基于深度学习的射频指纹(Radio Frequency Fingerprinting, RFF)识别具有增强物理层安全性能的潜力。近年来,为了满足深度学习对大规模数据的需求,提出了几种RFF数据集。然而,这些数据集是从类似的信道环境中收集的,多数仅提供来自接收...基于深度学习的射频指纹(Radio Frequency Fingerprinting, RFF)识别具有增强物理层安全性能的潜力。近年来,为了满足深度学习对大规模数据的需求,提出了几种RFF数据集。然而,这些数据集是从类似的信道环境中收集的,多数仅提供来自接收器的接收数据。针对上述问题,利用软件无线电设备作为无线电信号发生器,通过自定义收发射机系统参数,如频带、调制模式、天线增益等,实现射频信号数据集的个性化定制。由于数据集是通过各种复杂的信道环境生成的,旨在更好地描述现实世界中的射频信号,因此在发射机和接收机处同时收集数据,可以模拟基于长期演进(Long Term Evolution, LTE)的真实RFF数据集。此外,通过一个基于卷积神经网络的射频指纹识别例程,验证了数据集的可用性,所提出的数据集和相关代码均可以在GitHub下载。展开更多
Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate...Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate the prediction parameters of AR modeling. The complex data samples are directly extrapolated to obtain the extrapolated echo data in the frequency domain. The small rotating angle data extrapolation and the large rotating angular data extrapolation are considered separately in azimuth domain. The method of data extrapolation for the small rotating angle is the same as that in frequency domain, while the amplitude samples of large rotating angle echo data are extrapolated to obtain extrapolated echo amplitude, and the complex data of large rotating angle echo samples are extrapolated to get the extrapolated echo phase respectively. The calculation results show that the extrapolated echo data obtained by the above mentioned methods are accurate.展开更多
在线无线射频识别(radio frequency identification,RFID)数据流上的复杂事件处理技术是一个新的课题。现有研究工作仅是针对单一的复杂事件查询,没有考虑多复杂事件同时查询的处理策略。在复杂事件语言SASE(stream-based and shared ev...在线无线射频识别(radio frequency identification,RFID)数据流上的复杂事件处理技术是一个新的课题。现有研究工作仅是针对单一的复杂事件查询,没有考虑多复杂事件同时查询的处理策略。在复杂事件语言SASE(stream-based and shared event processing)的基础上设计了专门针对多查询的自动机及相关的优化技术,解决了RFID数据流上多复杂事件查询的问题。实验结果表明,算法在查询数量较大时,时间与空间上较传统算法有更好的表现。展开更多
文摘基于深度学习的射频指纹(Radio Frequency Fingerprinting, RFF)识别具有增强物理层安全性能的潜力。近年来,为了满足深度学习对大规模数据的需求,提出了几种RFF数据集。然而,这些数据集是从类似的信道环境中收集的,多数仅提供来自接收器的接收数据。针对上述问题,利用软件无线电设备作为无线电信号发生器,通过自定义收发射机系统参数,如频带、调制模式、天线增益等,实现射频信号数据集的个性化定制。由于数据集是通过各种复杂的信道环境生成的,旨在更好地描述现实世界中的射频信号,因此在发射机和接收机处同时收集数据,可以模拟基于长期演进(Long Term Evolution, LTE)的真实RFF数据集。此外,通过一个基于卷积神经网络的射频指纹识别例程,验证了数据集的可用性,所提出的数据集和相关代码均可以在GitHub下载。
文摘Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate the prediction parameters of AR modeling. The complex data samples are directly extrapolated to obtain the extrapolated echo data in the frequency domain. The small rotating angle data extrapolation and the large rotating angular data extrapolation are considered separately in azimuth domain. The method of data extrapolation for the small rotating angle is the same as that in frequency domain, while the amplitude samples of large rotating angle echo data are extrapolated to obtain extrapolated echo amplitude, and the complex data of large rotating angle echo samples are extrapolated to get the extrapolated echo phase respectively. The calculation results show that the extrapolated echo data obtained by the above mentioned methods are accurate.
文摘在线无线射频识别(radio frequency identification,RFID)数据流上的复杂事件处理技术是一个新的课题。现有研究工作仅是针对单一的复杂事件查询,没有考虑多复杂事件同时查询的处理策略。在复杂事件语言SASE(stream-based and shared event processing)的基础上设计了专门针对多查询的自动机及相关的优化技术,解决了RFID数据流上多复杂事件查询的问题。实验结果表明,算法在查询数量较大时,时间与空间上较传统算法有更好的表现。
