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Near-field communications:characteristics,technologies,and engineering

Frontiers of Information Technology & Electronic engineering 2024年第12期25卷 1580-1626页

作者： Yajun ZHAO Linglong DAI Jianhua ZHANG Ran JI Mengnan JIAN Hao XUE Hongkang YU Yunqi SUN Yu LU Zidong WU Zhuo XU Jinke LI Haiyang MIAO Zhiqiang YUAN Pan TANG Jiayu SHEN Tierui GONG Haixia LIU Jiaqi HAN Qiang FENG Zhi CHEN Lingxiang LI Gang YANG Yong ZENG Cunhua PAN Wang LIU Kangda ZHI Weidong HU Yuanwei LIU Xidong MU Chau YUEN Mérouane DEBBAH Chongwen HUANG Long LI Ping ZHANG State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China ZTE Corporation Beijing 100192China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310007China Key Laboratory of High Speed Circuit Design and EMC of Ministry of Education School of Electronic EngineeringXidian UniversityXi’an 710071China Collaborative Innovation Center of Information Sensing and Understanding Xidian UniversityXi’an 710071China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of ChinaChengdu 611731China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China School of Electrical Engineering and Computer Science Technical University of BerlinBerlin 10623Germany School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Centre for Wireless Innovation(CWI) School of ElectronicsElectrical Engineering and Computer ScienceQueen’s University BelfastBelfast BT39DTUK School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore KU 6G Research Center Khalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates

Near-field technology is increasingly recognized due to its transformative potential in communication systems,establishing it as a critical enabler for sixth-generation(6G)telecommunication *** paper presents a comprehensive survey of recent advancements in near-field technology ***,we explore the near-field propagation fundamentals by detailing definitions,transmission characteristics,and performance ***,we investigate various near-field channel models—deterministic,stochastic,and electromagnetic information theory based models,and review the latest progress in near-field channel testing,highlighting practical performance and *** evolving channel models,traditional mechanisms such as channel estimation,beamtraining,and codebook design require redesign and optimization to align with near-field propagation *** then introduce innovative beam designs enabled by near-field technologies,focusing on non-diffractive beams(such as Bessel and Airy)and orbital angular momentum(OAM)beams,addressing both hardware architectures and signal processing frameworks,showcasing their revolutionary potential in near-field communication ***,we highlight progress in both engineering and standardization,covering the primary 6G spectrum allocation,enabling technologies for near-field propagation,and network deployment ***,we conclude by identifying promising future research directions for near-field technology development that could significantly impact system *** comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

关键词： 6G Near-field technology Channel model Codebook Non-diffractive beams Orbital angular momentum engineering and standardization

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Risk Aware Trust of Information for Improving Safety in Vehicular networks

SSRN

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SSRN 2022年

作者： Choi, Jong-Young Ko, Young-Bae Department of AI Convergence Network Ajou University Suwon16499 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of

In vehicular networks, safety applications based on basic safety messages (BSMs) can significantly improve road safety. BSMs broadcast by a vehicle contain real-time information on its kinematics, namely its position, speed, and heading. Outdated or incorrect BSMs can lead to collisions;therefore, BSM rate adaptation is necessary to communicate fresh and accurate information. Several recent studies have applied the age of information (AoI) concept to BSM rate adaptation. In this study, we investigate why "as is" application of AoI is not optimal and demonstrate the importance of selecting specific vehicles for AoI minimization. We also propose the novel trust of information (ToI) concept, which can be applied to quantify the reliability of BSMs. Through simulations, a ToI-based BSM rate-adaptation algorithm is compared with a recent BSM rate-adaptation scheme, demonstrating that the ToI algorithm outperforms the scheme in terms of safety assurance. © 2022, The Authors. All rights reserved.

