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Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework

China communications 2020年第10期17卷 157-169页

作者： Huaji Zhou Licheng Jiao Shilian Zheng Lifeng Yang Weiguo Shen Xiaoniu Yang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi'an 710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

Generative adversarial network(GAN)has achieved great success in many fields such as computer vision,speech processing,and natural language processing,because of its powerful capabilities for generating realistic *** this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian ***,in many specific scenarios,we can’t obtain enough labeled data,which cause failure of deep learning methods because they are easy to fall into ***,semi-supervised learning(SSL)can leverage the large amount of unlabeled data to enhance the classification performance of classifiers,especially in scenarios with limited amount of labeled *** present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal *** to the characteristics of the electromagnetic signal,we propose a weighted loss function,leading to an effective classifier to realize the end-to-end classification of the electromagnetic *** validate the proposed method on both public RML2016.04c dataset and real-world Aircraft communications Addressing and Reporting System(ACARS)signal *** experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.

关键词： generative adversarial network semi-supervised learning electromagnetic signal classification end-to-end classification weighted loss function

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Deterministic Message Flow Scheduling in Industrial Internet: Message Middleware Use Case

Deterministic Message Flow Scheduling in Industrial Internet...

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International Conference on communication technology (ICCT)

作者： Xin Wang Chonghan Zhou Jing Jiang Key Laboratory of Information and Communication Systems Ministry of Information Industry and Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University (BISTU) Beijing China State Grid Economic and Technological Research Institute co. LTD Beijing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

This article mainly focuses on message flow scheduling for industrial message middleware in the integration of deterministic network with industrial internet, which is to find the optimal matching between telecom operator and message requests from industrial users. However, current deterministic network is limited to two or three layers, and the whole transmission reliability and latency involving industrial networking and service layers cannot be guaranteed, resulting in uncontrollable message scheduling reliability, deterministic latency, low channel resource utilization etc. To this end, an integer linear programming (ILP) model is formulated for message flow scheduling issue, with multi-objectives optimization of resource utilization and profit under customized message sending latencies, and deterministic reliability. A heuristic algorithm is also proposed. Furthermore, we present a Deep Reinforcement Learning based Message Flow Scheduling algorithm (DRL-MFS), which not only solves complex message flow scheduling problems, but also achieves the self-optimization automatically. Proposed message flow scheduling approaches are simulated based on data from real cloud messaging service platforms. Simulations results prove that proposed DRL-MFS is superior to heuristic, and is close to ILP model in multi-objective optimization under three urgency levels of message sending. The reliability of three methods is proven to be deterministic.

关键词： Job shop scheduling Scheduling algorithms Simulation Heuristic algorithms Deep reinforcement learning Telecommunications Telecommunication network reliability Resource management Middleware Optimization

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Scloud+: An Efficient LWE-Based KEM Without Ring/Module Structure 9th

Scloud+: An Efficient LWE-Based KEM Without Ring/Module Stru...

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9th International Conference on security Standardisation Research, SSR 2024

作者： Wang, Anyu Zheng, Zhongxiang Zhao, Chunhuan Qiu, Zhiyuan Zeng, Guang Yuan, Ye Mu, Changchun Wang, Xiaoyun Institute for Advanced Study BNRist Tsinghua University Beijing China School of Computer and Cyber Sciences Communication University of China Beijing China Shield Lab Huawei Technologies Beijing China Shandong Institute of Blockchain Jinan China Digital Currency Institute the People’s Bank of China China Zhongguancun Laboratory Beijing China National Financial Cryptography Research Center Beijing China Key Laboratory of Cryptologic Technology and Information Security School of Cyber Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031875403

We present Scloud+, an LWE-based key encapsulation mechanism (KEM). The key feature of Scloud+ is its use of the unstructured-LWE problem (i.e., without algebraic structures such as rings or modules) and its incorporation of ternary secrets and lattice coding to enhance performance. A notable advantage of the unstructured-LWE problem is its resistance to potential attacks exploiting algebraic structures, making it a conservative choice for constructing high-security schemes. However, a key disadvantage of such schemes is their limited computational and communication efficiency. Scloud+ utilizes ternary secrets and BW32 lattice codes to enhance noise control and ensure robust error correction during decryption, enabling smaller parameters while maintaining low decryption failure probabilities. Equipped with these techniques, Scloud+ exhibits a significant improvement in efficiency. When compared with FrodoKEM for parameter sets targeting 128, 192, and 256 bits of security respectively, Scloud+ achieves practical performance with a public key size approximately 0.71∼0.87x and a ciphertext size approximately 0.56∼0.78x that of FrodoKEM. The encapsulation plus decapsulation time is approximately 0.74∼0.84x that of FrodoKEM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum cryptography

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Design and Implementation of a Scattered Wireless Spectrum Sensor Network System 4

Design and Implementation of a Scattered Wireless Spectrum S...

