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Modulation Recognition of Composite Signal Based on ResNet and Frequency Domain Graph

Journal of Physics: Conference Series 2021年第1期2025卷

作者： Lijun Wang Yu Han Hongfang Ge Yongkuan Zhao Lei Shen Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

Aiming at the problem of composite signal recognition in measurement and control system, a modulation recognition method of composite signal is proposed based on clipped internal modulation signal frequency image and residual network. Residual network has the advantages of fast convergence and high recognition accuracy, which is used to realize the recognition of different modulation modes and different bandwidth signals. The experimental results show that, compared with the traditional decision tree algorithm and the constellation based CNN recognition algorithm, the recognition performance of the proposed method is improved by more than 3dB for the variable bandwidth composite modulation signal set (BPSK-FM and QPSK-FM with internal modulation bandwidth of 128kHz, BPSK-FM and QPSK-FM with internal modulation bandwidth of 2kHz and 2FSK-FM with internal modulation bandwidth of 32kHz and 2kHz).

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Deep Spectrum Prediction in High Frequency communication Based on Temporal-Spectral Residual Network

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China communications 2018年第9期15卷 25-34页

作者： Ling Yu Jin Chen Yuming Zhang Huaji Zhou Jiachen Sun College of Communications Engineering Army Engineering University of PLA Nanjing 210000 China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033 China

High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.

关键词： HF communication deep learning spectrum prediction temporal-spectral residual network

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Quantum proof and high-speed privacy amplification for quantum key distribution

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Physical Review Applied 2025年第5期23卷 054004-054004页

作者： Bing-Qi Yan Jun-Bin Wu Fan Fan Bing-Jie Xu Xiao-Ling Zhang Min Gu Key Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter School of Physics South China Normal University Guangzhou 510006 China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou 510006 China Quantum Science Center of Guangdong-HongKong-Macao Greater Bay Area (Guangdong) Shenzhen 518000 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China National Key Laboratory of Security Communication Institute of Southwestern Communication Chengdu 610041 China

Privacy amplification (PA) is a vital step in the postprocessing stage of quantum key distribution, which can extract a secure key from a source partially leaked to the environment. We propose a PA scheme that uses the square hash (SQH)–modular arithmetic hash (MH) function. In consideration of a potential quantum attacker, we use the trace distance as a metric to measure the closeness between two classical-quantum states. We transform a raw key state to a processed state using this scheme and derive the trace distance between the processed state and the ideal target state. It is proven that the trace distance can be reached as small as desired. Additionally, our approach accelerates the SQH-MH function through the number-theoretic transform, reducing the computational complexity to O(nlog n). On the basis of a CPU, a maximum throughput rate of 1.06 Gbit/s is achieved, and the results show that our algorithm demonstrates good stability of the throughput rate when handling tasks with different compression ratios.

关键词： Number theory

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An Intrusion Prevention Scheme for Malicious Network Traffic Based on SDN

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IOP Conference Series: Materials science and Engineering 2020年第1期790卷

作者： Xiaofeng Xu Jiahao Dai Yifang Zhi Science and Technology on Communication Information Security Control Laboratory China China Electronic Group Corporation NO.36 Institute Zhejiang Jiaxing China Xidian University Shanxi Xi'an China

In recent years, SDN technology has developed rapidly, and the security of SDN is the key to its further development and application. Intrusion prevention system possesses both the features of intrusion detection and protection, which is one of the important methods to ensure the security of SDN network. In this paper, we proposed a SDN-based intrusion prevention scheme for SDN security. The scheme utilizes the programmability of SDN to create four modules on the application plane of the controller, including the network monitoring module, IP address detection module, destructive packets detection and Snort linkage module. The scheme also provides users with Web UI and manual/automatic operation mode. Finally, we deployed the scheme on the Mininet platform with the Floodlight controller.

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SigNet: A novel deep learning framework for radio signal classification

arXiv

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arXiv 2020年

作者： Chen, Zhuangzhi Cui, Hui Xiang, Jingyang Qiu, Kunfeng Huang, Liang Zheng, Shilian Chen, Shichuan Xuan, Qi Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The College of Computer Science and Technology Zhejiang University of Technology Hangzhou China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China The Utron Technology Co. Ltd. [as Hangzhou Qianjiang Distinguished Expert Hangzhou310056 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

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A Novel Localization Method Based on FDA Beam Intersection

A Novel Localization Method Based on FDA Beam Intersection

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International Conference on Computer and communications (ICCC)

作者： Yezi Ma Ping Wei Huaguo Zhang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Communication Information Security Control Laboratory School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

In the past localization methods, both mechanical and phased array radars require angle scanning because their beampatterns are only angle-dependent, resulting in the echo time difference only contains the range information of the target. It will cause some ambiguity problems and limitations. Since Frequency Diverse Array (FDA) was proposed, its range-angle dependent beampattern provided more degrees of freedom (DoFs) to obtain more information in array signal processing. However, how to use these increased DoFs to get more information still needs to be studied. In this paper, we propose a novel localization method using FDA beam intersection. In this proposed method, part of the FDA beampattern approximates a straight line and propagates throughout the range axis. This moving beam line can capture the target like a fast-moving net and the echo time difference contains both the angle and range information of the target. By a simple straight-line intersection, we can obtain the whole position information, where two FDA beam lines intersect at the target. A simulation diagram is provided to illustrate the proposed localization process.

