Real-world Radar and LTE Signals Dataset Collected Over-the-air in Shared CBRS Band

Opening up of the CBRS band for the secondary users' transmissions poses challenges in the protection of incumbent radar users from co-channel interference. The use of Machine Learning algorithms for addressing these challenges requires representative real-world datasets.This datasetcontains overlapping radar and LTE signals captures over-the-air in the shared CBRS band using an experimental testbed composed of software defined radios in RFanechoic chamber.We collected this dataset using our open-source RF data recording API tool.  开放公民宽带无线电服务（Citizens Broadband Radio Service, CBRS）频段供次级用户开展传输，给保护在位雷达用户免受同信道干扰带来了挑战。若要采用机器学习算法解决此类频谱干扰挑战，则需要具备代表性的真实世界数据集。本数据集通过由软件定义无线电（Software Defined Radios, SDR）搭建的实验测试平台，在射频暗室的共享CBRS频段内完成了重叠雷达与长期演进（Long Term Evolution, LTE）信号的空中无线采集。我们通过自研的开源射频数据记录应用程序编程接口（Application Programming Interface, API）工具完成了本数据集的采集工作。

搜集汇总

背景与挑战

背景概述

该数据集包含在共享CBRS频段中通过实验平台采集的真实世界雷达和LTE信号，用于机器学习模型开发以应对干扰检测和频谱共享挑战。数据集采用SigMF格式，包含5640个样本，覆盖重叠和非重叠场景及宽SINR范围，总大小为55.5GB。