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Deep radio signal clustering with interpretability analysis based on saliency map

Digital communications and Networks 2024年第5期10卷 1448-1458页

作者： Huaji Zhou Jing Bai Yiran Wang Junjie Ren Xiaoniu Yang Licheng Jiao School of Artificial Intelligence Xidian UniversityXi’an710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033China

With the development of information technology,radio communication technology has made rapid *** radio signals that have appeared in space are difficult to classify without manually *** radio signal clustering methods have recently become an urgent need for this ***,the high complexity of deep learning makes it difficult to understand the decision results of the clustering models,making it essential to conduct interpretable *** paper proposed a combined loss function for unsupervised clustering based on *** combined loss function includes reconstruction loss and deep clustering *** clustering loss is added based on reconstruction loss,which makes similar deep features converge more in feature *** addition,a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency *** experiments have been conducted on a modulated signal dataset,and the results indicate the superior performance of our proposed method over other clustering *** particular,for the simulated dataset containing six modulation modes,when the SNR is 20dB,the clustering accuracy of the proposed method is greater than 78%.The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.

关键词： Unsupervised radio signal clustering Autoencoder Clustering features visualization Deep learning interpretability

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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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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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Practical continuous-variable quantum key distribution with feasible optimization parameters

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science China(information sciences) 2023年第8期66卷 80-91页

作者： Li MA Jie YANG Tao ZHANG Yun SHAO Jinlu LIU Yujie LUO Heng WANG Wei HUANG Fan FAN Chuang ZHOU Liangliang ZHANG Shuai ZHANG Yichen ZHANG Yang LI Bingjie XU Science and Technology on Communication Security Laboratory Institute of Southwestern Communication State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications

Continuous-variable quantum key distribution(CV-QKD) offers an approach to achieve a potential high secret key rate(SKR) in metropolitan areas. There are several challenges in developing a practical CV-QKD system from the laboratory to the real world. One of the most significant points is that it is really hard to adapt different practical optical fiber conditions for CV-QKD systems with unified hardware. Thus,how to improve the performance of practical CV-QKD systems in the field without modification of the hardware is very important. Here, a systematic optimization method, combining the modulation variance and error correction matrix optimization, is proposed to improve the performance of a practical CV-QKD system with a restricted capacity of postprocessing. The effect of restricted postprocessing capacity on the SKR is modeled as a nonlinear programming problem with modulation variance as an optimization parameter,and the selection of an optimal error correction matrix is studied under the same scheme. The results show that the SKR of a CV-QKD system can be improved by 24% and 200% compared with previous frequently used optimization methods theoretically with a transmission distance of 50 km. Furthermore, the experimental results verify the feasibility and robustness of the proposed method, where the achieved optimal SKR achieved practically deviates <1.6% from the theoretical optimal value. Our results pave the way to deploy high-performance CV-QKD in the real world.

关键词： continuous-variable quantum key distribution post-processing optimization secret key rate

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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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Contrastive Clustering for Unsupervised Recognition of Interference Signals

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Computer Systems science & Engineering 2023年第8期46卷 1385-1400页

作者： Xiangwei Chen Zhijin Zhao Xueyi Ye Shilian Zheng Caiyi Lou Xiaoniu Yang The School of Communication Engineering Hangzhou Dianzi UniversityHangzhou310018China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314001China

Interference signals recognition plays an important role in anti-jamming *** the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition ***,there is no unsupervised interference signals recognition algorithm at *** this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is ***,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive *** the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive *** addition,a dynamic entropy parameter strategy is *** simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC *** unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other *** particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 *** addition,our method is close to the supervised learning algorithm.

