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IEEE International Conference on Communications (ICC)

作者： Zhiyu Zhu Yang Yang Mingzhe Chen Caili Guo Yipeng Bai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Visible light positioning (VLP) is a promising positioning technique, which, however, typically requires multiple luminaires to achieve accurate positioning. This paper proposes a novel visual odometry (VO) assisted visible light positioning algorithm (VO-VLP) in achieving positioning with only a single luminaire. In the considered model, a user equipped with a camera jointly uses geometric features in the captured images and coordinates information obtained via visible light communication (VLC) for positioning. The proposed VLP algorithm does not rely on any extra inertial measurement unit and relaxes the tilted angle limitation at the user. In particular, VO-VLP first uses the circle feature of a luminaire to obtain dual normal vectors of the luminaire. Then, the basic principle of VO is used to eliminate the wrong normal vector by exploiting the geometric features in two consecutive images captured when the user moves. Finally, the pose and location of the user are obtained by using an artificially marked point on the luminaire's contour. VO-VLP can achieve accurate positioning with only a single luminaire and a camera. Simulation results show that the proposed indoor positioning algorithm can achieve a 97th-percentile positioning accuracy of around 10 cm.

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Symbol-level Integrated Sensing and Communication enabled Multiple Base Stations Cooperative Sensing

arXiv

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arXiv 2023年

作者： Wei, Zhiqing Xu, Ruizhong Feng, Zhiyong Wu, Huici Zhang, Ning Jiang, Wangjun Yang, Xiaoyu The Key Laboratory of Universal Wireless Communications Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The National Engineering Lab for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

With the support of integrated sensing and communication (ISAC) technology, mobile communication system will integrate the function of wireless sensing, thereby facilitating new intelligent applications such as smart city and intelligent transportation. Due to the limited sensing accuracy and sensing range of single base station (BS), multi-BS cooperative sensing can be applied to realize high-accurate, long-range and continuous sensing, exploiting the specific advantages of large-scale networked mobile communication system. This paper proposes a cooperative sensing method suitable to mobile communication systems, which applies symbol-level sensing information fusion to estimate the location and velocity of target. With the demodulation symbols obtained from the echo signals of multiple BSs, the phase features contained in the demodulation symbols are used in the fusion procedure, which realizes cooperative sensing with the synchronization level of mobile communication system. Compared with the signal-level fusion in the area of distributed aperture coherence-synthetic radars, the requirement of synchronization is much lower. When signal-to-noise ratio (SNR) is -5 dB, it is evaluated that symbol-level multi-BS cooperative sensing effectively improves the accuracy of distance and velocity estimation of target. Compared with single-BS sensing, the accuracy of distance and velocity estimation is improved by 40% and 72%, respectively. Compared with data-level multi-BS cooperative sensing based on maximum likelihood (ML) estimation, the accuracy of location and velocity estimation is improved by 12% and 63%, respectively. This work may provide a guideline for the design of multi-BS cooperative sensing system to exploit the widely deployed networked mobile communication system. Copyright © 2023, The Authors. All rights reserved.

关键词： Base stations

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Review on Comparative Analysis of Univariate and Multivariate Input Performance Over Time Series Ids Prediction Models in Healthcare Infrastructure

SSRN

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SSRN 2024年

作者： Mohd Yusof, Mohammad Hafiz Balfaqih, Mohammmed Khan, Shihab Almohammedi, Akram A. Shepelev, Vladimir Khan, Munir Hayet College of Computing Informatics and Mathematics Universiti Teknologi MARA Perak Tapah35400 Malaysia Department of Computer and Network Engineering University of Jeddah Jeddah Saudi Arabia INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai71800 Malaysia Electrical and Electronic Engineering Department Karabük University Karabük78050 Turkey Automobile Transportation Department South Ural State University Chelyabinsk454080 Russia

