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.
Sensor Signal Processing for Defence (SSPD)
May 9-10, 2019
3.5GHz, CBRS, radar detection, machine learning, sensor