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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Dai, Jiahao Chen, Yongqun Chen, Yucheng Meng, Aodong Science and Technology on Communication Information Security Control Laboratory China China Electronic Group Corporation NO.36 Institute Zhejiang Jiaxing China

The explosive growth of network traffic and the limited storage node resources make the existing network architecture bear great pressure, and network congestion appears in work and life from time to time. In order to ensure the timeliness and effectiveness of the network, we propose a congestion control method based on transmission power. Different from previous work, we built a multi-layer cellular network consisting of multiple macro stations, small base stations and mobile terminals. On this basis, we establish a congestion control model based on the mixed queuing model of service window and calculate the input/output rate and congestion. Finally, we use simulation to verify the effectiveness of the congestion model. The network builder can use our model to set the appropriate power of macro stations and small base stations to ensure network stability. © Published under licence by IOP Publishing Ltd.

关键词： Base stations

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Simultaneous Localization and Recognition of Subwavelength Non-Cooperative Entities Based on SISO Time Reversal and Neural Networks

arXiv

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arXiv 2024年

作者： Wang, Yinchen Duan, Yu Ye, Yuqi Wang, Ren Li, Biao Jiang, Bin Liu, Xin Wang, Bingzhong Institute of Applied Physics University of Electronic Science and Technology of China Chengdu611731 China Communication Information Security Control Laboratory No. 36 Research Institute of CETC Jiaxing314033 China

The simultaneous localization and recognition of subwavelength non-cooperative entities within complex multi-scattering environments using a simplified system continues to pose a substantial challenge. In this paper, we address this challenge by synergistically integrating time reversal time-frequency phase prints (TRTFPPs) and neural networks. Initially, a time reversal (TR) single-input single-output (SISO) framework is employed to generate TRTFPPs. To enhance the models’ adaptability, particularly in the presence of noise, data augmentation techniques are applied. Subsequently, neural networks are employed to comprehend the TRTFPPs. Specifically, a cascaded neural network structure is embraced, encompassing both a recognition neural network and distinct neural networks for localizing different entities. Through the devised approach, two types of subwavelength entities are successfully identified and precisely localized through numerical simulations and experimental verification in laboratory environment. The proposed methodology holds applicability across various electromagnetic systems, including but not limited to detection, imaging, human-computer interaction, and the Internet of Things (IoT). Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

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OS-SEI: Open-Set Specific Emitter Identification Based on Outlier Exposure and Label Smoothing

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IEEE Internet of Things Journal 2025年

作者： Wang, Juzhen Wang, Hanhong Wang, Wei Zha, Haoran Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China Binjiang Institute of Artificial Intelligence of Zhejiang University of Technology Hangzhou310052 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China Harbin Engineering University College of Information and Communication Engineering Harbin150009 China

Specific Emitter Identification (SEI), based on the inevitable hardware imperfections of emitters, plays a crucial role in physical layer security, independent of upper-layer cryptographic methods. In recent years, SEI combined with deep learning has demonstrated exceptional performance and considerable potential. However, most SEI methods are designed for closed, independent identically distributed environments, which contrasts with real-world open scenarios. This discrepancy leads to significant security vulnerabilities when deploying models in practical applications. To address this challenge, we propose a novel Open-Set SEI (OS-SEI) framework, Outlier Exposure with Label Smoothing (OELS), which utilizes partial exposure to auxiliary anomalous data during the training phase and introduces specific loss functions to mitigate the open-set risk. By constructing smooth decision boundaries through label smoothing and applying thresholds to class confidence, our SEI framework demonstrates robust open-set authentication capabilities. Extensive experiments conducted on ADS-B and WiFi datasets show that the OELS method achieves outstanding open-set recognition performance, with F1 scores of 83.77% and 93.14% at an openness level of 0.10557. Furthermore, ablation experiments confirm the effectiveness and potential of our framework’s components. By increasing the diversity of auxiliary unknown data, we further improve performance in OS-SEI tasks. © 2014 IEEE.