Intrusion Detection System (IDS) are vital for sensing zero-day and even n-day cyberattacks. Most IDS major works in Healthcare Infrastructure emphasize on developing classifiers to enhance detection and accuracy. While this approach can contribute to early detection the use of predictive models in IDS research for healthcare infrastructure remains limited, with most studies relying on LSTM and CNN variants. Furthermore, less effort has been conducted on the comparative performance between these IDS prediction models. Precisely to study the effect of having univariate, multivariate input against the multistep and single step output over the prime choices of time series prediction model. Moreover, most publicly available datasets inadequately reflect the complexity of network modern threats and contain redundant records or repetitive features. Therefore, the former issue is being resolved by the aim to comparatively review the accuracy and error performance of the selected LSTM and CNN-based predictive model across different input and output configurations. The latter, as to better simulate real-world cyber-attacks, CIRA-CIC-DoHBrw-2020 dataset, characterized by its low threat footprint closely resembling actual network conditions will be deployed as the benchmarked dataset in this study. The comparative analysis begins by transforming the input data sequence into 1*n and m*n. Next, univariate, multivariate, single and multistep splitting functions are established. These configurations are then evaluated against the benchmarked CIRA-CIC-DoHBrw-2020 dataset. The study proceeds by applying Bidirectional LSTM, Stacked LSTM, Vanilla LSTM, Encoder LSTM, Vector Output LSTM and CNN IDS prediction algorithms, followed by data visualization. Results show that the LSTM model trained on a sequence of multivariate input exhibits better prediction performance with low single-step and multiple-step error rates at 0.4% and 0.1% respectively. © 2024, The Authors. All rights reserved

关键词： Prediction models

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Ensemble Learning-based Traffic Classification with Small-Scale Datasets for Wireless networks

Ensemble Learning-based Traffic Classification with Small-Sc...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xiaorong Wang Wenting Wei Xiaoshan Yu Danyang Zheng Neeraj Kuma Lei Liu State Key Laboratory of Integrated Service Networks Xidian University Xi'an China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology India Guangzhou Institute of Technology Xidian University Xi'an China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Traffic classification is an essential part of intelligent network management in Fifth generation (5G) networks, from quality of service guarantees to security monitoring. With the evolution of diverse services and applications over 5G networks, traffic patterns and their implications also becoming more complicated, especially in wireless networks. How to dynamically manage the growing traffic demand and satisfy the quality of service (QoS) needs of new applications is particularly critical. The biggest challenge is how to identify traffic types accurately without application information and manual features. However, the existing deep learning (DL)-based classifiers heavily rely on extensive and high-quality samples, which proves impractical to obtain in wireless networks. To overcome this limitation, we propose an ensemble Learning based traffic classification model to provide accurate identification with small-scale datasets, where Attention-based Long Short-Term Memory networks are bagged to train this classifier. The ensemble learning approach employs a combinatorial design involving five base models to construct more accurate and robust learning models. This enables effective feature absorption, making it feasible to train classifiers on limited sample sets. Comprehensive experiments are conducted using a real-world dataset covering 16 applications. Results demonstrate that, even with a mere 10% of training samples, our proposed model achieves a classification accuracy of 95.57% and a precision of 95.83%, surpassing multiple cutting-edge methods.

关键词： Training Accuracy 5G mobile communication Wireless networks Computational modeling Quality of service Telecommunication traffic

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Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication

arXiv

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arXiv 2024年

作者： Uyoata, Uyoata E. Akinsolu, Mobayode O. Obayiuwana, Enoruwa Sangodoyin, Abimbola Adeogun, Ramoni Wireless Communication Network Section Aalborg University Aalborg Denmark Faculty of Arts Computing and Engineering Wrexham University WrexhamLL11 2AW United Kingdom Department of Electronic and Electrical Engineering Obafemi Awolowo University Ife Nigeria School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)-assisted communication is considered to investigate the gains of optimizing both the phase angles and selection of relays. The combination of successive refinement and reinforcement learning is proposed. Successive refinement algorithm is used for phase optimization and reinforcement learning is used for relay selection. Experimental results indicate that the proposed approach offers improved achievable rate performance and scales better with number of relays compared to considered benchmark approaches. © 2024, CC BY.

关键词： Reinforcement learning

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Energy Efficient Collaborative Federated Learning Design: A Graph Neural network based Approach

Energy Efficient Collaborative Federated Learning Design: A ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

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From model to implementation: a network algorithm programming language

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Science China(Information Sciences) 2020年第7期63卷 205-221页

作者： Jian WANG Jie AN Mingshuai CHEN Naijun ZHAN Lulin WANG Miaomiao ZHANG Ting GAN State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Engineering Tongji University Network Integration Technology Research Department Huawei Technologies Co. Ltd. School of Computer Science Wuhan University

Software-defined networking(SDN) is a revolutionary technology that facilitates network management and enables programmatically efficient network configuration, thereby improving network performance and flexibility. However, as the application programming interfaces(APIs) of SDN are low-level or functionality-restricted, SDN programmers cannot easily keep pace with the ever-changing devices, topologies, and demands of SDN. By deriving motivation from industry practice, we define a novel network algorithm programming language(NAPL) that enhances the SDN framework with a rapid programming flow from topology-based network models to C++ implementations, thus bridging the gap between the limited capability of existing SDN APIs and the reality of practical network management. In contrast to several state-of-the-art languages, NAPL provides a range of critical high-level network programming features:(1) topology-based network modeling and visualization;(2) fast abstraction and expansion of network devices and constraints;(3) a declarative paradigm for the fast design of forwarding policies;(4) a built-in library for complex algorithm implementation;(5) full compatibility with C++ programming; and(6) userfriendly debugging support when compiling NAPL into highly readable C++ codes. The expressiveness and performance of NAPL are demonstrated in various industrial scenarios originating from practical network management.

