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5G Ultra-Dense Network Fingerprint Positioning Method Based on Matrix Completion

China communications 2023年第3期20卷 105-118页

作者： Yuexia Zhang Chong Liu Key Laboratory of Information and Communication Systems Ministry of Information IndustryBeijing Information Science and Technology UniversityBeijing 100101China Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100101China

The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching ***,a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint ***,a fingerprint database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint ***,a classification weighted K-nearest neighbor fingerprint matching algorithm is *** estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint *** simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.

关键词： indoor positioning fingerprint matching matrix completion 5G UDN RSSI

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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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A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification

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China communications 2024年第11期21卷 88-103页

作者： Zhou Xiaoyu Qi Peihan Liu Qi Ding Yuanlei Zheng Shilian Li Zan State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China The No.011 Research Center Science and Technology on Communication Information Security Control LaboratoryJiaxing 314033China

With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art ***,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental ***,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be *** this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification *** proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance *** the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes *** results prove that the proposed method is superior to other finetuning methods and retrain methods.

关键词： deep learning few-shot label propagation modulation classification semi-supervised learning

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Teachers cooperation:team-knowledge distillation for multiple cross-domain few-shot learning

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Frontiers of Computer science 2023年第2期17卷 91-99页

作者： Zhong JI Jingwei NI Xiyao LIU Yanwei PANG School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China Tianjin Key Laboratory of Brain-Inspired Intelligence Technology Tianjin 300072China

Although few-shot learning(FSL)has achieved great progress,it is still an enormous challenge especially when the source and target set are from different domains,which is also known as cross-domain few-shot learning(CD-FSL).Utilizing more source domain data is an effective way to improve the performance of ***,knowledge from different source domains may entangle and confuse with each other,which hurts the performance on the target ***,we propose team-knowledge distllation networks(TKD-Net)to tackle this problem,which explores a strategy to help the cooperation of multiple ***,we distill knowledge from the cooperation of teacher networks to a single student network in a meta-learning *** incorporates task-oriented knowledge distillation and multiple cooperation among teachers to train an efficient student with better generalization ability on unseen ***,our TKD-Net employs both response-based knowledge and relation-based knowledge to transfer more comprehensive and effective *** experimental results on four fine-grained datasets have demonstrated the effectiveness and superiority of our proposed TKD-Net approach.

关键词： cross-domain few-shot learning meta-learning knowledge distillation multiple teachers

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A Review of Intelligent Configuration and Its security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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Research on privacy information retrieval model based on hybrid homomorphic encryption

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Cybersecurity 2024年第4期7卷 52-61页

作者： Wei-tao Song Guang Zeng Wen-zheng Zhang Dian-hua Tang Science and Technology on Communication Security Laboratory.Chengdu 610041 China Zhejiang University Hangzhou 311200China PLA SSF Information Engineering University Zhengzhou 450000China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

The computational complexity of privacy information retrieval protocols is often linearly related to database *** the database size is large,the efficiency of privacy information retrieval protocols is relatively *** paper designs an effective privacy information retrieval model based on hybrid fully homomorphic *** assignment method is cleverly used to replace a large number of homomorphic encryption *** the same time,the multiplicative homomorphic encryption scheme is first used to deal with the large-scale serialization in the search,and then the fully homomorphic encryption scheme is used to deal with the remaining simple *** depth of operations supported by the fully homomorphic scheme no longer depends on the size of the database,but only needs to support the single homomorphic encryption scheme to decrypt the circuit *** on this hybrid homomorphic encryption retrieval model,the efficiency of homomorphic privacy information retrieval model can be greatly improved.