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4th IEEE International Conference on Electronics technology, ICET 2021

作者： Zhang, Ke Fu, Cheng Niu, Yong Yang, Shuangmao Liu, Jieyan University of Electronic Science and Technology of China Science and Technology on Electronic Information Control Laboratory School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9781728176734

With the increasing shortage of wireless spectrum resources, it is urgent to monitor and utilize the limited wireless spectrum resources rationally. In this paper, a scattered wireless spectrum sensor network system is proposed and applied to electromagnetic spectrum monitoring in a city environment. We design a wireless spectrum sensor node and wireless spectrum sensor network, then develop a city wireless spectrum monitoring software system. We describe the network architecture and software design process in detail and then design several experiments to test the system. The results show that our system can satisfy the electromagnetic spectrum environment monitor requirements. © 2021 IEEE.

关键词： Zigbee

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DEEPCON: Improving Distributed Deep Learning Model Consistency in Edge-Cloud Environments via Distillation

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IEEE Transactions on Cognitive communications and Networking 2025年

作者： Qian, Bin Qian, Jiaxu Wen, Zhenyu Wu, Di He, Shibo Chen, Jiming Ranjan, Rajiv Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China University of St Andrews School of Computer Science United Kingdom Newcastle University United Kingdom

In a typical distributed Deep Learning (DL) based application, models are configured differently to meet the requirements of resource constraints. For instance, a large ResNet56 model is deployed on the cloud server while a small lightweight MobileNet model is more suitable for the end-user device with fewer computation resources. However, the heterogeneity of the model architectures and configurations may bring a systemic problem -models may produce different outputs when given the same input. This inconsistency problem may cause severe system failure of prediction agreement inside the application. Current research has not studied the systemic design for efficiently detecting and reducing the inconsistency among models in distributed DL applications. With the increasing scale of distributed DL applications, the challenges of inconsistency mitigation should consider both algorithm and system design. To this end, we design and implement DEEPCON, an adaptive deployment system across the edge-cloud layer with over-the-air model updates. We implement ASRS sampling for efficiently sampling data to reveal the real data distribution as well as model prediction inconsistency. Then, we implement DMML-Par, an asynchronous parallel training algorithm for quickly updating the models and reducing inconsistency. implements over-the-air updates with a set of APIS to enable seamless inconsistency detection and reduction in such deep learning applications. Our experiment results on both vision and language tasks demonstrate that DMML could improve the model consistency up to 4%, 7%, and 13% at CIFAR10/100 and IMDB datasets without sacrificing the accuracy of individual models. We also show that the ASRS sampling can save 90% network bandwidth of data transmission and that DMML-Par is up to 60% faster compared to simple synchronous parallel training. 2015 IEEE.

关键词： Distributed cloud

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Transformer based Online Continuous Multi-Target Tracking with State Regression

Transformer based Online Continuous Multi-Target Tracking wi...

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International Conference on control, Automation and information sciences (ICCAIS)

作者： Xinwei Wei Linxiu Chen Chenyu Zhang Yiru Lin Linao Zhang Xiaokai Liu Jixiang Jiang Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China

Multi-target Tracking (MTT) is the process of processing received measurements to maintain estimates of the current status of multiple targets, with important applications to autonomous driving, aerial reconnaissance, underwater operations, and others. In the model-based setting, Bayesian filtering can provide the theoretical optimal estimate in a single target scenario. However, in complex situations, uncertain factors such as changes in the number of targets will cause the amount of calculation to increase exponentially, resulting in a decline in tracking accuracy. To solve that problem, model-free methods based on deep-learning provide an attractive alternative, especially the state-of-the-art architecture Transformer based encoder-decoder prediction model, which outperforms the Bayesian filters in the single frame prediction tasks. However, when switching to continuous tracking, these algorithms need to be trained separately frame by frame to adapt to the new tasks. Still, there is no correlation between their predictions from different frames, which prevents them from fully utilizing all the measurements. In this paper, we propose an end-to-end Transformer based MTT method with state autoregression, which allows the model to have the capability of online continuous tracking and make total use of the entire trajectory. The results show that the proposed model is a great extension from single-frame prediction to online continuous tracking.

