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RF Dataset of Incumbent Radar Signals in the 3.5 GHz CBRS Band



Raied Caromi, Michael R. Souryal, Timothy Hall


This Radio Frequency (RF) dataset consists of synthetically generated waveforms of incumbent 3.5 GHz radar systems. The intended use of the dataset is for developing and evaluating detectors for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands where the primary users of the band are Federal radar systems. The dataset can be used for developing and testing radar detection algorithms using machine learning/deep learning techniques. The algorithm aims to detect whether the radar signal is present or absent regardless of the signal type. The target signals have a variety of modulation types and parameters chosen from wide ranges. In addition, the start time and the center frequency of the radar signals are randomized in the waveform. The variety of signals and their random parameters makes the detection problem more challenging when using non- naive (e.g., energy detector is a naive signal detector) classical signal processing techniques.
Journal of Research (NIST JRES) -


3.5 GHz, CBRS, incumbent radar detection, RF dataset, classification, deep learning, machine learning


Caromi, R. , Souryal, M. and Hall, T. (2019), RF Dataset of Incumbent Radar Signals in the 3.5 GHz CBRS Band, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online],, (Accessed June 14, 2024)


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Created December 16, 2019, Updated October 12, 2021