Detection of Incumbent Radar in the 3.5 GHz CBRS Band Using Support Vector Machines

Published

May 16, 2019

Author(s)

Raied Caromi, Michael R. Souryal

Abstract

In the 3.5 GHz Citizens Broadband Radio Service (CBRS), 100 MHz of spectrum will be dynamically shared between commercial users and federal incumbents. Dynamic use of the band relies on a network of sensors dedicated to detecting the presence of federal incumbent signals and triggering protection mechanisms when necessary. This paper uses field-measured waveforms of incumbent signals in and adjacent to the band to evaluate the performance of support vector machines (SVM) classifiers for these sensors. We find that a peak analysis classifier and a higher-order statistics classifier perform comparably when the signal is in white Gaussian noise or commercial long term evolution (LTE) emissions, but with out-of-band emissions of adjacent- band systems the peak analysis classifier is far superior. This result also highlights the importance of including adjacent-band emissions in any performance evaluation of 3.5 GHz sensors.

Proceedings Title

Sensor Signal Processing for Defence (SSPD)

Conference Dates

May 9-10, 2019

Conference Location

Brighton, UK

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/SSPD.2019.8751641

Local Download

Keywords

3.5GHz, CBRS, radar detection, machine learning, sensor

Citation

Caromi, R. and Souryal, M. (2019), Detection of Incumbent Radar in the 3.5 GHz CBRS Band Using Support Vector Machines, Sensor Signal Processing for Defence (SSPD), Brighton, UK, [online], https://doi.org/10.1109/SSPD.2019.8751641, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=927060 (Accessed October 10, 2025)