关键词： Interference signals recognition unsupervised clustering contrastive learning deep learning k-nearest neighbor

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A Defective Ground Structure Design for Reducing Coupling between Microstrip Antenna Arrays

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Progress in Electromagnetics Research C 2025年 151卷 123-130页

作者： Xu, Junhao Wang, Yafei Wang, Xiaozhe Li, Zhensong Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China

In order to reduce the coupling between dense antenna arrays in multiple input multiple output (MIMO) systems, this paper proposes a method to reduce the coupling between microstrip antenna arrays by utilizing a defected ground structure (DGS), which consists of "W"-shaped slits etched in the ground plane. Simulation results show that the structure can achieve −46.6 dB isolation between antenna arrays. To verify the effectiveness of the structure, the microstrip antenna arrays loaded with this DGS are fabricated and measured, and the measured results are consistent with the simulated ones, which verifies the effectiveness of the structure. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Antenna arrays

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Latency minimization for multiuser computation offloading in fog-radio access networks

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Digital communications and Networks 2025年第1期11卷 160-171页

作者： Wei Zhang Shafei Wang Ye Pan Qiang Li Jingran Lin Xiaoxiao Wu School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089China

Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user *** this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog *** UE can select one of the fog nodes to offload its task,and each fog node may serve multiple *** tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul *** order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all *** min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a *** MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM *** addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the *** this model,a similar min-max latency optimization problem is formulated and tackled by the inexact *** results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.

关键词： Fog-radio access network Fog computing Majorization minimization WMMSE

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Modulation technology in Cross-Domain Magnetic Induction communication 14

Modulation Technology in Cross-Domain Magnetic Induction Com...

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14th IEEE International Conference on Signal Processing, communications and Computing, ICSPCC 2024

作者： Cheng, Ruoxue Zhang, Xin Qi, Xiaoming Zhang, Xiaoji Ge, Yizhou School of Marine Science and Technology Northwestern Polytechnical University Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiangxi China

ISBN: (纸本)9798350366556

The research on technology of cross-domain communication, which spans air and sea domains, has been widely concerned. As an emerging cross-domain communication technology, magnetic induction (MI) communication boasts unique advantages such as stable channels, minimal susceptibility to the dynamic ocean environment, and low propagation delay. However, it also has limitations in terms of transmission distance×data rate. In this paper, the modulation technology in cross-domain magnetic induction communication is studied, and the very minimum shift keying (VMSK/2) coding modulation scheme based on direct sequence spread spectrum (DSSS) technology is proposed. VMSK modulation is used to adapt to the severely bandwidth-limited underwater magnetic induction channel, and DSSS technology is used to enhance the detection ability of weak magnetic induction signal, thereby increasing the water entry depth of cross-domain communication. In this paper, the scheme of VMSK/2-DSSS code modulation is given, and the simulation analysis and field trial are carried out. The simulation and lake test results show that compared to magnetic induction communication without DSSS, the VMSK/2-DSSS coding modulation scheme can achieve a greater water entry depth, enabling error-free cross-domain magnetic induction communication at a depth of 40 meters and a data rate of 100bps. © 2024 IEEE.

关键词： Signal modulation

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Joint Task Offloading and Resource Allocation Strategy for Space-Air-Ground Integrated Vehicular Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1027-1043页

作者： Yuanshuo Gang Yuexia Zhang Zhihai Zhuo School of Information and Communication Engineering Beijing Information Science and Technology UniversityBeijing 100101China Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100101China

Space-Air-Ground integrated Vehicular Network(SAGVN)aims to achieve ubiquitous connectivity and provide abundant computational resources to enhance the performance and efficiency of the vehicular ***,there are still challenges to overcome,including the scheduling of multilayered computational resources and the scarcity of spectrum *** address these problems,we propose a joint Task Offloading(TO)and Resource Allocation(RA)strategy in SAGVN(namely JTRSS).This strategy establishes an SAGVN model that incorporates air and space networks to expand the options for vehicular TO,and enhances the edge-computing resources of the system by deploying edge *** minimize the system average cost,we use the JTRSS algorithm to decompose the original problem into a number of subproblems.A maximum rate matching algorithm is used to address the channel allocation and the Lagrangian multiplier method is employed for computational *** acquire the optimal TO decision,a differential fusion cuckoo search algorithm is *** simulation results demonstrate the significant superiority of the JTRSS algorithm in optimizing the system average cost.

关键词： edge computing Resource Allocation(RA) Space-Air-Ground integrated Vehicular Network(SAGVN) Task Offloading(TO)

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