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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Di Chen Gang Xiong Baigang Huang Lijun Wang Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of Cetc Jiaxing China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Institute of Navigation and Sensing Shanghai Jiao Tong University China

ISBN: (纸本)9781665446006

In this paper, a modulated signal recognition method based on feature fusion and ResCNN (FF-ResCNN) under generalized fractal noise background is proposed. Firstly, in the feature extraction and fusion part, considering the robustness, anti-noise and interpretability of the features, the fusion feature vectors of generalized fractal spectrum, instantaneous feature and high-order cumulant feature are constructed. Secondly, in order to suppress the possible gradient and degradation problems, the ResCNN model is used to train and classify the fusion features when the CNN model is used to further collect the deep-level abstract features. Finally, feature extraction, feature fusion and classification experiments are carried out for nine modulated signals under two noise types (GWN and fractal noise). Experimental results show that in the case of generalized fractal noise, when SNR=0dB, the recognition accuracy of signal modulation type reaches 95.42%, which verifies the superiority of the proposed method. At the same time, the simulation analysis is carried out for different feature fusion schemes, which provides a valuable reference for feature combination optimization under different noise environment.

关键词： Degradation Deep learning Conferences Neural networks Modulation Feature extraction Fractals

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RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation Measurement and Classification for ISAC Applications

arXiv

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arXiv 2025年

作者： Li, Yan Yang, Jie Huang, Yixuan Yang, Tao 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 National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Institute of Water Science and Technology Hohai University Nanjing210098 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

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model that leverages multipath and temporal features. Using only 20 seconds of CSI data, RainGaugeNet achieved over 90% classification accuracy in line-of-sight scenarios and over 85% in non-line-of-sight scenarios, significantly outperforming state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel state information

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Research On Multi - Target Data Association and Location Algorithm Based On Passive Multi - Sensor System

Research On Multi - Target Data Association and Location Alg...

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International Conference on Wavelet Active Media technology and information Processing (ICWAMTIP)

作者： Yao Siyi Xu Jianliang Shen Weiguo Wang Li Li Wanchun School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Communication Information Security Control Laboratory Jiaxing PRC

ISBN: (纸本)9781665413657

In this paper, the multi-target positioning research is carried out on the basis of a passive multi-sensor reconnaissance system, which requires an algorithm to solve the problem of multi-target data association and positioning in the presence of false alarms and missed detections (also named non-ideal). This paper proposes a new data association and localization algorithm. This new algorithm adopts the method of hierarchical association of data, achieves data association through coarse association and fine association, and finally deletes redundant targets through backtracking. The simulation results show the effectiveness and stability of the algorithm.

关键词： Location awareness Correlation Simulation Reconnaissance Power system stability Media Stability analysis

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An SDN controller security Cluster Scheme Based on Intrusion Detection technology

An SDN Controller Security Cluster Scheme Based on Intrusion...

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作者： Xiao-feng XU Jia-hao DAI Gao-lei YANG Science and Technology on Communication Information Security Control Laboratory China Electronic Group Corporation NO.36 Institute Xidian University

Software Defined Network(SDN) is a new network innovation architecture that separates the control layer and the data layer of the *** the SDNs,the control layer is the core of the entire network,and its security is very *** practical applications,the control layer is usually composed of SDN controller *** order to enhance the security of the SDN controller cluster,this paper first studies the development status of the current SDN technology,and then analyzes the current state of the defense technology of the network *** the analysis and summary of the two,we proposed the discovery and isolation scheme of the invaded controller in the cluster environment,and designed the intrusion detection module,control module and defense ***,we implemented the scheme based on Opendaylight controller,and proved the effectiveness of the scheme by experiments.

关键词： Network security Intrusion prevention Software defined network Cluster

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Deep transfer clustering of radio signals

arXiv

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arXiv 2021年

作者： Xuan, Qi Li, Xiaohui Chen, Zhuangzhi Xu, Dongwei Zheng, Shilian Yang, Xiaoniu The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Modulation recognition is an important task in radio signal processing. Most of the current researches focus on supervised learning. However, in many real scenarios, it is difficult and cost to obtain the labels of signals. In this letter, we turn to the more challenging problem: can we cluster the modulation types just based on a large number of unlabeled radio signals? If this problem can be solved, we then can also recognize modulation types by manually labeling a very small number of samples. To answer this problem, we propose a deep transfer clustering (DTC) model. DTC naturally integrates feature learning and deep clustering, and further adopts a transfer learning mechanism to improve the feature extraction ability of an embedded convolutional neural network (CNN) model. The experiments validate that our DTC significantly outperforms a number of baselines, achieving the state-of-the-art performance in clustering radio signals for modulation recognition. © 2021, CC BY.