关键词： Radar Array signal processing Frequency diversity Radar antennas Phased arrays Mathematical model Receivers

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On the Secrecy of UAV Systems With Linear Trajectory

arXiv

引用

arXiv 2020年

作者： Pan, Gaofeng Lei, Hongjiang An, Jianping Zhang, Shuo Alouini, Mohamed-Slim School of Information and Electronics Engineering Beijing Institute of Technology Beijing100081 China Thuwal23955-6900 Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Shannxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’anShaanxi710121 China School of Information and Electronics Engineering Beijing Institute of Technology Beijing100081 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing100854 China Beijing Aerospace Automatic Control Institute Beijing100854 China Thuwal23955-6900 Saudi Arabia

By observing the fact that moving in a straight line is a common flying behavior of unmanned aerial vehicles (UAVs) in normal applications, e.g., power line inspections, and air patrols along with highway/streets/borders, in this paper we investigate the secrecy outage performance of a UAV system with linear trajectory, where a UAV (S) flies in a straight line and transmits its information over the downlink to a legitimate receiver (D) on the ground while an eavesdropping UAV (E) trying to overhear the information delivery between S and D. Meanwhile, some information is delivered to S over the uplink from D, such as commanding messages to control S’s detecting operations, which can also be eavesdropped by E. The locations of S, D, and E are randomly distributed. We first characterize the statistical characteristics (including cumulative distribution functions and probability density function) of the received signal-to-noise ratio over both downlink and uplink, and then the closed-form analytical Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A Novel Threat Sensing Architecture for Heterogeneous Cognitive Sensor Networks

A Novel Threat Sensing Architecture for Heterogeneous Cognit...

引用

Signal, information and Data Processing (ICSIDP), IEEE International Conference on

作者： Yongji Ren Yun Zhang Zheng Zhou Xiaofeng Xu Xiaolei Liu Naval Aviation University Yantai China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Yantai Vocational College Yantai China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

With the rapid development and application of novel network information technologies, the various radar and/or communication transmitters have become more intelligent and networked in modern electromagnetic operational environment, which causes the threat sensing architecture of traditional electronic warfare system more inadequate, especially encountering unknown threat signals or networked targets. In this case, the threat sensing becomes one of the biggest challenges that the traditional electronic warfare system is facing. Therefore, this paper proposed a novel heterogeneous architecture for cognitive sensor networks to facilitate efficient threat sensing. To deal with the heterogeneous problem of cognitive sensor networks, multiple constraints satisfaction based approach has been employed. And the game based resources scheduling scheme has been utilized to facilitate the efficiency. To the best of our knowledge, our work is the first one that introduces the heterogeneous cognitive architecture into threat sensing domain of electronic warfare system. The simulation results show the proposed novel threat sensing architecture for heterogeneous cognitive sensor networks is feasible and effective.

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Sensing Matrix Design for MMV Compressive Sensing: An MVDR Approach

Sensing Matrix Design for MMV Compressive Sensing: An MVDR A...

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作者： Zhang, Liang Huang, Lei Li, Bo Yin, Jingwei Bao, Weimin School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Nuance Communication Inc. MontrealQCH3A 3S7 Canada Key Laboratory of Marine Information Acquisition and Security Ministry of Industry and Information Technology Harbin150001 China Science and Technology Council China Aerospace Science and Technology Cooperation Beijing100048 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

Compressive sensing (CS) has been widely used in vehicular technology including compressive spectrum sensing, sparse channel estimation, and vehicular communications. The complete procedure of CS consists of sparse representation, sparse measurement, and sparse recovery. In the CS, measurement matrix Φ is utilized to sample a sparse signal, while sensing matrix Ψ is exploited for sparse recovery. It is important to properly design the sensing matrix that significantly affects the signal recovery performance. This paper addresses the issue of the sensing matrix design for the CS with multiple measurement vectors (MMV). We first study sufficient conditions for the MMV-CS and then develop three sensing matrix design approaches in different situations. The proposed methods have their roots in the principle of minimum variance distortionless response (MVDR) beamformer. This allows us to optimally determine the sensing matrix in the framework of the MVDR, which amounts to minimizing local cumulative cross-coherence between Φ and Ψ while keeping the columns of Ψ highly correlated to their counterparts of Φ. Simulation results are presented to verify the effectiveness of the proposed methods. © 1967-2012 IEEE.

关键词： Compressed sensing

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The Method to Recognize Linear Block Code Based on the Distribution of Code Weight 10

The Method to Recognize Linear Block Code Based on the Distr...

引用

10th International Conference on communication Software and Networks, ICCSN 2018

作者： Lei, Wang Yizhou, Ge Fucai, Zhao Yong, Wang Science and Technology on Communication Information Security Control Laboratory Jiaxing China Key Laboratory of Electronic Restriction Anhui Province National University of Defense Technology Hefei China

ISBN: (纸本)9781538672235

The definition and matrix description of linear cyclic code is introduced in this paper first. Then the distribution of code weight is analyzed. The distance of code weight distribution is defined according to distance of vectors. The theoretic probability of code weight of random sequence is ratiocinated. The method to estimate code weight distribution of actual sequence is established. The code length and start are estimated by the distance between the code weight distribution and the check matrix and generation matrix are estimated by Gauss Elimination. The results of simulating experiment to different length code show that this method can recognize code with middle and short code length effectively when BER is 10-3. © 2018 IEEE.

关键词： Matrix algebra

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