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23rd IEEE International Conference on communication technology, ICCT 2023

作者： Du, Tao Yang, Jie Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing211189 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China Zte Corporation Shenzhen518057 China State Key Laboratory of Mobile Network and Mobile Multimedia Shenzhen518057 China

ISBN: (纸本)9798350325959

Integrated sensing and communication (ISAC) is expected to improve the energy and spectral efficiency of communication systems. Probability hypothesis density (PHD) simultaneous localization and mapping (SLAM) without explicit data association is one of the mainstream algorithms for implementing sensing function in ISAC. However, most existing research focused on multi-sensor and multi-user sensing fusion, while few studies involve multi base station (BS) fusion schemes. In this study, we introduce the virtual reference point rather than common used virtual anchor to model reflecting surface to form a consistent description of the surrounding environment among different BSs. Then, the estimation results of BSs are sequentially utilized as prior information, which is compared with arithmetic average (AA) and generalized covariance intersection (GCI) fusion algorithms. To improve the poor performance of existing schemes when filed of view (FoV) of different BSs is partial overlapped, we propose the concept of establishing global cloud map. The AA fusion algorithm is integrated into cloud fusion to improve the map estimation performance. Simulation results demonstrate that the performance of sequential fusion is superior to AA and GCI schemes when FoVs of different BSs are fully overlapped. Besides, the cloud map fusion scheme can obtain global maps even if some features are no longer in FoV, as well as improve the accuracy and stability of local maps estimation for each BS. © 2023 IEEE.

关键词： integrated sensing and communication mmWave multi-BS fusion probability hypothesis density

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Study on the Medium Fusion Data Storage technology for High Performance Airborne Sensors

Study on the Medium Fusion Data Storage Technology for High ...

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Intelligent technology and Embedded Systems (ICITES), International Conference on

作者： Dan Xie Haishan Tian Yuxin Sun Mingru Zhang Yawei Huang Chang Tang School of Physics and Electronics Hunan Normal University Changsha China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing China

The data rate of high-performance airborne sensors is much higher than the wireless data transfer rate in many detection applications, so the onboard data storage systems are usually used to store the sensor data. There is a problem in the onboard data storage that the frequency of address jumps among incontinuous areas is very high due to the high data updating frequency of the FAT (File Allocation Table) and FDT (File Directory Table), which would significantly reduce the data storage speed. This is because the most widely used NAND (Not And) flash has a long response time when storing the data in the incontinuous areas. In order to solve the problem, a called medium fusion storage method, which uses multiple mediums time sharing to store the data of file areas and data region, is proposed in this study. The performance of the proposed method is tested on an actual data storage system of the airborne sensor. The experimental results show that medium fusion storage method significantly reduces the data updating frequency of the FAT and FDT in the data storage, and the response time is negligible compared with the actual data writing time.

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An Enhanced Resource Selection Scheme for Efficient Intra-Platoon Message Delivery

An Enhanced Resource Selection Scheme for Efficient Intra-Pl...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Wang, Bingying Zheng, Jun Li, Cheng Nanjing University of Information Science and Technology School of Electronics and Information Engineering Nanjing210044 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada Memorial University Department of Electrical and Computer Engineering St. John'sNLA1B 3X5 Canada

ISBN: (纸本)9798350351255

This paper proposes an enhanced resource selection (eInP-RS) scheme for efficient intra-platoon message delivery of cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs). To achieve this goal, the eInP-RS scheme allows each vehicle to transmit CAM and DENM packets on a C-V2X channel (CH1) and an 802.11p channel (CH2), separately, and introduces four mechanisms to enhance the standardized sensing-based SPS scheme. A contention window (CW) size adjustment mechanism is introduced to enable a vehicle to adjust its CW size according to the information it collects on CH1 in order to avoid potential packet collisions on CH2;a resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles moving in opposite directions to select different resources and thus avoid potential merging collisions on CH1;an intra-platoon cooperation mechanism is introduced to enable the PL of a platoon to know the resource and channel occupation information of the platoon's hidden nodes on CH1 and CH2;and a packet collision detection mechanism is used to enable a non-platoon vehicle to detect packet collisions occurring on both channels after a lane-changing maneuver to avoid potential merging collisions. Simulation results show that the proposed eInP-RS scheme outperforms the standardized sensing-based SPS scheme in terms of the intra-platoon CAM/DENM delivery ratio and the average intra-platoon DENM delivery delay. © 2024 IEEE.

