IEEE Transactions on Network Science and Engineering最新文献_第3页

Resource Block-Granularity Precoding Optimization and Compression for Cell-Free Mobile Networks 无蜂窝移动网络的资源块粒度预编码优化与压缩

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-22 DOI: 10.1109/TNSE.2025.3563370

Zhiwei Chen;Kai Cai;Junliang Ye;Qiang Li;Xiaohu Ge

{"title":"Resource Block-Granularity Precoding Optimization and Compression for Cell-Free Mobile Networks","authors":"Zhiwei Chen;Kai Cai;Junliang Ye;Qiang Li;Xiaohu Ge","doi":"10.1109/TNSE.2025.3563370","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3563370","url":null,"abstract":"The forward-precoding scheme could significantly reduce downlink fronthaul traffic in cell-free mobile networks by compressing resource-block (RB)-granularity precoding matrices. Nevertheless, this scheme would result in a sum-rate degradation due to the compression distortion and the significant frequency-selective fading. To address this issue, the compressed RB-granularity precoding optimization is first formulated as a non-convex stochastic problem and then its lower bound is derived. By maximizing the lower bound, the stochastic non-convex problem is transformed into a deterministic RB-granularity precoding optimization problem and a matrix compression problem. For the deterministic RB-granularity precoding optimization problem, its stationary point is proved to be a linear combination of frequency channel matrices. Based on this property, an RB-granularity weighted minimum mean-square-error (RB-WMMSE) precoding algorithm is designed. For the matrix compression problem, a transformer-based vector-quantized variational autoencoder (TVQ-VAE) algorithm is designed to achieve a high ratio compression. Simulation results show that the proposed RB-WMMSE algorithm could improve the sum-rate by a maximum of 101% compared to the traditional RB-granularity precoding algorithm. When the compression distortion of precoding matrix is considered, the proposed TVQ-VAE algorithm could improve the sum-rate by a maximum of 106% compared to the traditional autoencoder algorithm.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3641-3655"},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty Quantification of Network Inference With Data Sufficiency 具有数据充分性的网络推理的不确定性量化

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-22 DOI: 10.1109/TNSE.2025.3563303

Bharat Singhal;Jorge Luis Ocampo-Espindola;K. L. Nikhil;Erik D. Herzog;István Z. Kiss;Jr-Shin Li

{"title":"Uncertainty Quantification of Network Inference With Data Sufficiency","authors":"Bharat Singhal;Jorge Luis Ocampo-Espindola;K. L. Nikhil;Erik D. Herzog;István Z. Kiss;Jr-Shin Li","doi":"10.1109/TNSE.2025.3563303","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3563303","url":null,"abstract":"Network inference, which involves reconstructing the connectivity structure of a network from recorded data, is essential for broadening our understanding of physical, biological, and chemical systems. Although data-driven network inference algorithms have made significant strides in recent years, determining how much data is required so that the inferred network topology faithfully mirrors the underlying network remains an essential but often overlooked subject. In this paper, we present a statistical method to determine whether the recorded data carries sufficient variability to ensure an accurate reconstruction of the true network topology. Our approach leverages parametric confidence intervals to establish the bounds of true connection strengths, which subsequently enable the uncertainty quantification of inferred connectivity. The proposed technique is validated using noisy data generated from networks of Kuramoto and Stuart-Landau oscillators. Additionally, the method is applied to experimentally obtained data from an electrochemical oscillator network, where we find that the data sufficiency technique can successfully predict the accuracy of the inferred network.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3600-3610"},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Estimation and Selective Control of Epidemics Spreading Over Complex Networks 流行病在复杂网络上传播的分布式估计与选择性控制

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-22 DOI: 10.1109/TNSE.2025.3563261

Matthias Pezzutto;Ouassim Benhamouche;Nicolas Bono Rossello;Emanuele Garone

{"title":"Distributed Estimation and Selective Control of Epidemics Spreading Over Complex Networks","authors":"Matthias Pezzutto;Ouassim Benhamouche;Nicolas Bono Rossello;Emanuele Garone","doi":"10.1109/TNSE.2025.3563261","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3563261","url":null,"abstract":"In this work we propose a novel control scheme for the containment of infectious epidemics spreading over networks of individuals. The proposed control scheme consists of a distributed estimator, which provides the estimate of the state of each individual, and a resource allocation policy, which selects potential individuals to test and quarantine, and to vaccinate, taking into account possibly limited numbers of tests and vaccine doses available. The proposed strategy provides interventions on an individual basis rather than average actions at the population level, as commonly done in most of the existing literature. Moreover it exploits the available observations on the individual states in a closed-loop fashion, contrary to the open-loop approach used on the part of the literature focusing on individual-based control strategies. Simulation results show how the proposed approach produces a clear improvement in the containment of epidemics compared to traditional strategies and that our approach is robust with respect to uncertainties on the knowledge of the network.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3577-3589"},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advection Dynamics in Traffic Networks: Modeling, Analysis, and Optimization 交通网络中的平流动力学：建模、分析和优化

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-22 DOI: 10.1109/TNSE.2025.3563317