关键词： Cryptography Hybrid homomorphic encryption Privacy protection Private information retrieval

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Energy Minimization Oriented Resource Allocation for Relay Assisted NOMA-MEC Networks

Energy Minimization Oriented Resource Allocation for Relay A...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Wang, Qianru Qian, Liping Wu, Yuan Yang, Xiaoniu Zhejiang University of Technology College of Information Engineering Hangzhou China University of Macau State Key Laboratory of Internet of Things for Smart City China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China

ISBN: (纸本)9798350351255

With the growing demand for image transmission, there is a need for solutions that offer low energy consumption and low latency. In this paper, we present a novel relay-assisted system based on non-orthogonal multiple access (NOMA) and mobile edge computing (MEC). Our proposed system compresses images at the device end, decompresses them at either a relay or a cloud server (CS). The primary objective is to minimize system energy consumption under given task delay constraints. Considering that this is a non-convex optimization problem, we solve it by decomposing it into a continuous subproblem and a discrete subproblem. To solve the continuous subproblem, we convexify it by introducing new parameters and change variables to get the optimal the computing power of devices, relay and CS, sampling rate of devices, transmission power of devices and relay. To solve the discrete subproblem, we propose a cross-entropy (CE) algorithm to obtain the optimal decompression decision and subcarrier allocation decision. Simulation results demonstrate the accuracy and effectiveness of our algorithm in optimizing total energy consumption compared to the Linear Interactive and General Optimizer (LINGO) and frequency division multiple access (FDMA) methods. © 2024 IEEE.

关键词： Convex optimization

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Coastal Regions Sea Surface Salinity Retrieval of SMAP MISSION Based on Light Gradient Boosting Model

Coastal Regions Sea Surface Salinity Retrieval of SMAP MISSI...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Song, Haoming Zhang, Yifan Zhang, Biao Lv, Yanfang Zhang, Lanjie Ministry of Information Industry Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems China Ministry of Education Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology China

ISBN: (纸本)9798350375909

Sea surface salinity (SSS) as an important indicator of ocean water circulation, affects global ecology and climate change. However, the accuracy of L-band satellite sea surface salinity retrieval products has been severely affected in coastal regions due to factors such as Radio Frequency Interference (RFI) and Land Sea Continuation (LSC). In this study, coastal regions observational data from the Soil Motion Active Passive (SMAP) satellite, and established the LGBM model for SSS retrieval. The results indicated that LGBM performed excellently in the retrieval of SSS in coastal regions. In comparison to in-situ SSS, the LGBM SSS demonstrated a mean bias (MB) of 0.11psu, a root mean squares error (RMSE) of 0.68psu. This paper also added the bias between LGBM SSS and in situ SSS for secondary correction of LGBM. This correction reduced the MB to 0.03psu, the RMSE to 0.54psu. This study affirms the feasibility of using LGBM for SSS retrieval in coastal regions. Future efforts will focus on integrating more auxiliary data that may influence SSS in coastal regions, aiming to further enhance the precision of the coastal regions' SSS retrieval product. © 2024 IEEE.

关键词： Radio interference

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Energy leakage in OFDM sparse channel estimation:The drawback of OMP and the application of image deblurring

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Digital communications and Networks 2024年第5期10卷 1280-1288页

作者： Gang Qiao Xizhu Qiang Lei Wan Hanbo Jia Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001China Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Underwater Acoustic Communication and Marine Information Technology(Xiamen University) Ministry of EducationXiamen 361005China

In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting ***,we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method,Orthogonal Matching Pursuit(OMP).The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval,which prevents OMP-based methods from achieving better *** overcome this drawback,the image deblurring-based channel estimation method,in which the channel estimation problem is analogized to one-dimensional image deblurring,was proposed to improve the large compensation distance of traditional *** advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.

关键词： Sparse channel estimation Orthogonal matching pursuit(OMP) Orthogonal frequency division multiplexing (OFDM) Linear fitting Image deblurring

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Model-based deep learning for fiber bundle infrared image restoration

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Defence technology（防务技术） 2023年第9期27卷 38-45页

作者： Bo-wen Wang Le Li Hai-bo Yang Jia-xin Chen Yu-hai Li Qian Chen Chao Zuo Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Smart Computational Imaging(SCI)Laboratory Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Science and Technology on Electro-Optical Information Security Control Lab Tianjin300308China

As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual *** to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is *** the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep *** building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated *** results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated *** the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging *** proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.

关键词： Fiber bundle Deep learning Infrared imaging Image restoration

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