关键词： Motor transportation

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge Computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

arXiv

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Information Processing Factory 2.0 - Self-awareness for Autonomous Collaborative Systems

Information Processing Factory 2.0 - Self-awareness for Auto...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Nora Sperling Alex Bendrick Dominik Stöhrmann Rolf Ernst Bryan Donyanavard Florian Maurer Oliver Lenke Anmol Surhonne Andreas Herkersdorf Walaa Amer Caio Batista de Melo Ping-Xiang Chen Quang Anh Hoang Rachid Karami Biswadip Maity Paul Nikolian Mariam Rakka Dongjoo Seo Saehanseul Yi Minjun Seo Nikil Dutt Fadi Kurdahi Institute of Computer and Network Engineering TU Braunschweig Germany Department of Computer Science San Diego State University School of Computation Information & Technology Technical University of Munich Germany Center for Embedded and Cyber-physical Systems (CECS) University of California Irvine

This paper summarizes the talks of a special session on the IPF 2.0 project, a collaborative German-US research project that leverages self-awareness principles for the self-management of distributed systems of autono...

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A Compositional Approach to Safety-Critical Resilient Control for Systems with Coupled Dynamics

A Compositional Approach to Safety-Critical Resilient Contro...

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IEEE Conference on Decision and Control

作者： Abdullah Al Maruf Luyao Niu Andrew Clark J. Sukarno Mertoguno Radha Poovendran Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA Department McKelvey School of Engineering Electrical and Systems Engineering Washington University St. Louis Mo School of Cybersecurity and Privacy Georgia Institute of Technology Atlanta GA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Complex, interconnected Cyber-physical Systems (CPS) are increasingly common in applications including smart grids and transportation. Ensuring safety of interconnected systems whose dynamics are coupled is challenging because the effects of faults and attacks in one sub-system can propagate to other sub-systems and lead to safety violations. In this paper, we study the problem of safety-critical control for CPS with coupled dynamics when some sub-systems are subject to failure or attack. We first propose resilient-safety indices (RSIs) for the faulty or compromised sub-systems that bound the worst-case impacts of faulty or compromised sub-systems on a set of specified safety constraints. By incorporating the RSIs, we provide a sufficient condition for the synthesis of control policies in each failure- and attack- free sub-systems. The synthesized control policies compensate for the impacts of the faulty or compromised sub-systems to guarantee safety. We formulate sum-of-square optimization programs to compute the RSIs and the safety-ensuring control policies. We present a case study that applies our proposed approach on the temperature regulation of three coupled rooms. The case study demonstrates that control policies obtained using our algorithm guarantee system’s safety constraints.

关键词： Sufficient conditions Heuristic algorithms Transportation Interconnected systems Cyber-physical systems Regulation Safety

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The effectiveness of interactive digital evaluation training for improving teacher skills in the covid-19 pandemic period 4

The effectiveness of interactive digital evaluation training...

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4th International Conference on Vocational Education and Training, ICOVET 2020

作者： Nur Hidayat, Wahyu Suswanto, Hary Wijaya Kristanto, Chandra Pramudya Wardhani, Afista Hamdan, Achmad Kartika Sari, Rahajeng Universitas Negeri Malang Department of Electrical Engineering Malang Indonesia SMKN 10 Malang Computer and Network Engineering Malang Indonesia

ISBN: (纸本)9781728181318

In improving the quality of education, teachers have a very important role in it. To carry out these tasks, teachers must have skills and knowledge in accordance with the curriculum used, the 2013 curriculum. And with the COVID-19 pandemic, teachers are also required to be more creative in using technology in learning that can increase participant interest. Some applications that can help teachers in online learning are the Google Classroom and Quizizz applications. However, currently, many teachers do not really understand how to use technology in online learning. Therefore, this training was held with the aim of finding out how effective technology is for teachers with a project-based learning model. Research participants will get several projects to create online classes in google classroom and create online quizzes with the quizizz application, which can be applied to their own participants in online learning. The research method used is quantitative research methods using pretest-posttest group design research. The research was conducted at SMAN 1 Jombang with 61 participants, all of the participants is a teachers. The training was held for three days, where the first day of face-To-face learning, and the next day online learning was carried out. The results show significant improvement and skills. In several aspects such as the relevance of the score is 86.11%. From the quality of the materials got 85.00%, and from the quality of the trainers 87.77% and overall, it means very good. © 2020 IEEE.

