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Sensing With Random communication Signals

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arXiv 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Southern University of Science and Technology Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security and also The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing211189 China

communication-centric Integrated Sensing and communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025, CC BY-SA.

关键词： Signaling

On Performance Loss of DOA Measurement Using Massive MIMO Receiver with Mixed-ADCs

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arXiv 2021年

作者： Shi, Baihua Zhu, Lingling Cai, Wenlong Chen, Nuo Shen, Tong Zhu, Pengcheng Shu, Feng Wang, Jiangzhou School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing100854 China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Information and Communication Engineering Hainan University Haikou570228 China School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

High hardware cost and high power consumption of massive multiple-input and multiple output (MIMO) are two challenges for the future wireless communications including beyond fifth generation (B5G) and sixth generation (6G). Adopting the low-resolution analog-to-digital converter (ADC) is viewed as a promising solution. Additionally, the direction of arrival (DOA) estimation is an indispensable technology for beam alignment and tracking in massive MIMO systems. Thus, in this paper, the performance of DOA estimation with mixed-ADC structure is firstly investigated. The Cramér-Rao lower bound (CRLB) for this architecture is derived based on the additive quantization noise model. Eventually, a performance loss factor and the associated energy efficiency factor is defined for analysis in detail. Simulation results show that the mixed-ADC architecture can strike a good balance among performance loss, circuit cost and energy efficiency. More importantly, just a few bits (up to 4 bits) of low-resolution ADCs can achieve a satisfactory performance for DOA measurement. Copyright © 2021, The Authors. All rights reserved.

关键词： Direction of arrival

Spatial non-locality induced non-markovian EIT in a single giant atom

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arXiv 2021年

作者： Zhu, Y.T. Wu, R.B. Xue, S. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Automation Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China

In recent experiments, electromagnetically induced transparency (EIT) were observed with giant atoms, but nothing unconventional were found from the transmission spectra. In this letter, we show that unconventional EIT does exist in giant atoms, and indicate why it has not been observed so far. Different from these existing works, this letter presents a consistent theory including a real space method and a time delayed master equation for observing unconventional EIT. We discover that this phenomenon is a quantum effect which cannot be correctly described in a semi-classical way as those in recent works. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom, which is crucial for a future experimental observation. This new phenomenon results from inherent non-locality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time. Our theory establishes a framework for application of nonlocal systems to quantum information processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Atoms

A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

Single-channel blind source separation empowered joint transceiver optimization for wireless communications using deep learning

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Digital communications and Networks 2025年

作者： Pengcheng Guo Fuqiang Yao Miao Yu Cheng Li Yanqun Tang Zhaolong Ning The Sixty-third Research Institute National University of Defense Technology Nanjing 210007 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003 China School of Electronics and Communication Engineering Sun Yat-Sen University Guangzhou 510006 China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing 400065 China

To tackle the physical layer security challenges in wireless communication, this paper introduces a multi-user architecture that leverages single-channel blind source separation, centered around a Multi-source Signal Mixture Separator (MSMS). This architecture consists of a multi-user encoder, a channel layer, and a separation decoder, allowing it to handle multiple functions simultaneously, including encoding, modulation, signal separation, demodulation, and decoding. The MSMS receiver effectively enables the separation of numerous user signals, making it exceedingly difficult for unauthorized eavesdroppers to extract valuable information from the mixed signals, thus significantly enhancing communication security. The MSMS can address the challenges of few-shot sample training and achieve joint optimization during transmission by employing a deep learning-based network design. The design of a single receiver reduces system costs and improves spectrum efficiency. The MSMS outperforms traditional Space-time Block Coding (STBC) strategies regarding separation performance, particularly in Block Error Rate (BLER) metrics. Modulation constellation diagrams further analyze the effectiveness of multi-source signal mixture separation. Moreover, this study extends the MSMS framework from a two-user scenario to a three-user scenario, further demonstrating the flexibility and scalability of the proposed architecture.

关键词： Physical layer security Multi-user wireless communication Single-channel source separation Deep learning Space-time block coding

A Study of Visual Comfort for Disparity Gradient of S3D Image with Multiple Salient Objects 4

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A Study of Visual Comfort for Disparity Gradient of S3D Imag...

