Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic infrastructure. Meanwhile, Sparse Tensor Factorization (STF) is a useful tool for dimension reduction to analyze High-Order, High-Dimension, and Sparse Tensor (HOHDST) data which is transmitted on 5G Internetof-things (IoT). Hence, HOHDST data relies on STF to obtain complete data and discover rules for real-time and accurate analysis. From another view of computation and data security, the current STF solution seeks to improve the computational efficiency but neglects privacy security of the IoT data, e.g., data analysis for network traffic monitor system. To overcome these problems, this paper proposes a Multiple-strategies Differential Privacy framework on STF (MDPSTF) for HOHDST network traffic data analysis. MDPSTF comprises three Differential Privacy (DP) mechanisms, i.e., ε− DP, Concentrated DP (CDP), and Local DP (LDP). Furthermore, the theoretical proof of privacy bound is presented. Hence, MDPSTF can provide general data protection for HOHDST network traffic data with high-security promise. We conduct experiments on two real network traffic datasets (Abilene and G ÈAN T ). The experimental results show that MDPSTF has high universality on the various degrees of privacy protection demands and high recovery accuracy for the HOHDST network traffic data.
Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.
In signal communication based on a non-cooperative communication system, the receiver is an unlicensed third-party communication terminal, and the modulation parameters of the transmitter signal cannot be predicted in advance. After the RF signal passes through the RF band-pass filter, low noise amplifier, and image rejection filter, the intermediate frequency signal is obtained by down-conversion, and then the IQ signal is obtained in the baseband by using the intermediate frequency band-pass filter and down-conversion. In this process, noise and signal frequency offset are inevitably introduced. As the basis of subsequent analysis and interpretation, modulation recognition has important research value in this environment. The introduction of deep learning also brings new feature mining tools. Based on this, this paper proposes a signal modulation recognition method based on multi-feature fusion and constructs a deep learning network with a double-branch structure to extract the features of IQ signal and multi-channel constellation, respectively. It is found that through the complementary characteristics of different forms of signals, a more complete signal feature representation can be constructed. At the same time, it can better alleviate the influence of noise and frequency offset on recognition performance, and effectively improve the classification accuracy of modulation recognition.
We conclude that the low-pass filter effect associated with the fat saturation technique is responsible for dramatically increased motion artifacts.
Aiming at the search of the underwater acoustic beacons used in aircraft or ship parameter recorders, this paper mainly introduces the underwater acoustic beacon detection and location method of the short baseline array system. According to the signal form of underwater acoustic beacon, this paper discusses the basic algorithm and the engineering algorithm applied in this system. In theory, the basic algorithm is a necessary algorithm for short baseline array to calculate target azimuth, which including the two-element linear array direction measurement algorithm, two-direction cross-location algorithm and the virtual direction discrimination algorithm. In addition, according to the experience of offshore operations, this paper introduces the engineering practice methods such as the angle measurement, the data extraction, the location ray screening and the location ray starting point coordinate calculation. The combination of engineering algorithm and basic algorithm can effectively reduce the impact of the test equipment and environment, and improve the accuracy of underwater acoustic beacon detection and position, which has been tested in actual operation at sea.
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