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Tensor-based Channel Estimation for Extremely Large-Scale MIMO-OFDM with Dynamic Metasurface Antennas

IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Ruoyu Chen, Guangyi Cheng, Lei Guan, Xinrong Wu, Qingqing W., Wu R., Zhang Ministry of Education School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China College of Communications Engineering Army Engineering University of Pla Nanjing210007 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of H ong Kong Shenzhen Guangdong518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Extremely large-scale multiple-input multiple-output (XL-MIMO) with orthogonal frequency division multiplexing (OFDM) transmission can provide unprecedented improvement in spectral efficiency and data rate. Dynamic metasurface antennas (DMAs) have been proposed as a cost-effective and power-efficient solution for realizing XL-MIMO systems. However, the extremely large number of antennas in XL-MIMO-OFDM with DMAs poses critical challenges in acquiring accurate channel state information. To address this issue, we propose in this paper a tensor-based channel estimation method for frequency-selective XL-MIMO-OFDM systems with DMAs. We first characterize the configurable property of DMAs and propose a microstrip-sequential channel training method with quasi-dynamically adjustable metamaterial elements, by representing the received frequency-domain training signals as a fourth-order tensor which admits the canonical polyadic decomposition. Then, by exploiting the sparsity of XL-MIMO channels, we propose a two-stage tensor decomposition-based channel estimation algorithm, where the four coupling factor matrices are obtained without the need of iterative refinement, and the channel multipath parameters can be extracted for reconstructing the entire high-dimensional channel matrix. In addition, we analyze the uniqueness condition for the proposed tensor-based channel estimation method, which reveals that the required channel training overhead is only proportional to the number of channel multipaths, instead of that of metamaterial elements and microstrips. Numerical results demonstrate the superior performance of our proposed design with significantly reduced training overhead as compared to various benchmark schemes. © 2025 IEEE.

关键词： Frequency domain analysis

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OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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WebShield 5.0: Harnessing AI and NLP to combat web threats in Industry 5.0

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Alexandria engineering Journal 2025年 127卷 677-689页

作者： Verma, Priyanka O'Shea, Donna Newe, Thomas Vidyarthi, Ankit Gupta, Deepak Ali, Jabir Aldawsari, Hamad Breslin, John G. School of Computer Science University of Galway University road GalwayH91 TK33 Ireland Department of Electronics and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Digital Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Department of CSE&IT Jaypee Institute of Information Technology sector 62 Noida201309 India Maharaja Agrasen Institute of Technology Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida Delhi India Department of Computer Science Haql University College University of Tabuk Tabuk Saudi Arabia Data Science Institute University of Galway University road GalwayH91 TK33 Ireland

Industry 5.0 characterized by the integration of human intelligence and advanced technologies is inherently more connected and interdependent than previous industrial paradigms. This increased connectivity exposes to various web-based attacks and calls for strong security controls. To address the challenges and enhance attack detection, this paper introduces Ingress Manager (IM), a novel approach that amalgamates Natural Language Processing (NLP) with Machine Learning (ML) to mitigate web-based threats. By combining multimodal data and utilizing the Mayfly optimization algorithm for feature selection, IM carries out a thorough analysis for efficient web-based attack detection. Mayfly Optimization is considered to be a variation of Particle Swarm Optimization (PSO), combining the benefits of Firefly Algorithm, Genetic Algorithm (GA), and PSO. Experiments on the HTTP CSIC-2010 dataset show that this integration is effective, as evidenced by the notable gains in accuracy, precision, and F-score above baseline models. Notable performance metrics such as accuracy of 98.5753% along with thorough component analysis (ablation study) add deeper understanding to the proposed approach. The paper's contributions lie in its utilization of Industry 5.0 principles, the incorporation of Mayfly optimization for feature selection, and the innovative combination of NLP and ML for robust web-based attack detection. © 2025

关键词： Genetic algorithms

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Context-aware Inductive Graph Matrix Completion with Sentence BERT

Context-aware Inductive Graph Matrix Completion with Sentenc...