关键词： Hardware security

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Identifying driver genes in cancer based on Pareto optimality consensus

Identifying driver genes in cancer based on Pareto optimalit...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Deng, Zheng Wu, Jingli Chen, Xiaorong Li, Gaoshi Guangxi Normal University College of Computer Science Information Technology Guilin China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Wuzhou University Guangxi Key Laboratory of Machine Vision Intelligent Control Wuzhou China

ISBN: (纸本)9781665468190

An important issue in cancer genomics is the identification of driver genes. It is significant for the discovery of key biomarkers and the development of effective personalized therapies. In this paper, a computated method PGScore is proposed. It scores genes at multilayer and integrates the scores to identify cancer driver genes based on Pareto Optimality Consensus(POC) strategy. PGScore uses random walks to reevaluate gene mutations, and integrates differential expression of mRNA and miRNA in normal and cancer samples. It measures the centrality of the gene in the network according to the weight of its direct and indirect neighbors, and finally integrates the above layers to get the final priority of the genes. We compare PGScore with state-of-the-art cancer driver genes prioritization methods on two real cancer datasets. The results show that PGScore can obtain better performance in identification accuracy and the partial area under the ROC(pAUC) curve on multiple reference databases. © 2022 IEEE.

关键词： Gene expression

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Subgraph Networks Based Contrastive Learning

arXiv

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arXiv 2023年

作者： Wang, Jinhuan Shao, Jiafei Wang, Zeyu Yu, Shanqing Xuan, Qi Yang, Xiaoniu 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 Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to generate favorable graph representations for downstream tasks. Most existing GCL methods focus on the design of graph augmentation strategies and mutual information estimation operations. Graph augmentation produces augmented views by graph perturbations. These views preserve a locally similar structure and exploit explicit features. However, these methods have not considered the interaction existing in subgraphs. To explore the impact of substructure interactions on graph representations, we propose a novel framework called subgraph network-based contrastive learning (SGNCL). SGNCL applies a subgraph network generation strategy to produce augmented views. This strategy converts the original graph into an Edge-to-Node mapping network with both topological and attribute features. The single-shot augmented view is a first-order subgraph network that mines the interaction between nodes, node-edge, and edges. In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information. We compare SGNCL with classical and state-of-the-art graph contrastive learning methods on multiple benchmark datasets of different domains. Extensive experiments show that SGNCL achieves competitive or better performance (top three) on all datasets in unsupervised learning settings. Furthermore, SGNCL achieves the best average gain of 6.9% in transfer learning compared to the best method. Finally, experiments also demonstrate that mining substructure interactions have positive implications for graph contrastive learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Breather dynamics in an optomechanical array with staggered coupling

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Physical Review A 2025年第3期111卷 033502-033502页

作者： Yuan Zhao Yiheng Hou Yu Wang Zhuo Fan Wei Peng Qin Zhou Siliu Xu Shihua Chen Key Laboratory of Optoelectronic Sensing and Intelligent Control Hubei University of Science and Technology Xianning 437100 China School of Biomedical Engineering and Imaging Xianning Medical College Hubei University of Science and Technology Xianning 437100 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning 437100 China Research Center of Nonlinear Science School of Mathematics and Physical Sciences Wuhan Textile University Wuhan 430200 China School of Physics and Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing 211189 China

We present an approach toward generating highly controllable breathers, using an array of staggered optomechanical cavities that feature alternating coupling strengths. Four different types of breather states as well as stable two-soliton states are demonstrated to form in such an optomechanical array, thanks to the interplay between the photon-hopping induced tunneling effect and the photon-phonon coupling in the optomechanical cell. It is seen that both their photonic and phononic wave components manifest quasi-periodic oscillatory patterns and coherent localization. We unveil that such breather dynamics can be well controlled by adjusting the coupling strength, the cavity field decay, and the number of array cells. As a result, we observe an unusual odd-even lattice effect in that stable breathers are only available when the cell number is even. We anticipate that these findings from the staggered optomechanical configuration may expand the realm of cavity optomechanics, yielding promising applications such as on-chip manipulation of light propagation.

关键词： Solitons

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Adversarial Examples Detection of Electromagnetic Signal Based on GAN

Adversarial Examples Detection of Electromagnetic Signal Bas...

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International Conference on Big Data Computing and Communications (BIGCOM)

作者： Jiawei Zhu Jiangpeng Li Dongwei Xu Chuntao Gu Qi Xuan Shunling Wang Security Control Laboratory Science and Technology on Communication Information Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology College of Information Engineering Zhejiang University of Technology Hangzhou China

