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3rd CSI/CPSSI International Symposium on Real-Time and Embedded Systems and Technologies, RTEST 2020

作者： Thiele, Lothar Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology Zurich Switzerland

ISBN: (纸本)9781728175515

If visions and forecasts of industry come true then billions of interconnected embedded devices will soon surround us. We have the legitimate expectation that the individual devices as well as the overall system behaves in a reliable and predictable manner. We expect correct and timely results from sensing, computation, communication and actuation due to economic importance or even catastrophic consequences if the overall system is not working correctly. It will be argued that one of the major reasons for unpredictable and unwanted behavior of IoT systems is due to interference from external or internal processes. We need novel architectural concepts, an associated design process and validation strategies to satisfy the strongly conflicting requirements and associated design challenges of platforms for IoT: handle at the same time limited available resources, adaptive run-time behavior, and predictability. The presentation will introduce recent advances in this direction. © 2020 IEEE.

关键词： Internet of things

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Transfer Learning assisted Beam Training via Large-Scale Intelligent Omni-surface in Dynamic Environments

Transfer Learning assisted Beam Training via Large-Scale Int...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zhihan Chen Shuhang Zhang Shuhao Zeng Boya Di School of Electronics Engineering and Computer Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China

Intelligent omni-directional surfaces (IOS), which can simultaneously reflect and refract incident signals, are considered as a promising solution for enhancing communication quality. To conduct joint beamforming of the BS and IOS, beam training is introduced such that perfect channel state information is not required anymore. However, the propagation environment is usually dynamically varying in practice, leading to frequent beam training procedures and huge training overhead. In this paper, we propose a transfer learning based beam training scheme for the IOS-assisted multi-user system to adapt to the dynamically changing propagation environment. We first build on an offline phase to train a beam prediction model that outputs the optimal beam with the highest data rate given only the received power of a small number of beams as the input. Then a transfer learning based method is developed such that the above beam prediction model can be updated to adapt to the dynamic environment rapidly. Simulation results demonstrate that the proposed scheme outperforms the existing beam training schemes in dynamic environments in terms of the convergence speed and the sum rate.

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Waveform Precoding Design for Mobile Crowd ISCC System Using Mean Field Game

Waveform Precoding Design for Mobile Crowd ISCC System Using...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Wang, Dezhi Huang, Chongwen He, Jiguang Zhu, Chen Wang, Wei Chen, Xiaoming Zhang, Zhaoyang Han, Zhu Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China Technology Innovation Institute Abu Dhabi United Arab Emirates Polytechnic Institute Zhejiang University Hangzhou China University of Houston Department of Electrical and Computer Engineering HoustonTX United States Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350370218

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation (ISCC) systems with various applications such as smart home and connected cars. However, as the number of integrated sensing and communication (ISAC) devices grows, there exist intensive interactions among ISAC devices in the processes of data collection and processing. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC system and propose an efficient waveform precoding design algorithm based on the mean field game (MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first employ the MFG method to transform the influence from other ISAC devices into the mean field term. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem based on the MFG. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem. Finally, numerical results demonstrate that the proposed algorithm converges quickly and can reduce energy consumption by about 10% compared with the existing algorithms. © 2023 IEEE.

关键词： communication computation integrated sensing mean field game mobile crowd sensing Waveform precoding design

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Integrated Sensing and Communication for 6G Holographic Digital Twins

arXiv

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arXiv 2025年

作者： Zhang, Haijun Zhang, Ziyang Liu, Xiangnan Li, Wei Li, Haojin Sun, Chen Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Sony China Research Laboratory Beijing100027 China

With the advent of 6G networks, offering ultra-high bandwidth and ultra-low latency, coupled with the enhancement of terminal device resolutions, holographic communication is gradually becoming a reality. Holographic digital twin (HDT) is considered one of key applications of holographic communication, capable of creating virtual replicas for real-time mapping and prediction of physical entity states, and performing three-dimensional reproduction of spatial information. In this context, integrated sensing and communication (ISAC) is expected to be a crucial pathway for providing data sources to HDT. This paper proposes a four-layer architecture assisted by ISAC for HDT, integrating emerging paradigms and key technologies to achieve low-cost, high-precision environmental data collection for constructing HDT. Specifically, to enhance sensing resolution, we explore super-resolution techniques from the perspectives of parameter estimation and point cloud construction. Additionally, we focus on multi-point collaborative sensing for constructing HDT, and provide a comprehensive review of four key techniques: node selection, multi-band collaboration, cooperative beamforming, and data fusion. Finally, we highlight several interesting research directions to guide and inspire future work. Copyright © 2025, The Authors. All rights reserved.

