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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： Tianyi, Liang Huijie, Zhu Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China No.36 Research Institute of China Electronics Technology Group Corporation Jiaxing314033 China

ISBN: (纸本)9781665441582

In the 5th Generation Mobile communication technology (5G) system, physical downlink control channel (PDCCH) controls the transmission and reception of uplink and downlink data by transmitting Downlink control information (DCI), which is the core of the physical channel control of the 5G system. Obtain the DCI information to obtain the uplink and downlink scheduling information of the signal, thereby providing more possibilites for effectively interfering with the mobile signal. In this paper, by studying the signal transmitting end, the corresponding resource demapping, deinterleaving, decoding and other algorithms are used to realize the full link processing of the receiving end and obtain DCI information. The actual 5G signal collected is processed, which verifies the reliability and accuracy of the algorithm. © 2021 IEEE.

关键词： 5G mobile communication systems

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Sub-Sample High-Resolution TOA Estimation Algorithm for LTE Downlink 9

Sub-Sample High-Resolution TOA Estimation Algorithm for LTE ...

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2017 9th IEEE International Conference on communication Software and Networks (ICCSN 2017)

作者： Huijie Zhu Jiawei Zhu Bin Jiang Science and Technology on Communication Information Security Control Laboratory No. 36 Research Institute of China Electronics Technology Group Corporation

ISBN: (纸本)9781509038237;9781509038220

This paper presents a novel super-resolution time of arrive(TOA) estimation algorithm, which utilizes the primary synchronization signal(PSS) in LTE downlink. Unlike the conventional coherent detection algorithm with sample-level estimation accuracy, the proposed algorithm fully takes advantage of phase characteristic of the sampled orthogonal frequency division multiplexing(OFDM) signal to reach subsample resolution accuracy without increasing sampling frequency. With a number of simulation experiments, the efficacy of the proposed TOA estimation algorithm is validated by numerical results. The different performance shown in additive white Gaussian noise(AWGN) channel and fading channel indicates that this algorithm can be used in open environment or indoors with high-precision positioning requirements.

关键词： component LTE TOA estimation algorithm OFDM

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Open World Recognition of communication Jamming Signals

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China communications 2023年第6期20卷 199-214页

作者： Yan Tang Zhijin Zhao Jie Chen Shilian Zheng Xueyi Ye Caiyi Lou Xiaoniu Yang School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is *** algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new *** network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known *** the one hand,the network is required to have the ability to distinguish whether two samples are from the same *** the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set *** the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known *** simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.

关键词： communication jamming signals Siamese Neural Network Open World Recognition unsupervised clustering of new jamming type Gaussian probability density function

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Research on Space-Time Block Code technology in MIMO System 7

Research on Space-Time Block Code Technology in MIMO System

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7th International Conference on Computer and communications, ICCC 2021

作者： Pan, Conglin Chen, Si Wu, Wei Qian, Jiachuan Wang, Lijun No.36 Research Institute of CETC Science and Technology On Communication Information Security Control Laboratory Jiaxing China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665409506

MIMO technology has been widely used in the telecommunication systems nowadays, and the space-time coding is a key part of MIMO technology. A good coding scheme can exploit the spatial diversity to correct the error which is generated in transmission, and increase the normalized transfer rate with low decoding complexity. On the Basis of the research on different Space-Time Block Codes, this essay proposes a new STBC, Diagonal Block Orthogonal Space-Time Block Code. Then we will compare it with other STBCs in the performance of bit error rate, transfer rate, decoding complexity and peek-to-average power ratio, the final result will prove the superiority of DBOAST. © 2021 IEEE.

关键词： MIMO systems

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High Precision Translational Compensation for Ultrahigh Resolution ISAR 13

High Precision Translational Compensation for Ultrahigh Reso...

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13th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2021

作者： Yang, Lichao Science and Technology on Communication Information Security Control Laboratory No. 36 Research Institute of CETC China

ISBN: (纸本)9781665418126

In recent years, due to the emergence of ultrahigh resolution (UHR) radar systems, the bandwidth of radar has been increasing. However, conventional imaging algorithms have difficulties in the translational compensation of UHR inverse synthetic aperture radar (ISAR) data with high order range cell migration (RCM) and phase error. In this paper, a high-precision translational compensation method based on power flatness optimization (PFO) is proposed. The algorithm constructs the optimal criterion of power flatness and ensure the complete extraction of the prominent point energy. Then, the original data is compensated in the two-dimensional data domain through the prominent point phase inversion matrix. The results show that the proposed algorithm has significantly improved accuracy in the translational compensation of ultrahigh-resolution ISAR imaging compared with traditional algorithms. © 2021 IEEE.

关键词： Inverse synthetic aperture radar

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Minimum dispersion beamforming using modified conjugate gradient method

Minimum dispersion beamforming using modified conjugate grad...