关键词： software-defined networking(SDN) network algorithm programming language(NAPL) network abstraction

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ACM-NDC study 2021-2022

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ACM Inroads 2022年第3期13卷 38-54页

作者： Zweben, Stuart Tims, Jodi L. Tucker, Cindy Timanovsky, Yan Department of Computer Science and Engineering The Ohio State University 2015 Neil Avenue ColumbusOH43210 United States Khoury College in the Global Network Valley The Practice-San Francisco Silicon Valley Northeastern University 360 Huntington Ave. BostonMA02115 United States 470 Cooper Dr LexingtonKY40506 United States Education and Professional Development Manager Association for Computing Machinery 1601 Broadway New YorkNY10019 United States

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Ferromagnetism triggered by band tripling in ruthenate Sr4Ru3O10

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Physical Review B 2024年第15期110卷 155134页

作者： N. Watanabe S. Souma K. Nakayama K. Yamauchi J. F. Ribeiro Y. Wang Miho Kitamura Koji Horiba Hiroshi Kumigashira T. Oguchi Z. Q. Mao Y. P. Chen T. Sato Department of Physics Graduate School of Science Advanced Institute for Materials Research (WPI-AIMR) Center for Science and Innovation in Spintronics (CSIS) Precursory Research for Embryonic Science and Technology (PRESTO) Center for Spintronics Research Network (CSRN) School of Electrical and Computer Engineering and Birck Nanotechnology Center Department of Physics Institute of Materials Structure Science (QST) Sendai 980-8579 Japan Institute of Multidisciplinary Research for Advanced Materials (IMRAM) Department of Physics and Astronomy and Purdue Quantum Science and Engineering Institute International Center for Synchrotron Radiation Innovation Smart (SRIS) Mathematical Science Center for Co-creative Society (MathCCS)

Itinerant ferromagnetism is understood in terms of a quasiparticle picture with renormalized many-body effects. While the ferromagnetic ground state is destabilized by thermal and quantum fluctuations leading to exotic states such as unconventional superconductivity, how the quasiparticles evolve across the ferromagnetic transition is a target of intensive debate. Here, we present a type of ferromagnetic transition that is accompanied by a drastic reconstruction of quasiparticle spectrum in a layered ferromagnetic ruthenate, Sr4Ru3O10. Angle-resolved photoemission spectroscopy uncovered that the three-dimensional coherent states below the ferromagnetic transition temperature (TC) turn into two-dimensional incoherent electronic states slightly above TC characterized by the disappearance of trilayer band splitting, ferromagnetic exchange splitting, and long-lived quasiparticles. Our findings suggest that the electronic coherence strongly modifies the fermiology and magnetic order, pointing to an intriguing coupling between quasiparticles and magnetic properties.

关键词： Electronic structure Fermi surface Ferromagnetism Quasiparticles & collective excitations van Hove singularity Ruddlesden-Popper compounds Angle-resolved photoemission spectroscopy Density functional theory First-principles calculations

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TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection

arXiv

引用

arXiv 2024年

作者： Wang, Kai-Ni Wang, Haolin Zhou, Guang-Quan Wang, Yangang Yang, Ling Chen, Yang Li, Shuo School of Biological Science and Medical Engineering Southeast University Nanjing China Jiangsu Key Laboratory of Biomaterials and Devices Southeast University Nanjing China Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China TUGE Healthcare Nanjing China Institute of Medical Technology Peking University Health Science Center China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal-Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously. Technically, we first propose a global temporal-aware convolution, assembling the preceding information to dynamically guide the current convolution kernel to focus on global features between sequences. In addition, we designed a hierarchical queue integration mechanism to combine multi-temporal features through a progressive accumulation manner, fully leveraging contextual consistency information together with retaining long-sequence-dependency features. Meanwhile, at the spatial level, we advance a position-aware clustering to explore the spatial relationships among candidate boxes for recalibrating prediction confidence adaptively, thus eliminating redundant bounding boxes efficiently. The experimental results on three publicly available polyp video dataset show that TSdetector achieves the highest polyp detection rate and outperforms other state-of-the-art methods. The code can be available at https://***/soleilssss/TSdetector. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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