Understanding spectrum characteristics with little prior knowledge requires fine-grained spectrum data in the frequency, spatial, and temporal domains; gathering such a diverse set of measurements results in a large data volume. Analysis of the resulting dataset poses unique challenges; methods in the status quo are tailored for specific spectrum-related applications (apps), and are ill equipped to process data of this magnitude. We design BigSpec, a general-purpose framework that allows for fast processing of apps. The key idea is to reduce computation costs by performing computation extensively on compressed data that preserves signal features. Compared with baselines and prior works, we achieve 17× run time efficiency, sublinear rather than linear run time scalability, and extend the definition of anomaly to different domains (frequency & spatio-temporal).
The following figure shows the data analysis pipeline of BigSpec.
For more information, please refer to our paper https://dl.acm.org/citation.cfm?id=3345450
Overall, the raw data is more than 1TB and for each 100MHz band we have more than 50k measurements. Specifically, our data collection process is:
- Wideband: 300MHz to 4GHz on a 100MHz basis
- Fine-grained: 26k energy readings per measurement
- Over a large spatio-temporal scale: ~400km^2 area for a year
All data is collected using a WSA4000 sensor from ThinkRF.
Faculty Member: Suman Banerjee
External Collaborator: Domenico Giustiniano
Students: Yijing Zeng, Varun Chandrasekaran