4th IEEE Advanced information technology, Electronic and Automation control Conference, IAEAC 2019

作者： Su, Zhibin Liu, Yuanzi Li, Weiwei Wei, Lin Ren, Hui Communication University of China School of Information and Communication Engineering China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism China CITVC KEHUA CO. LTD. Technology Service Branch Beijing China

ISBN: (纸本)9781728119076

This paper had investigated the perception of visual comfort for the disparity gradient of stereoscopic 3D(S3D) image though subjective evaluation experiments. For the extraction of gradient features, the calculation of absolute disparity and relative disparity were all considered. The experiment result shown that the relative disparity gradient mainly affects the visual comfort under the circumstance of positive disparity, while the absolute disparity gradient would affect the viewing experience with negative disparity. As a whole, the higher both the disparity gradient was, the lower the rating score was. The research achievement is of practical value, which can not only provide importance experimental support for the visual perception mechanism, but also offer the valid basis for feature extraction of stereoscopic images. © 2019 IEEE.

关键词： Extraction

An Integration Testing Framework and Evaluation Metric for Vulnerability Mining Methods

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China communications 2018年第2期15卷 190-208页

作者： Jin Li Jinfu Chen Minhuan Huang Minmin Zhou Wanggen Xie Zhifeng Zeng Shujie Chen Zufa Zhang National key laboratory of science and technology on information system security Beijing institute of system engineering School of Computer Science and Communication Engineering Jiangsu University

Software an important way to vulnerability mining is detect whether there are some loopholes existing in the software, and also is an important way to ensure the secu- rity of information systems. With the rapid development of information technology and software industry, most of the software has not been rigorously tested before being put in use, so that the hidden vulnerabilities in software will be exploited by the attackers. Therefore, it is of great significance for us to actively de- tect the software vulnerabilities in the security maintenance of information systems. In this paper, we firstly studied some of the common- ly used vulnerability detection methods and detection tools, and analyzed the advantages and disadvantages of each method in different scenarios. Secondly, we designed a set of eval- uation criteria for different mining methods in the loopholes evaluation. Thirdly, we also proposed and designed an integration testing framework, on which we can test the typical static analysis methods and dynamic mining methods as well as make the comparison, so that we can obtain an intuitive comparative analysis for the experimental results. Final- ly, we reported the experimental analysis to verify the feasibility and effectiveness of the proposed evaluation method and the testingframework, with the results showing that the final test results will serve as a form of guid- ance to aid the selection of the most appropri- ate and effective method or tools in vulnera- bility detection activity.

关键词： software vulnerability detection loopholes information security evaluationmethod testing framework

On the regularization and optimization in quantum detector tomography

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arXiv 2022年

作者： Xiao, Shuixin Wang, Yuanlong Zhang, Jun Dong, Daoyi Yokoyama, Shota Petersen, Ian R. Yonezawa, Hidehiro University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Centre for Quantum Computation and Communication Technology Australian Research Council Centre for Quantum Dynamics Griffith University BrisbaneQLD4111 Australia Centre for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia School of Engineering Australian National University CanberraACT2601 Australia

Quantum detector tomography (QDT) is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we utilize regularization to improve the QDT accuracy whenever the probe states are informationally complete or informationally incomplete. In the informationally complete scenario, without regularization, we optimize the resource (probe state) distribution by converting it to a semidefinite programming problem. Then in both the informationally complete and informationally incomplete scenarios, we discuss different regularization forms and prove the mean squared error scales as O(1/N) or tends to a constant with N state copies under the static assumption. We also characterize the ideal best regularization for the identifiable parameters, accounting for both the informationally complete and informationally incomplete scenarios. Numerical examples demonstrate the effectiveness of different regularization forms and a quantum optical experiment test shows that a suitable regularization form can reach a reduced mean squared error. Copyright © 2022, The Authors. All rights reserved.

关键词： Tomography

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

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Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surf...

information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xianzhi Qi Wenxing Fu Pei An Bingli Wu Jie Ma National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.

关键词： Three-dimensional displays Laser radar Filtering Filtering algorithms Spatial resolution Task analysis Measurement by laser beam