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



Raied Caromi, Michael R. Souryal


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


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


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],, (Accessed April 21, 2024)
Created May 16, 2019, Updated April 19, 2022