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Efficient IoT Devices Localization Through Wi-Fi CSI Feature Fusion and Anomaly Detection

arXiv

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arXiv 2024年

作者： Li, Yan Yang, Jie Shih, Shang-Ling Shih, Wan-Ting Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Internet of Things (IoT) device localization is fundamental to smart home functionalities, including indoor navigation and tracking of individuals. Traditional localization relies on relative methods utilizing the positions of anchors within a home environment, yet struggles with precision due to inherent inaccuracies in these anchor positions. In response, we introduce a cutting-edge smartphone-based localization system for IoT devices, leveraging the precise positioning capabilities of smartphones equipped with motion sensors. Our system employs artificial intelligence (AI) to merge channel state information from proximal trajectory points of a single smartphone, significantly enhancing line of sight (LoS) angle of arrival (AoA) estimation accuracy, particularly under severe multipath conditions. Additionally, we have developed an AI-based anomaly detection algorithm to further increase the reliability of LoS-AoA estimation. This algorithm improves measurement reliability by analyzing the correlation between the accuracy of reversed feature reconstruction and the LoS-AoA estimation. Utilizing a straightforward least squares algorithm in conjunction with accurate LoS-AoA estimation and smartphone positional data, our system efficiently identifies IoT device locations. Validated through extensive simulations and experimental tests with a receiving antenna array comprising just two patch antenna elements in the horizontal direction, our methodology has been shown to attain decimeter-level localization accuracy in nearly 90% of cases, demonstrating robust performance even in challenging real-world scenarios. Additionally, our proposed anomaly detection algorithm trained on Wi-Fi data can be directly applied to ultra-wideband, also outperforming the most advanced techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Smartphones

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Thermal camera based on frequency upconversion and its NETD characterization

arXiv

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arXiv 2023年

作者： Ge, Zheng Zhou, Zhi-Yuan Ceng, Jing-Xin Chen, Li Li, Yin-Hai Li, Yan Niu, Su-Jian Shi, Bao-Sen CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

We present a scheme for estimating the noise-equivalent temperature difference (NETD) of frequency upconversion detectors (UCDs) that detect mid-infrared (MIR) light. In particular, this letter investigates the frequency upconversion of a periodically polarized crystal based on lithium niobate, where a mid-infrared conversion bandwidth of 220 nm can be achieved in a single poled period by a special design. Experimentally for a temperature target with a central wavelength of 7.89 μm in mid-infrared radiation, we estimated the NETD of the device to be 56 mK. Meanwhile, a direct measurement of the NETD was performed utilizing conventional methods, which resulted in 48 mK. We also compared the NETD of our UCD with commercially available direct mid-infrared detectors. Here, we showed that the limiting factor for further NETD reduction of our device is not primarily from the upconversion process and camera noise, but from the limitations of the heat source performance. Our detectors have good temperature measurement performance and can be used for a variety of applications involving temperature object identification and material structure detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Temperature measurement

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Ultra-Wideband Spectrum Monitoring Using Software Defined Radio

Ultra-Wideband Spectrum Monitoring Using Software Defined Ra...

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International Conference on communication technology (ICCT)

作者： Huijie Zhu Binhui Chen Yiteng Wang Lijun Wang Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China

This paper presents an ultra-wideband spectrum monitoring system using a software defined radio platform. The system has been developed by using an Ettus X310 universal software radio peripheral (USRP) for data acquisition and a laptop for displaying the graphical user interface. The system can operate in two modes with two different daughter boards installed. One of the operation modes allows simultaneously monitoring up to two different channels, each with a maximum sampling bandwidth of 200 MHz, or simultaneously monitoring up to four different channels, each with a maximum sampling bandwidth of 100 MHz. The other operation mode allows monitoring a single channel with a maximum sampling bandwidth exceeding 300 MHz. Furthermore, unlike the widely adopted GNU Radio software, the proposed system software is friendly to the Microsoft Windows operating system.

关键词： Portable computers Operating systems Data acquisition Bandwidth Monitoring Software radio Ultra wideband technology

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Weak Signal Detection based on Beta Divergence

Weak Signal Detection based on Beta Divergence

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IEEE Workshop on Signal Processing Advances in Wireless communications (SPAWC)

作者： Jiahao Liu Haoxuan Liu Wenqiang Pu Rui Zhou Ming-Yi You Qingjiang Shi Science and Technology on Communication Information Security Control Laboratory Jiaxing China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Software Engineering Tongji University Shanghai China

Among different electromagnetic spectrum monitoring tasks, a fundamental one is detecting the presence of weak communication signal under non-cooperative setting. Various detection methods have been studied in the literature. These detection methods usually construct a proper hypothesis testing metric to distinguish the signal-plus-noise and noise-only signals. information entropy based metric has recently been studied and shown its superiority over traditional energy based metrics. We find that it can be explained as a realization of the general Kullback-Leibler divergence. Inspired by that, in this paper, we propose to detect the signal using a statistic divergence called β-divergence. Different choices of β will lead to several well known divergences, e.g., Itakura-Saito divergence and Euclidean distance. We study its detection performance for non-cooperative signal sources. Both simulation and real experiments demonstrate that β-divergence can improve detection performance for various modulation types of communication signals.

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