关键词： Magnetic levitation vehicles

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Unsupervised Domain Adaptation Using Attention Network and New Loss Function

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 704-718页

作者： Zhao, Li-Quan Zhou, Si-Ying Teng, Zi-Ming Jiang, Zi-Cong Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of information and communication Jilin University Changchun130012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan School of Electrical and Information Engineering Beihua University Jilin132013 China

An unsupervised domain adaptation method is proposed based on the step-wise adaptive feature norm method to improve transfer learning accuracy. The feature extraction network consists of channel attention and spatial attention modules, which en-hance feature extraction ability. To reduce the influence of dimension change on channel weight, a convolution layer with the same kernel size is used to replace the fully connected layer in the bottleneck layer used in the common channel attention module. In addition, a smooth loss function is also proposed. The curve of the proposed function is smoother than the L2 function that is the least square error function. The proposed function’s gradient changes more slowly than the L2 function. The proposed function is used as the activate function of classification prediction regression to reduce the risk of gradient explosion. Compared with a selection of existing methods (Resnet50, conditional domain adversarial networks method, deep adaptive network method, joint adaptation network method, domain adaptation neural network method, and stepwise adaptive feature norm method), the proposed transfer learning method has the highest accuracy than other methods on different datasets for all tasks. © 2022 Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Feature extraction

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Design and Implementation of High-Speed Serial Transmission for ADC Based on JESD204B

Design and Implementation of High-Speed Serial Transmission ...

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Intelligent technology and Embedded Systems (ICITES), International Conference on

作者： Yawei Huang Haishan Tian Mingru Zhang Yuxin Sun Dan Xie Chang Tang School of Physics and Electronics Hunan Normal University Changsha China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing China

Currently, as the resolution of sensors such as radar continues to improve, the signal bandwidth is rapidly increasing. There is a growing demand for higher sampling rates and data transmission rates in Analog-to-Digital Converters (ADCs). However, the parallel transmission commonly used in ADCs faces issues such as high crosstalk and difficulties in transmission synchronization. In response to this problem, this paper proposes a method for the design and implementation of a high-speed serial data transmission interface based on JESD204B. Specifically, it utilizes Xilinx's ZYNQ-7 series FPGA and ADC as the core components and presents clock synchronization design, ADC circuit design, and FPGA 204B IP design methods. Experimental testing was conducted using selected link parameters (LMFS=4222) as an example. The experimental results demonstrate that correct JESD204B interface data transmission for four channels was achieved at a data rate of 10 Gbps, thus validating the feasibility of the proposed high-speed transmission link design based on JESD204B in this paper.

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A Collaborative Jamming Algorithm Based on Multi-UAV Scheduling

arXiv

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arXiv 2023年

作者： Jiang, Yixin Zhou, Lingyun Tang, Yijia Tu, Ya Liu, Chunhong Shi, Qingjiang School of Software Engineering Tongji University Shanghai201804 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this paper, we consider the problem of multiunmanned aerial vehicles’ (UAVs)’ scheduling for cooperative jamming, where UAVs equipped with directional antennas perform collaborative jamming tasks against several targets of interest. To ensure effective jamming towards the targets, we formulate it as an non-convex optimization problem, aiming to minimize the communication performance of the targets by jointly optimizing UAVs’ deployment and directional antenna orientations. Due to the unique structure of the problem, we derive an equivalent transformation by introducing a set of auxiliary matrices. Subsequently, we propose an efficient iterative algorithm based on the alternating direction method of multipliers (ADMM), which decomposes the problem into multiple tractable subproblems solved in closed-form or by gradient projection method. Extensive simulations validate the efficacy of the proposed algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 15513-15526页

作者： Liu, Lei Feng, Jie Mu, Xuanyu Pei, Qingqi Lan, Dapeng Xiao, Ming Xidian University Guangzhou Institute of Technology Guangzhou510555 China Ministry of Education Shanghai201804 China Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunication Engineering Shaanxi Xi'an710071 China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an710121 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China University of Oslo Department of Informatics Oslo0373 Norway Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Research on Privacy Preserving Computing technology in Edge Computing

Research on Privacy Preserving Computing Technology in Edge ...