关键词： Modulation

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Adaptive visibility graph neural network and it's application in modulation classification

arXiv

引用

arXiv 2021年

作者： Xuan, Qi Qiu, Kunfeng Zhou, Jinchao Chen, Zhuangzhi Xu, Dongwei Zheng, Shilian Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Our digital world is full of time series and graphs which capture the various aspects of many complex systems. Traditionally, there are respective methods in processing these two different types of data, e.g., Recurrent Neural Network (RNN) and Graph Neural Network (GNN), while in recent years, time series could be mapped to graphs by using the techniques such as Visibility Graph (VG), so that researchers can use graph algorithms to mine the knowledge in time series. Such mapping methods establish a bridge between time series and graphs, and have high potential to facilitate the analysis of various real-world time series. However, the VG method and its variants are just based on fixed rules and thus lack of flexibility, largely limiting their application in reality. In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AVGNet, by utilizing GNN model DiffPool as the classifier. We then adopt AVGNet for radio signal modulation classification which is an important task in the field of wireless communication. The simulations validate that AVGNet outperforms a series of advanced deep learning methods, achieving the state-of-the-art performance in this task. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

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A USRP-Based Testbed for Dual Mode Passive Radar

A USRP-Based Testbed for Dual Mode Passive Radar

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International Conference on communication technology (ICCT)

作者： Huijie Zhu Lan Zhang Yiteng Wang Libo Shen Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China

ISBN: (纸本)9781665432078

This paper presents a dual mode passive radar with alternative localization mechanisms of time difference of arrival (TDOA)-direction of arrival (DOA) mode and multi-TDOA mode. For demonstration and validation, a high-performance testbed is needed with the characteristics of wide dynamic range, multichannel, time-frequency synchronization and flexible local oscillator mechanism. To realize these features, an Ettus X310 universal software radio peripheral (USRP) is utilized for integration in the passive radar hardware platform. Processing algorithms for the multi-channel signals acquired by the X310 is implemented in real-time with the graphics processing unit (GPU) embedded in a server computer. Outdoor detections of small unmanned aerial vehicle with the dual mode operation of the testbed show a meter-level accuracy.

关键词： Location awareness Passive radar Time-frequency analysis Time difference of arrival Software algorithms Graphics processing units Real-time systems

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Automatic Detection of Morse with Wideband Receiver

Automatic Detection of Morse with Wideband Receiver

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International Conference on communication technology (ICCT)

作者： Hui Cheng Feng Wang Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China

ISBN: (纸本)9781665432078

Morse communication is widely used in the field of land, water and air transportation and military communication, and especially plays a great role in military communication as a necessary means. Morse signals have noise interference inevitably in the process of HF channel transmission. Because of the noise has greatly harmed to human body, it increases the Labor intensity of copy personnel, especially for that long time engaged in HF Morse copy work. To solve the problem that receiving of Morse signals requires manual operation, this paper presents a solution to automatically detect Morse signals with a wideband receiver. The solution features equipment miniaturization, enables Morse signals to be received in full frequency and time domain and gets rid of manual work. It also allows real-time access to the situation of HF Morse signals.

关键词： Military communication Radio frequency Time-frequency analysis Sensitivity Receivers Real-time systems Personnel

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Performance Analysis of Underwater Acoustic Automatic Modulation Classification System with Machine Learning

Performance Analysis of Underwater Acoustic Automatic Modula...

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International Conference on communication technology (ICCT)

作者： Lan Zhang Huijie Zhu Wei Sun Qing Zhou Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institution of China Electronics Technology Group Corporation Jiaxing China

ISBN: (纸本)9781665432078

Underwater acoustic (UWA) channel is a triple-selective fading channel in time, space and frequency as a result of its lower sound speed and its complicated boundaries in the ocean. It is very difficult to realize a successful classification of the communication signal modulation within a single feature in such harsh channel. The feature extraction is much more sensitive to the environmental parameters and signals. In this paper, an automatic modulation classification (AMC) system based on machine learning is established in order to improve the correction rate of AMC for the several common modulation types. In the case of extracting the feature, the machine learning algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Classification and Regression Tree (CART) are adopted. The performance comparison of these three algorithms is implemented through the simulation data analysis, which would denote how the UWA multipath channel, the signal noise ratio (SNR) affects the performance of the proposed system.

关键词： Support vector machines Machine learning algorithms Baseband Modulation Machine learning Feature extraction Classification algorithms

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IoT ETEI: End-to-End IoT Device Identification Method

IoT ETEI: End-to-End IoT Device Identification Method

引用

IEEE Conference on Dependable and Secure Computing

作者： Feihong Yin Li Yang Yuchen Wang Jiahao Dai Xidian University Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

The past decades have seen the rapid development of Internet of Things (IoT) in various domains. Identifying the IoT devices connected to the network is a crucial aspect of network security. However, existing work on identifying IoT devices based on manually extracted features and prior knowledge, leading to low efficiency and identification accuracy. In this paper, we propose an automatic end-to-end IoT device identification method (IoT ETEI) based on CNN+BiLSTM deep learning model, which outperforms traditional methods from the perspective of overhead and identify accuracy. We demonstrate the effectiveness and flexibility of the proposed method by deploying IoT ETEI in the face of identifying IoT devices on public datasets with the accuracy rate over 99 %, even for IoT devices that use encryption protocols.

关键词： Deep learning Telecommunication traffic Feature extraction Encryption Object recognition Internet of Things security

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