Visible to the public Biblio

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2021-04-27
Ding, K., Meng, Z., Yu, Z., Ju, Z., Zhao, Z., Xu, K..  2020.  Photonic Compressive Sampling of Sparse Broadband RF Signals using a Multimode Fiber. 2020 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC). :1–3.
We propose a photonic compressive sampling scheme based on multimode fiber for radio spectrum sensing, which shows high accuracy and stability, and low complexity and cost. Pulse overlapping is utilized for a fast detection. © 2020 The Author(s).
2018-04-11
Huang, Kaiyu, Qu, Y., Zhang, Z., Chakravarthy, V., Zhang, Lin, Wu, Z..  2017.  Software Defined Radio Based Mixed Signal Detection in Spectrally Congested and Spectrally Contested Environment. 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA). :1–6.

In a spectrally congested environment or a spectrally contested environment which often occurs in cyber security applications, multiple signals are often mixed together with significant overlap in spectrum. This makes the signal detection and parameter estimation task very challenging. In our previous work, we have demonstrated the feasibility of using a second order spectrum correlation function (SCF) cyclostationary feature to perform mixed signal detection and parameter estimation. In this paper, we present our recent work on software defined radio (SDR) based implementation and demonstration of such mixed signal detection algorithms. Specifically, we have developed a software defined radio based mixed RF signal generator to generate mixed RF signals in real time. A graphical user interface (GUI) has been developed to allow users to conveniently adjust the number of mixed RF signal components, the amplitude, initial time delay, initial phase offset, carrier frequency, symbol rate, modulation type, and pulse shaping filter of each RF signal component. This SDR based mixed RF signal generator is used to transmit desirable mixed RF signals to test the effectiveness of our developed algorithms. Next, we have developed a software defined radio based mixed RF signal detector to perform the mixed RF signal detection. Similarly, a GUI has been developed to allow users to easily adjust the center frequency and bandwidth of band of interest, perform time domain analysis, frequency domain analysis, and cyclostationary domain analysis.