Public Data Resource

Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band

Contact: Raied Caromi..
Identifier: doi:10.18434/mds2-2380
Version: 1.0

Description

This project aims to create a comprehensive framework for generating radio frequency (RF) datasets, designing deep learning (DL) detectors, and evaluating their detection performance using both simulated and experimental test data. The proposed tools and techniques are developed in the context of dynamic spectrum use for the 3.5 GHz Citizens Broadband Radio Service (CBRS), but they can be utilized and expanded for standardization of machine learned spectrum awareness technologies and methods. This dataset consists of pre-trained DL models for radar detection in the CBRS band using simulated waveforms. The code for creating and using these models is available at https://github.com/usnistgov/BaselineDeepLearningRadarDetectors.
Research Topics: Advanced Communications: Wireless (RF)    
Subject Keywords: 3.5 GHz; CBRS; radar; detection; deep learning; radio frequency signals; spectrum; MLSA    

Data Access

These data are public.
Files

Loading file list...

About This Dataset

Version: 1.0
Cite this dataset
Raied Caromi (2021), Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2380 (Accessed 2024-04-21)
Repository Metadata
Machine-readable descriptions of this dataset are available in the following formats:
NERDm
Access Metrics
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.