关键词： Data fusion

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Extendedsketch+: Super Host Identification and Network Host Trust Evaluation with Memory Efficiency and High Accuracy

SSRN

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SSRN 2022年

作者： Han, Hui Jing, Xuyang Yan, Zheng Pedrycz, Witold State Key Laboratory on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Electrical and Computer Engineering University of Alberta Canada

Host cardinality estimation is one crucial task in network traffic measurement. Super host is the host that exhibits anomalies in host cardinality and it is usually related to network abnormal events. Therefore, accurate host caidinality estimation is the premise of super host identification and it can be used to measure the trust degree of a network. Sketches have been widely used in super host identification. However, expanding network scale and increasing link rate bring challenges to the efficiency and accuracy of sketch-based super host identification. The size of the counters in most sketches requires a trade-off between memory resources and accuracy. To meet the need of measuring high cardinalities, large size counters are applied, which leads to memory waste in monitoring low cardinality hosts. On the other hand, most sketches have high computational overhead when tracking superhosts, resulting in inefficiency. In order to address these issues, we propose a novel memory efficient and reversible sketch, named ExtendedSketch+, to provide accurate host cardinality estimation with the purpose of super host identification and network host trust evaluation. ExtendedSketch+ achieves both high memory utilization efficiency and high accuracy, in addition to high super host identification efficiency. It applies extensible counters to record unbalanced host cardinality distribution, by adaptively expanding the counters with the increase of cardinality. It adopts a lossless traffic information transfer strategy during counter extension to ensure the accuracy of cardinality estimation. It can directly tracks the host with the highest cardinality in each bucket, which greatly improves the identification efficiency of super hosts. Based on accurate cardinality estimation of ExtendedSketch+, we can further accurately evaluate host trust degree. We theoretically analyze ExtendedSketch+ with regard to its space and time complexities and estimation accuracy. We conduct perform

关键词： Efficiency

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STARS-Enabled Full-Duplex Two-Way mMIMO System Under Spatially-Correlated Channels

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IEEE Transactions on Vehicular Technology 2025年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Chatzinotas, Symeon Kaklamani, Dimitra I. Venieris, Iakovos S. University of Hertfordshire Communications and Intelligent Systems Research Group HatfieldAL10 9AB United Kingdom University of Luxembourg SnT Luxembourg Microwave and Fiber Optics Laboratory Greece National Technical University of Athens Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering Athens Zografou15780 Greece

Low-cost reconfigurable intelligent surfaces (RISs) are being considered as promising physical-layer technology for next-generation wireless networks due to their ability to re-engineer the propagation environment by tuning their elements. Although RIS-aided systems have many advantages, one of their major bottlenecks is that they can provide coverage only in front of the surface. Fortunately, simultaneous transmitting and reflecting surface (STARS)-assisted systems have emerged to fill this gap by providing 360° wireless coverage. In parallel, full-duplex (FD) communication offers a higher achievable rate through efficient spectrum utilization compared to the half-duplex (HD) counterpart. Moreover, two-way/bi-directional communications in an FD system can further enhance the system's spectral efficiency. Hence, in this paper, we propose a STARS-enabled massive MIMO deployment in an FD two-way communication network for highly efficient spectrum utilization, while covering the dead zones around the STARS. This model enables simultaneous information exchange between multiple nodes, while potentially doubling the spectral efficiency (SE). By invoking the use-and-then-forget (UaTF) combining scheme, we derive a closed-form expression for an achievable SE at each user of the system considering both uplink and downlink communications based on statistical channel state information (CSI), while also accounting for imperfect CSI and correlated fading conditions. Moreover, we formulate an optimization problem to obtain an optimal passive beamforming matrix design at the STARS that maximizes the sum achievable SE. The considered problem is non-convex and we propose a provably-convergent low-complexity algorithm, termed as projected gradient ascent method (ProGrAM), to obtain a stationary solution. Extensive numerical results are provided to establish the performance superiority of the FD STARS-enabled system over the HD STARS-enabled and FD conventional RIS (cRIS)-enabled coun

