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Intelligent Reflecting Surface-Assisted Combined TOA and AOA Localization Algorithm

KSII Transactions on Internet and information Systems 2025年第4期19卷 1357-1373页

作者： Zhang, Yuexia Gang, Yuanshuo Duan, Shiru Wang, Yu Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing102206 China Key Laboratory of Information and Communication Systems Beijing Information Science and Technology University Beijing102206 China

Wireless positioning technology plays a crucial role in various applications, including intelligent transportation, industrial automation, and smart cities. However, in non-line-of-sight environments, signal obstruction and multipath effects significantly degrade localization accuracy. To address these challenges, this paper proposes an Intelligent Reflecting Surface-assisted Combined time of arrival (TOA) and angle of arrival (AOA) Localization (IRS-CTAL) algorithm. Specifically, this paper introduces a dynamic partially connected IRS structure, which selectively activates IRS sub-arrays in a time-division manner. This dynamic activation mitigates multipath interference and enhances the controllability of IRS-reflected signals, leading to improved localization performance. In the proposed IRS-CTAL algorithm, the 2D multiple signal classification algorithm is employed to jointly estimate the TOA and AOA of the received signals. Furthermore, to optimize the estimated coordinates and minimize the localization error, this paper incorporates the root mean square propagation algorithm, which efficiently corrects nonlinear distortions caused by IRS reflections. Simulation results demonstrate that the IRS-CTAL algorithm significantly outperforms traditional localization methods, achieving an average localization error reduction of 41% compared to the direct geometric computation method. Copyright © 2025 KSII.

关键词： Computational geometry

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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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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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Orthogonal Time Frequency Space with Sub-Band Index Modulation

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IEEE communications Letters 2025年第5期29卷 1047-1051页

作者： Zhang, Si-Yu Zhang, Yue-Xia Shahrrava, Behnam Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Key Laboratory of Information and Communication Systems Ministry of Information Industry School of Information and Communication Engineering Beijing100101 China University of Windsor Department of Electrical and Computer Engineerning Windsor N9B 3P4 Canada

This letter proposes an innovative orthogonal time-frequency space with sub-band index modulation (OTFS-SBIM) scheme. To overcome the complexity and spectral efficiency (SE) limitations inherent in the OTFS-IM, this study introduces a transmission mechanism where sub-carriers are equally divided into two sub-bands, and the index modulation (IM) operations are restricted to a single sub-band. To offset the potential SE loss, the proposed OTFS-SBIM employs a band-controlling bit β and performs IM independently in the in-phase and quadrature (I/Q) dimensions. These improvements ensure both the diversity gain and enhanced bit error rate (BER) performance. Further, a low-complex maximum likelihood (ML) detection is proposed for bit recovery. Finally, analytical expressions for average bit error probability (ABEP) and peak-to-average power ratio (PAPR) are derived and used to evaluate the OTFS-SBIM’s performance. Simulation results demonstrate that the proposed scheme outperforms the OTFS-IM scheme in terms of the BER and PAPR metrics while maintaining higher SE. © 1997-2012 IEEE.

关键词： Signal modulation

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RIS Enabled SCLAM: An Approach for Simultaneous Radio communication, Localization, and Mapping

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IEEE Internet of Things Journal 2025年第10期12卷 14663-14676页

作者： Zhao, Jinqiu Bai, Zhiquan Guo, Shuaishuai Ma, Dejie Li, Na Kwak, Kyung Sup Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Information Science and Engineering Qingdao266237 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Control Science and Engineering Jinan250061 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan250200 China INHA University Graduate School of Information Technology and Telecommunications Incheon22212 Korea Republic of

Radio-based simultaneous localization and mapping (SLAM) facilitates unmanned systems to fulfill self-localization and navigation in complex environments. However, the existing studies overlooked communication between devices, which may lead to low efficiency and high costs when performing SLAM tasks. Reconfigurable intelligent surface (RIS) can satisfy the growing demands of users and improve SLAM accuracy in dynamic and complex environments. This paper proposes a RIS enabled simultaneous radio communication, localization, and mapping (SCLAM) system, where an unmanned aerial vehicle (UAV) can perform concurrent communication and SLAM with the help of RIS by utilizing communication signals from base station (BS). The proposed method enables the UAV to simultaneously accomplish SLAM and ensure communication with the BS, while also enhancing the spectrum utilization efficiency. We derive the Bayesian Fisher information matrix (BFIM) for joint SLAM and symbol detection of the proposed system, in which the trade-off between the BFIM of SLAM and the upper bound of the ergodic mutual information is illustrated. Then, a weighted factor based BFIM is presented to further achieve a performance trade-off between SLAM and communication. We formulate an optimization problem of joint BS active beamforming and RIS passive beamforming to maximize the log determinant of weighted BFIM. Numerical results verify the superiority of the proposed SCLAM system on position error bound (PEB), mapping error bound (MEB), and spectral efficiency (SE). The performance trade-off between communication and SLAM is also discussed and explored. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research on Radar Anti-deceptive-jamming Method based on Characterization 16