In the field of signal modulation classification, deep neural networks (DNNs) perform very well and have good generalization ability. However, the DNN model is extremely vulnerable. The model gives a false output with high confidence from an input example, which formed only by deliberately adding tiny disturbances. Such elaborate examples that make the model misclassify are called adversarial examples. In this paper, for the detection of adversarial examples in electromagnetic signals, we propose a method based on generative adversarial networks (GANs), a novel strategy to defend deep neural networks against such attacks. First of all, by experiment we came to the conclusion that the confidence of the DNN model for normal examples is stable, and the confidence for adversarial examples is unstable. We trained a classifier that performs well in extracting electromagnetic signal features. Then, We feed the normal examples to the generative adversarial network, the generator generates examples with similar data distribution to the clean examples. We send such pairs of examples to the classifier to calculate a threshold T, and the same operations will be performed on the test examples. Finally, the absolute error E of each pair of test examples will be calculated and compared with the threshold T to achieve electromagnetic signal adversarial examples detection. We define the method in the paper as Detect-GAN. The experimental results show that Detect-GAN can effectively defend against common and powerful attack algorithm. Comparing with other detection methods, Detect-GAN achieves more competitive performance.

关键词： Deep learning Neural networks Modulation Big Data Generative adversarial networks Feature extraction Generators

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Blind identification of MIMO Space-Time Block Codes Based on Convolutional Neural Network 21

Blind identification of MIMO Space-Time Block Codes Based on...

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Proceedings of the 7th International Conference on Communication and information Processing

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

ISBN: (纸本)9781450385190

Aiming at the blind identification of space-time block codes (STBC) in multiple input multiple output (MIMO) system, this paper proposes a new convolutional neural network (CNN-N) to realize the blind identification. Compared to traditional algorithms using statistical features of received signal, CNN-N can reduce the computation with a higher correct identification rate. Consist of multiple layers with special functions, CNN-N has good generalization ability in complex MIMO channels. The network in this paper can recognize 6 STBC codes include spatial multiplexing signal (SM) and some OSTBC codes. The simulation result shows that this new convolutional neural network can finish STBC identification with a high correct rate even in low SNR by consuming moderate amounts of time.

关键词： Generalization Ability Convolutional Neural Network Blind Identification STBC MIMO

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Learning-Driven Transmission Latency Minimization in EH-Relay Assisted IoT Networks

Learning-Driven Transmission Latency Minimization in EH-Rela...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Qianru Wang Li Ping Qian Mingqing Li Wei Jiang Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Internet of Things (IoT) is one of the key applications of 5G, and the data transmission is the basis of IoT networks. In this paper, we investigate the data transmission scheme in non-orthogonal multiple access (NOMA) for IoT networks to minimize the transmission latency. In order to improve the communication efficiency between devices and the base station (BS) without more energy consumption, we deploy an energy harvesting (EH) relay node between devices and the BS for data transmitting and forwarding. Based on this networking model, we first aim at minimizing the transmission latency by jointly optimizing the transmit power of devices and the relay, forwarding ratios among devices, and forwarding time fraction when transmitting a fixed data bits from devices to the BS via the relay under the constraints of energy buffer and data buffer. Noted that the formulated problem is discrete-continuous mixed and non-convex, we apply the deep deterministic policy gradient (DDPG) algorithm in the framework of bisection searching to obtain the optimal solution. Specifically, the bisection searching is used to seek the possible transmission latency, and the DDPG is to check the feasibility of the chosen transmission latency. Finally, the effectiveness of the proposed model-data-driven algorithm is verified by comparing it with other benchmark algorithms, such as LINGO.

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Energy Minimization with Secrecy Provisioning in Federated Learning-Assisted Marine Digital Twin Networks

Energy Minimization with Secrecy Provisioning in Federated L...

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IEEE International Conference on Communications (ICC)

作者： Li Ping Qian Mingqing Li Xinyu Dong Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Digital twin has been emerging as a promising paradigm that connects the physical entities and digital space, and continuously evolves and optimizes the physical systems. In this paper, we focus on studying the efficient data communication and computation when constructing the marine digital twin network with secrecy provisioning. Specifically, we leverage the federated learning (FL) to train the digital twin model. In the process of FL, all unmanned surface vehicles (USVs) deliver the trained models with non-orthogonal multiple access (NOMA) to the high altitude platform (HAP) for the global model aggregation. Considering the possible eavesdropping on the HAP, we utilize the chaotic sequences to spread the model information during the global model broadcasting. In this framework, we further want to minimize the total energy consumption of completing the digital twin training by jointly optimizing the global accuracy, local accuracy, HAP's transmission power, and model uploading duration subject to the secrecy provisioning and latency constraint. Despite the non-convexity, we propose a low-complexity search algorithm (LCS-Algorithm) to solve this joint optimization problem. Finally, the numerical results validate the performance of the proposed algorithm in terms of optimality and time efficiency.

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