This software tool generates simulated radar signals and creates RF datasets. The datasets can be used to develop and test detection algorithms by utilizing machine learning/deep learning techniques for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands. In these bands, the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters. The pulse modulation types for the radar signals and their parameters are selected based on NTIA testing procedures for ESC certification, available at http://www.its.bldrdoc.gov/publications/3184.aspx. Furthermore, the tool mixes the waveforms with interference and packages them into one RF dataset file. The tool utilizes a graphical user interface (GUI) to simplify the selection of parameters and the mixing process. A reference RF dataset was generated using this software. The RF dataset is published at https://doi.org/10.18434/M32116.
Research Topics: Advanced Communications: Wireless (RF)
Subject Keywords: 3.5 GHz, CBRS, LTE, ESC, radar, radio frequency signals, spectrum, machine learning, deep learning, detection
These data are public.
Data and related material can be found at the following locations:
Caromi, Raied M., Souryal, Michael R. (2020), Simulated Radar Waveform and RF Dataset Generator for Incumbent Signals in the 3.5 GHz CBRS Band, National Institute of Standards and Technology, https://doi.org/10.18434/M32229 (Accessed 2023-03-26)
Machine-readable descriptions of this dataset are available in the following formats:
Metrics data is not available for all datasets, including this one. This may be because the data is served via servers external to this repository.