CommRad RF:用于检测,识别和分类的通信无线电信号的数据集
Muhammad Usama Zahid1, Muhammad Usman Akram2, Muhammad Danish Nisar1
1Department of Electrical Engineering, Sir Syed CASE Institute of Technology, Islamabad 46000, Federal, Pakistan.
Data in brief
|March 19, 2025
概括
这项研究引入了来自无线电话机的无线电信号的新数据集,以提高无线安全性. 它还介绍了两种深度学习模型,用于检测未经授权的传输和分析射频指纹.
科学领域:
- 网络安全 网络安全
- 无线通信无线通信
- 信号处理 信号处理
背景情况:
- 无线技术的进步带来了安全风险,例如未经授权的频谱访问.
- 射频 (RF) 指纹识别对于识别独特的无线设备至关重要.
- 现有的数据集缺乏用于商业无线电 (如无线电话机) 的全面射频指纹数据.
研究的目的:
- 解决无线电收音机射频指纹识别自由可用的数据集的缺口.
- 通过能够检测未经授权的传输来提高无线通信通道的安全性.
- 引入新的深度学习模型来分析复杂的无线电信号数据.
主要方法:
- 在室内多路径环境中从27个商业无线电站收集了超过2700个无线电信号.
- 开发了两个深度学习模型:轻量1DCNN和轻量1DCNN.
- 利用深度学习来有效处理和识别无线电信号数据集中的模式.
主要成果:
- 创建了一个独特的无线电信号数据集,填补了一个关键的研究空白.
- 证明了数据集在分析和检测未经授权的传输方面的潜力.
- 拟议的深度学习模型对高效的射频指纹和信号分析充满希望.
结论:
- 新的数据集和深度学习模型对无线安全和射频指纹研究做出了重大贡献.
- 这项工作有助于制定更强大的安全措施,防止商业无线电通信的未经授权访问.
- 进一步的研究可以利用这个数据集来探索先进的信号分析和设备识别技术.
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