关键词： Curricula

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TIoT: A Trust by Design Architecture for Secure and Private Internet of Things (IoT)

TIoT: A Trust by Design Architecture for Secure and Private ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： H. Alsheakh Tareq Nasser Mahdi Mustafa S. Kadhm Abeer Al Shammari Fatimah I. Abdulsahib Hazeem B Taher Waleed Algriree Marwa Q. Alobaidi Hayder Dhafer Mohammed Department of Computer Science College of Science Mustansiriyah University Baghdad Iraq Clinical Laboratory Science College of Pharmacy Mustansiriyah University Baghdad Iraq IT Directorate Ministry of Science and Technology Baghdad Iraq Department of Computer Science Szechenyi Istvan Egyetem Gyor Hungary Department of Computer Science University of Thi-Qar Thi-Qar Iraq Dep. Of Wireless Communication and Network Engineering University Putra Malaysia Selangor Malaysia National Engineering School of Sfax Sfax University Sfax Tunisia College of Pharmacy Mustansiriyah University Baghdad Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

The Internet of Things (loT) facilitates the interconnection of a vast array of devices, services, and individuals for data exchange in multi-domain loT ecosystems, the social, physical, and cyber domains. That emerges various security, privacy, and trust issues, such as vulnerabilities, threats, and attacks. Addressing these challenges requires innovative solutions. New controls (solutions) are needed. Trust by design can be one of these controls, supporting security and privacy (among other system components). This paper proposes a Trusted loT (TloT), a trust-by-design model, and highlights the author's principal contributions. First, it advocates for implementing Tlo T through trust services. Second, it outlines an architecture for using trust by design in loT, including the design of its top-level structure and activities. Minor contributions include overviews of loT and trust, as well as looking at security and privacy in terms of security services and privacy services, respectively.

关键词： Privacy Ecosystems Security Internet of Things Arrays

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Large language models are fixated by red herrings: exploring creative problem solving and einstellung effect using the only connect wall dataset 23

Large language models are fixated by red herrings: exploring...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Saeid Alavi Naeini Raeid Saqur Mozhgan Saeidi John Giorgi Babak Taati Kite Research Institute Toronto Rehabilitation Institute University Health Network and Institute of Biomedical Engineering University of Toronto and Vector Institute for AI Department of Computer Science University of Toronto and Vector Institute for AI Department of Computer Science University of Toronto and Vector Institute for AI and Department of Biomedical Data Science Stanford University Department of Computer Science University of Toronto and Vector Institute for AI and Donnelly Centre for Cellular & Biomolecular Research University of Toronto Kite Research Institute Toronto Rehabilitation Institute University Health Network and Department of Computer Science University of Toronto and Institute of Biomedical Engineering University of Toronto and Vector Institute for AI

The quest for human imitative AI has been an enduring topic in AI research since its inception. The technical evolution and emerging capabilities of the latest cohort of large language models (LLMs) have reinvigorated the subject beyond academia to the cultural zeitgeist. While recent NLP evaluation benchmark tasks test some aspects of human-imitative behavior (e.g., BIG-bench's 'human-like behavior' tasks), few, if not none, examine creative problem. solving abilities. Creative problem solving in humans is a well-studied topic in cognitive neuroscience with standardized tests that predominantly use the ability to associate (heterogeneous) connections among clue words as a metric for creativity. Exposure to misleading stimuli — distractors dubbed red herrings — impede human performance in such tasks via the fixation effect and Einstellung paradigm. In cognitive neuroscience studies, such fixations are experimentally induced by pre-exposing participants to orthographically similar incorrect words to subsequent word-fragments or clues. The popular British quiz show Only Connect's Connecting Wall segment essentially mimics Mednick's Remote Associates Test (RAT) formulation with built-in, deliberate red herrings, which makes it an ideal proxy task to explore and study the fixation effect and Einstellung paradigm from cognitive neuroscience in LLMs. In this paper, we present the novel Only Connect Wall (OCW) dataset and report results from our evaluation of selected pre-trained language models and LLMs on creative problem solving tasks like grouping clue words by heterogeneous connections and identifying correct open knowledge domain connections in respective groups. We synthetically generate two additional datasets: OCW-Randomized, OCW-WordNet to further analyze our red-herrings hypothesis in language models. The code and link to the dataset are available at https://***/TaatiTeam/OCW.

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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