to identify channels and changes in spectrum occupancy. Channel identification and change detection algorithms are applied to real spectrum data collected over several years on two different measurement systems in Chicago. These analyses were then used to formulate queries to a knowledge graph implemented on a neo4j graph database. The results of the queries validated the channel identification and provided validation and explanation of the changes detected. This methodology was tested on measurement data from 470–698 MHz.