关键词： Digital television

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Hybrid Near-field and Far-field Localization with Holographic MIMO

arXiv

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arXiv 2025年

作者： Cao, Mengyuan Zhang, Haobo Eldar, Yonina C. Zhang, Hongliang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Department of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Due to its ability to precisely control wireless beams, holographic multiple-input multiple-output (HMIMO) is expected to be a promising solution to achieve high-accuracy localization. However, as the scale of HMIMO increases to improve beam control capability, the corresponding near-field (NF) region expands, indicating that users may exist in both NF and far-field (FF) regions with different electromagnetic transmission characteristics. As a result, existing methods for pure NF or FF localization are no longer applicable. We consider a hybrid NF and FF localization scenario in this paper, where a base station (BS) locates multiple users in both NF and FF regions with the aid of a reconfigurable intelligent surface (RIS), which is a low-cost implementation of HMIMO. In such a scenario, it is difficult to locate the users and optimize the RIS phase shifts because whether the location of the user is in the NF or FF region is unknown, and the channels of different users are coupled. To tackle this challenge, we propose a RIS-enabled localization method that searches the users in both NF and FF regions and tackles the coupling issue by jointly estimating all user locations. We derive the localization error bound by considering the channel coupling and propose an RIS phase shift optimization algorithm that minimizes the derived bound. Simulations show the effectiveness of the proposed method and demonstrate the performance gain compared to pure NF and FF techniques. Copyright © 2025, The Authors. All rights reserved.

关键词： Communication channels (information theory)

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Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition 24

Generating Action-conditioned Prompts for Open-vocabulary Vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jia, Chengyou Luo, Minnan Chang, Xiaojun Dang, Zhuohang Han, Mingfei Wang, Mengmeng Dai, Guang Dang, Sizhe Wang, Jingdong School of Computer Science and Technology MOEKLINNS Lab Xi'an Jiaotong University Shaanxi Xi'an China University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China ReLER Lab AAII University of Technology Sydney SydneyNSW Australia Zhejiang University of Technology College of Computer Science and Technology China SGIT AI Lab State Grid Corporation of China Beijing China Baidu Inc Beijing China United Arab Emirates Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China SGIT AI Lab State Grid Corporation of China China School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798400706868

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability. © 2024 ACM.

关键词： Embeddings

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An Integrated Substrate Gap Waveguide Filter Metalens Antenna for 6G THz Band

An Integrated Substrate Gap Waveguide Filter Metalens Antenn...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Da Hou Sihong Lin Dongya Shen Hong Yuan Biao Wang Xiupu Zhang Yunnan Engineering Laboratory of Cloud Wireless & Heterogeneous Networks Yunnan University Kunming China Department of Electrical and Computer Engineering Concordia University Montreal Canada

Terahertz (THz) technology has become one of the key technologies of future wireless communications. In this paper, combining Through-Silicon Via (TSV) with Integrated Substrate Gap Wave-guide (ISGW), a terahertz filter metalens antenna suitable for 6G wireless communication is proposed. Based on TSV based ISGW that is capable of suppressing surface waves and reducing antenna losses, ultra-wideband filtering function is formed by circular ring resonators with different impedance branches, broadband radiation characteristics are obtained by interdigital slots, and Huygens meta-surface lens is used to improve antenna gain. A 46.1% relative bandwidth in 142-227 GHz and more than 14 dBi gain are achieved with a small footprint. This antenna is suitable for 6G THz communications.

关键词： 6G mobile communication Microwave antennas Wireless communication Slot antennas Resonator filters Ultra wideband antennas Impedance

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Computing Power in the Sky: Digital Twin-assisted Collaborative Computing with Multi-UAV networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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