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2021 International Conference on Electronic information Engineering and Computer communication, EIECC 2021

作者： Song, Jinyang Wang, Wei Chen, Yan Science and Technology on Communication Information Security Control Laboratory China Cetc 36 Zhejiang Jiaxing China

ISBN: (数字)9781510653207

ISBN: (纸本)9781510653191

In order to solve linear constraint minimize dispersion problem in beamforming with non-Gaussian signals or noise, this paper proposed a real time adaptive beamforming technique based on modified conjugate gradient (MCG) method. Compared to conventional adaptive method, MCG have faster convergence rate than stochastic gradient (SG) method, and comparable convergence rate with recursive least square (RLS) method with lower complexity. And it simplifies the procedure of conjugate gradient method with one iteration per snapshots by using inexact line search scheme. Simulation has demonstrated the superior performance in output SINR and its convergence rate. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Beamforming

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Few-shot electromagnetic signal classification:A data union augmentation method

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Chinese Journal of Aeronautics 2022年第9期35卷 49-57页

作者： Huaji ZHOU Jing BAI Yiran WANG Licheng JIAO Shilian ZHENG Weiguo SHEN Jie XU Xiaoniu YANG School of Artificial Intelligence Xidian UniversityXidian UniversityXi'an 710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

Deep learning has been fully verified and accepted in the field of electromagnetic signal classification. However, in many specific scenarios, such as radio resource management for aircraft communications, labeled data are difficult to obtain, which makes the best deep learning methods at present seem almost powerless, because these methods need a large amount of labeled data for training. When the training dataset is small, it is highly possible to fall into overfitting, which causes performance degradation of the deep neural network. For few-shot electromagnetic signal classification, data augmentation is one of the most intuitive countermeasures. In this work, a generative adversarial network based on the data augmentation method is proposed to achieve better classification performance for electromagnetic signals. Based on the similarity principle, a screening mechanism is established to obtain high-quality generated signals. Then, a data union augmentation algorithm is designed by introducing spatiotemporally flipped shapes of the signal. To verify the effectiveness of the proposed data augmentation algorithm, experiments are conducted on the RADIOML 2016.04C dataset and real-world ACARS dataset. The experimental results show that the proposed method significantly improves the performance of few-shot electromagnetic signal classification.

关键词： Data union augmentation Electromagnetic signal classification Few-shot Generative adversarial network Screening mechanism

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Enhancing security of the Reduced-Operation Two-Factor Authentication by Using Ambient WiFi

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Chinese Journal of Electronics 2018年第3期27卷 625-633页

作者： LIU Dong CHEN Jing DU Ruiying ZHANG Huanguo Key Laboratory of Aerospace Information Security and the Trusted Computing Ministry of Education School of ComputerWuhan University Science and Technology on Communication Security Laboratory North Automatic Control Technique Research Institute Collaborative Innovation Center of Geospatial Technology

Recently several reduced-operation two Factor authentication(2FA) methods have been proposed to improve the usability of traditional 2FA. The existing works cannot protect the user's password from online guessing attack and offline dictionary attack. They cannot resist the identity fraud attack caused by co-located attackers who have obtained the victim's password. To solve these problems, we provide a Wi Fi-based 2FA approach which can enhance security of the reduced-operation 2FA but not increase the complexity of the operation for a *** analyze our approach's security in terms of identity fraud attack resistance, salt guessing attack resistance, and password guessing attack resistance. We also implement a prototype system and test its performance in various scenarios, e.g. lab, library, and dormitory. The security analysis and experimental results show the effectiveness of our scheme for authentication.

关键词： Two factor authentication(2FA) Proximate authentication Fuzzy vault

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Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

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Computer Modeling in Engineering & sciences 2024年第4期139卷 279-296页

作者： Hui Zhang Huaji Zhou Li Wang Feng Zhou The Ministry Key Laboratory of Electronic Information Countermeasure and Simulation Xidian UniversityXi’an710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033China

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open *** spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center *** designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during ***,this method allows unknown classes to occupy a larger space in the feature *** reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more ***,the feature comparator threshold can be used to reject unknown *** signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world *** experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open ***,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

关键词： Electromagnetic signal recognition deep learning feature extraction open set recognition

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Incremental Learning of Radio Modulation Classification Based on Sample Recall

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China communications 2023年第7期20卷 258-272页

作者： Yan Zhao Shichuan Chen Tao Chen Weiguo Shen Shilian Zheng Zhijin Zhao Xiaoniu Yang School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China Institute of Cyberspace Security Zhejiang University of TechnologyHangzhou 310023China

Radio modulation classification has always been an important technology in the field of *** difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old *** this paper,we propose a sample memory and recall framework for incremental learning of radio modulation *** data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for *** compare the performance of our proposed method with three baselines through a large number of simulation *** show that our method achieves far higher classification accuracy than finetuning method and feature extraction ***,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

关键词： radio modulation classification incremen-tal learning deep learning convolutional neural net-work.

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