Research on Radar Anti-deceptive-jamming Method based on Cha...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Lei, Lei Jiang, Meiqiu Li, JiaChen Guo, Shisheng Cui, Guolong Xia, Bijun Zhu, Lei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Electromagnetic Space Security Chengdu China

ISBN: (纸本)9781510689251

Deceptive jamming plays an increasingly important role in radar electronic countermeasures, and there is an urgent need to study new radar anti-deceptive jamming methods. In this paper, four characteristic factors, namely, amplitude variance, amplitude ratio, power ratio and number of correlated signals, are extracted based on the intra pulse characteristic differences between the target and deceptive jamming signals. Based on these four factors, a multi-domain cross-coupled neural network is proposed to distinguish between real targets and deceptive jamming. Finally, the simulation results show that this method has a significant effect on anti-deceptive jamming. © 2025 SPIE.

关键词： Radar countermeasures

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Radio Signal Modulation Pattern Recognition Based on Time-Frequency Adaptive Decomposition and Hybrid Neural Network

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IEEE Internet of Things Journal 2025年

作者： Dai, Shaogang Xu, Jianwei Zhou, Huaji Hangzhou Dianzi University School of communication engineering Hangzhou310018 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China

Automatic modulation classification of electromagnetic signals has an important role in the field of signal processing. The current methods for identifying the modulation pattern of radio signals mainly used end-to-end neural network models, which suffered from the problems such as network redundancy and non-interpretability. In order to solve the above problems, this paper proposes a radio signal modulation pattern recognition method based on time-frequency adaptive decomposition and hybrid neural network. The discrete wavelet decomposition is used to decompose the original radio signals into adaptive high and low frequencies. Then feed the resulting high and low frequency portions into a multi-channel hybrid neural network (MHNN) for training to obtain the classification results. In the discrete wavelet decomposition process, the number of decomposition layers and the decomposition threshold are determined adaptively according to the difference of SNRs. In the design of multi-path hybrid neural network structure, the signal characteristics of high and low frequency parts are considered comprehensively. The network channels with different convolutional kernel scales and different layers are constructed to reduce the number of model parameters and training time while ensuring the classification accuracy. Using RadioML2016.10a dataset for experiments, the recognition accuracy of MHNN is prior to the end-to-end deep learning methods, which can achieve the accuracy of 92.30% and the F1 of 91.84% when SNR=2dB. It shows that the proposed radio signal modulation pattern recognition method based on the time-frequency adaptive decomposition and hybrid neural network can balance the accuracy and lightweight. Moreover, it has certain interpretability for the original input data, distinguishing the main body and details of the signal by dividing the signal into high and low frequencies, which provides a new idea for the interpretability of deep convolutional neural networks. ©

关键词： Signal modulation

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Deterministic Event-Based Physical Watermarks Against Replay Attacks in Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 61-69页

作者： Zhao, Xudong Xing, Wei Zhang, Junfeng Wang, Xinyu Zong, Guangdeng Zhao, Ning Hainan University School of Information and Communication Engineering Haikou570288 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116622 China Tiangong University School of Control Science and Engineering Tianjin300387 China Bohai University College of Control Science and Engineering Jinzhou121013 China

Cyber-physical systems (CPSs) are of great importance in various applications and have garnered significant attention. Ensuring their security is a crucial concern, while this paper focuses on the security of CPSs against replay attacks during the transmission of sensor measurements over a wireless network. It is widely recognized that incorporating physical watermark signals is an effective approach to mitigate replay attacks. However, traditional physical watermarks are time-based and result in a notable degradation in the performance of Linear Quadratic Gaussian systems. With this in mind, a novel event-based physical watermarking technique is devised to address the performance degradation and detect replay attacks. The approach involves a deterministic physical watermark schedule that utilizes the innovation sequence to determine whether to incorporate physical watermarks or not. We provide evidence that the probability of successfully embedding physical watermarks increases when the system is targeted by malicious attacks. Additionally, we demonstrate that the deterministic event-based watermarking approach is more effective than the traditional time-based watermarking method. Finally, simulation examples are included to validate and illustrate the theoretical results. © 2023 IEEE.

关键词： Cyber attacks

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