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IEEE International Conference on Web Services (ICWS)

作者： Donghee Han Daehee Kim Keejun Han Mun Yong Yi Graduate School of Data Science Korea Advanced Institute of Science and Technology Daejeon Republic of Korea School of Computer Engineering Hansung University Seoul Republic of Korea Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

Existing graph neural network (GNN) based recommendation models depend highly on initial node features and graph structures. Most prior studies do not support the use of additional information on user-item interactions and adopt a transductive method, which has limitations for actual web service. To overcome these problems, we propose Context-aware Inductive Graph Matrix Completion (CGMC), which utilizes context vectors that represent user-item interactions and user-item bipartite graph structures in an inductive manner. Our method constructs context vectors from the review, timestamp, and rating and uses them as edge features of the user-item bipartite graph. Relations between homogeneous nodes are also constructed based on common neighbors. Our model combines context vectors and relational information using Context-aware Graph Attention Networks and Edge Fusion Graph Convolutional Networks. We conducted extensive experiments using six real-world datasets, and the results show that the proposed model achieves superior performances over other competing models. Furthermore, we analyzed the inductive characteristics of CGMC through the cross-domain transferability. The source code is available in the repository at [https://***/venzino-han/CGMC_SBERT].

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Using Causal Inference to Explore Government Policy Impact on computer Usage

arXiv

引用

arXiv 2025年

作者： Zhu, Mingjia Wang, Lechuan Sebot, Julien Arbab, Bijan Salimi, Babak Cloninger, Alexander University of California Computer Science and Engineering San Diego United States University of California Department of Mathematics San Diego United States Intel Corporation United States University of California Halicioğlu Data Science Institute San Diego United States

We explore the causal relationship between COVID-19 lockdown policies and changes in personal computer usage. In particular, we examine how lockdown policies affected average daily computer usage, as well as how it affected usage patterns of different groups of users. This is done through a merging of the Oxford Policy public data set, which describes the timeline of implementation of COVID policies across the world, and a collection of Intel’s data Collection & Analytics (DCA) telemetry data, which includes millions of computer usage records and updates daily. Through difference-indifference, synthetic control, and change-point detection algorithms, we identify causal links between the increase in intensity (watts) and time (hours) of computer usage and the implementation of work from home policy. We also show an interesting trend in the individual’s computer usage affected by the policy. We also conclude that computer usage behaviors are much less predictable during reduction in COVID lockdown policies than during increases in COVID lockdown policies. Copyright © 2025, The Authors. All rights reserved.

关键词： Personal computers

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Deep Graph Anomaly Detection: A Survey and New Perspectives

arXiv

引用

arXiv 2024年

作者： Qiao, Hezhe Tong, Hanghang An, Bo King, Irwin Aggarwal, Charu Pang, Guansong School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science University of Illinois Urbana-Champaign United States College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science & Engineering Chinese University of Hong Kong Hong Kong IBM T. J. Watson Research Center United States

Graph anomaly detection (GAD), which aims to identify unusual graph instances (e.g., nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model designs for tackling open GAD problems. To this end, in this work we aim to present a comprehensive review of deep learning approaches for GAD. Existing GAD surveys are focused on task-specific discussions, making it difficult to understand the technical insights of existing methods and their limitations in addressing some unique challenges in GAD. To fill this gap, we first discuss the problem complexities and their resulting challenges in GAD, and then provide a systematic review of current deep GAD methods from three novel perspectives of methodology, including GNN backbone design, proxy task design for GAD, and graph anomaly measures. To deepen the discussions, we further propose a taxonomy of 13 fine-grained method categories under these three perspectives to provide more in-depth insights into the model designs and their capabilities. To facilitate the experiments and validation of the GAD methods, we also summarize a collection of widely-used datasets for GAD and empirical performance comparison on these datasets. We further discuss multiple important open research problems in GAD to inspire more future high-quality research in this area. A continuously updated repository for GAD datasets, links to the codes of GAD algorithms, and empirical comparison is available at https://***/mala-lab/Awesome-Deep-G

关键词： Contrastive Learning

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Identification of Alterations in Surgical Wounds Through the Application of Artificial Intelligence in Digital Images

Identification of Alterations in Surgical Wounds Through the...