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International Conference on Networking and Network Applications (NaNA)

作者： Yuyao Wang Lei Chen Siyuan Ni Fei Yu Yuanhang He Qiang Fang Yuzheng Zhou Science and Technology on Communication Security Laboratory Chengdu PR China No.30 Research Institute of China Electronics Technology Group Corporation Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

With the rapid development of mobile Internet and Internet of Things applications, the traditional cloud computing model can provide centralized remote services, but when the amount of data increases sharply, it has been unable to effectively process the data generated by edge devices. To address these challenges and efficiently utilize distributed computing resources, edge computing emerges. With the rise of edge computing, more and more attention has been paid to its security threats and countermeasures. This paper first introduces the basic definition, architectural features, and current security threats of edge computing, then discusses the privacy protection issues in edge computing, and systematically expounds the latest research results at home and abroad. The application of privacy-preserving computing technology in computing, discusses the development trend of edge computing, and proposes further research directions.

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Receiver-Agnostic Radio Frequency Fingerprint Identification via Feature Disentanglement

Receiver-Agnostic Radio Frequency Fingerprint Identification...

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

作者： Hongli Liu Shaoxian Zhu Liu Yang Qiang Li Jingran Lin School of Information and Communication Engineering University of Electronic Science and Technology of China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

The Radio Frequency Fingerprint Identification (RFFI) technique utilizes the subtle and unintentional modulation present in the transmitted RF waveform, caused by the non-ideal characteristics of the device, to uniquely identify an emitter. RFFI has recently gained significant attention, and deep learning (DL) approach has achieved superior performance. In this paper, we study the DL-based RFFI with an emphasis on the fingerprint contamination by receivers. Specifically, during reception, the receivers’ fingerprints are inevitably introduced, and different receivers affect emitters’ fingerprints differently, thereby degrading recognition performance when the RFFI recognition model is deployed at different receivers. To mitigate the receivers’ effect, we propose a receiver-independent recognition model based on the idea of feature disentanglement. With the aid of some carefully designed model structure, loss functions and training strategy, the proposed model learns a feature space, in which the emitters’ features are disentangled from the receivers’, through multi-receiver’s training data. When deploying the model on a new receiver, the emitter-dependent features are exploited to achieve receiver-independent identification. Experiments on two real-world datasets demonstrate that our method significantly outperforms the baseline method without considering fingerprint contamination.

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Dynamic 3D UAV Placement Optimization: Improved Bonobo Optimizer for Enhanced Coverage and communication 35

Dynamic 3D UAV Placement Optimization: Improved Bonobo Optim...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio communications, PIMRC 2024

作者： Yahia, Selma Taleb, Sylia Mekhmoukh Loscri, Valeria Saadi, Amylia Ait Ho, Tu Dac Vo, Van Nhan Eldeeb, Hossien Muhaidat, Sami Inria Lille-Nord Europe Lille 59000 France University of m'Hamed Bougara List Laboratory Boumerdes35000 Algeria Esme Engineering School Ivry-sur-Seine94200 France Norwegian University of Science and Technology Department of Information Security and Communication Technology Norway The Arctic University of Norway Department of Electrical Engineering Norway Duy Tan University Faculty of Information Technology Viet Nam Duy Tan University The Institute of Research and Development Viet Nam Khalifa University 6G Research Center Computer and Communication Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350362244

Unmanned aerial vehicles (UAVs) offer a promising solution for enhancing network coverage, reliability, and data speed in future wireless network generations. However, deploying UAVs as aerial base stations requires careful consideration of crucial design factors, including three-dimensional (3D) placement and performance optimization tailored to specific applications. In this paper, the 3D placement of multiple UAVs, acting as aerial base stations, is investigated in a dynamic user scenario. First, a closed-form expression for the coverage probability is derived. Then, to maximize the network coverage and sum rate while ensuring reliable and energy efficient system, a joint multi-objective optimization problem is formulated considering the real-time user movements. To solve the problem, an improved Chaos-based Bonobo Optimizer (CBO) scheme is proposed which combines chaotic maps with the Bonobo Optimizer (BO) algorithm. The obtained results demonstrate the superior performance of the proposed approach compared with different benchmark algorithms. The results reveal that the proposed CBO algorithm offers a minimum of 1 5 % and 9 0 ~ M b i t / s ~ i m p r o v e m e n t s ~ in coverage and sum rate, respectively. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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