引用

Biomedical engineering and Bioengineering (CI-IB&BI), IEEE International Congress of

作者： Javier Armando Rodríguez Prada Álvaro Augusto Cote Flórez Amolfi Hernando Pineda Gómez Hernán Darío Vargas Cardona Research and Education Unit Instituto del Corazón de Bucaramanga Bucaramanga Colombia Engineering and Sciences (Master in Data Science) Pontificia Universidad Javeriana Cali Colombia Department of Electronics and Computer Science Pontificia Universidad Javeriana Cali Colombia

ISBN: (数字)9798331532352

ISBN: (纸本)9798331532369

Globally, surgical site infections are common complications that are both serious and costly. While telemedicine has enhanced the remote assessment of surgical wounds, it still faces limitations. This study introduces a convolutional neural network (CNN) model designed to automatically classify digital images of surgical wounds as either altered or unaltered. The study utilized a dataset of 4,262 segmented and expert-labeled images. The CNN model achieved an accuracy of 83.46%, a sensitivity of 81.54%, and an AUROC of 92.22%. Although the MobileNet model demonstrated acceptable performance, it was less effective in comparison. The findings s uggest t hat C NNs a re e ffective for classifying images of surgical wounds, with potential for further improvement using advanced techniques and a multidisciplinary expert panel.

关键词： Sensitivity Digital images Telemedicine Transfer learning Surgery Wounds Convolutional neural networks Optimization Monitoring Biomedical engineering

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PC-based Intelligent Traffic Monitoring System with Real-time Analysis for Smart Cities 14

PC-based Intelligent Traffic Monitoring System with Real-tim...

引用

14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Lau, Chi-Yat Yuen, Man-Ching Yueng, Ka-Ho Fan, Cheuk-Pan Ko, On-Yi Ngan, Lit-Wang Tam, Wing-Chun Yeung, Wai-Nam Vocational Training Council Department of Information Technology Hong Kong Hong Kong Hong Kong Shue Yan University IFREE GROUP Innovation and Research Centre Department of Applied Data Science Hong Kong Hong Kong Limited Hong Kong Hong Kong City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Chinese University of Hong Kong Department of Statistics Hong Kong Hong Kong National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan Taiwan University of the West of England Bristol Department of Computer Science and Creative Technologies Bristol United Kingdom

ISBN: (纸本)9781665421041

In recent years, Hong Kong has been experiencing severe traffic problems and especially traffic congestion. In this paper, we proposed a traffic monitoring system which is PC-based, no infrastructure requirement but suitable for the complex road networks in Hong Kong. Besides, our system is designed to recognize vehicle type and plate number for vehicles commonly used in Hong Kong. By analyzing the data collected on road usage, we present our findings for decision-making regarding traffic policies. © 2022 IEEE.

关键词： Smart city

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Towards Adversarially Robust dataset Distillation by Curvature Regularization

arXiv

引用

arXiv 2024年

作者： Xue, Eric Li, Yijiang Liu, Haoyang Wang, Peiran Shen, Yifan Wang, Haohan Department of Computer Science University of Toronto Canada Electrical and Computer Engineering University of California San Diego United States School of Information Sciences University of Illinois Urbana-Champaign United States Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information, so that models trained on the distilled datasets can achieve a comparable accuracy while saving significant computational loads. Recent research in this area has been focusing on improving the accuracy of models trained on distilled datasets. In this paper, we aim to explore a new perspective of DD. We study how to embed adversarial robustness in distilled datasets, so that models trained on these datasets maintain the high accuracy and meanwhile acquire better adversarial robustness. We propose a new method that achieves this goal by incorporating curvature regularization into the distillation process with much less computational overhead than standard adversarial training. Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks. Our implementation is available at: https://***/yumozi/GUARD. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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