David Angulo-Garcia;Daniel Burbano

{"title":"Advection Dynamics in Traffic Networks: Modeling, Analysis, and Optimization","authors":"David Angulo-Garcia;Daniel Burbano","doi":"10.1109/TNSE.2025.3563317","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3563317","url":null,"abstract":"Traffic congestion is a critical challenge in urban environments, negatively affecting quality of life and economic productivity. Mathematical models have sought to provide foundational insights, with microscopic models capturing individual behaviors and macroscopic models offering computational efficiency. Microscopic models, however, are computationally intensive, while macroscopic models lack the detail required for devising traffic solutions. This work introduces a novel, intermediate-resolution framework based on networks of dynamic systems governed by ordinary differential equations with discontinuous right-hand sides. Our approach employs graph-based advection dynamics, where nodes represent discrete road segments with capacity constraints. Through analytical derivations, we gain insights into the equilibrium states that emerge as roads experience congestion. We further exploit the mathematical tractability of our model to formulate well-posed optimization problems designed to mitigate congestion. The efficacy of our framework is validated using both representative numerical examples and a real-world dataset from Manhattan's Upper West Side in New York City, USA. Notably, our findings suggest that sparse, time-dependent interventions on critical road segments can be used to effectively alleviate congestion throughout the day.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3611-3624"},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Scale Contrast for Global-Interaction Graph Representation Learning 全局交互图表示学习的多尺度对比

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-21 DOI: 10.1109/TNSE.2025.3561783

Bin Yu;Xuyang Zhang;Yu Xie;Chen Zhang;Xinlei Wang;Wenjie Mao

{"title":"Multi-Scale Contrast for Global-Interaction Graph Representation Learning","authors":"Bin Yu;Xuyang Zhang;Yu Xie;Chen Zhang;Xinlei Wang;Wenjie Mao","doi":"10.1109/TNSE.2025.3561783","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3561783","url":null,"abstract":"Recently, significant strides have been made in the field of representation learning. Nevertheless, prior methods have predominantly centered on supervised learning, which necessitates a reliance on costly labeled datasets. In response to this challenge, graph contrastive learning leverages the potential of unlabeled data by maximizing the consistency between similar graph pairs, extracting effective representations of graph data. However, existing methods lack the consideration of multi-scale information and tend to focus on extracting local graph features, especially overlooking the interdependencies between graph-level representations. To overcome these challenges, we present an innovative framework, Multi-Scale Contrast for Global-Interaction Graph Representation Learning (MSCGI) to capture more abundant features. Firstly, our framework models the given graph at multiple scales, with subgraphs at each scale encoded by independent encoders. Subsequently, we design a Global Interaction module that generates edges among graphs based on the similarity of graph-level representations, thereby constructing a “global graph” to capture the interrelations among graphs. Finally, the framework maximizes the mutual information across the various scales to capture hierarchical information. Comprehensive experiments demonstrate that MSCGI outperforms the state-of-the-art unsupervised methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3541-3554"},"PeriodicalIF":7.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Intrusion Detection in AMI Systems Based on Federated Semi-Supervised Learning 基于联邦半监督学习的AMI系统高效入侵检测

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-21 DOI: 10.1109/TNSE.2025.3562751

Zhuoqun Xia;Haidong Tang;Zhenzhen Hu;Hongmei Zhou

{"title":"Efficient Intrusion Detection in AMI Systems Based on Federated Semi-Supervised Learning","authors":"Zhuoqun Xia;Haidong Tang;Zhenzhen Hu;Hongmei Zhou","doi":"10.1109/TNSE.2025.3562751","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3562751","url":null,"abstract":"The advanced metering infrastructure (AMI) network is the most critical part of a smart grid, and it faces serious security challenges from network attacks. Federated learning (FL) is a common method for addressing network security problems; however, it has several shortcomings, such as difficulty in obtaining labeled data and high communication costs. Therefore, in this paper, an efficient intrusion detection method based on federated semi-supervised learning is proposed to improve the efficiency of AMI network intrusion detection and reduce communication overhead. First, an intrusion detection framework based on federated distillation (FD) is established to address intense assumption dependency problems and reduce communication overhead. Then, an efficient intrusion detection algorithm is used to improve the classification performance. Finally, the designed deep convolutional generative adversarial network (DCGAN) model is used to obtain high-quality sample data. The experimental results show that the scheme achieves an accuracy of 99.58%, a communication overhead of 75 MB, and a reduction in the false-positive rate of 6%.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3565-3576"},"PeriodicalIF":7.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning-Augmented Online Minimization of Age of Information and Transmission Costs 信息时代和传输成本的学习增强在线最小化

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-18 DOI: 10.1109/TNSE.2025.3561736

Zhongdong Liu;Keyuan Zhang;Bin Li;Yin Sun;Y. Thomas Hou;Bo Ji

{"title":"Learning-Augmented Online Minimization of Age of Information and Transmission Costs","authors":"Zhongdong Liu;Keyuan Zhang;Bin Li;Yin Sun;Y. Thomas Hou;Bo Ji","doi":"10.1109/TNSE.2025.3561736","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3561736","url":null,"abstract":"We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the <italic>Age-of-Information</i>. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) <italic>consistency</i>: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) <italic>robustness</i>: ensuring worst-case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning-augmented algorithm achieves both consistency and robustness.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3480-3496"},"PeriodicalIF":7.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formation and Obstacle Avoidance Control Based on Multi-Agent Reinforcement Learning 基于多智能体强化学习的编队与避障控制

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-17 DOI: 10.1109/TNSE.2025.3561744

Fuxi Niu;Xiaohong Nian;Chao Pan;Xunhua Dai;Haibo Wang;Hongyun Xiong

引用次数: 0

Effects of Cyberattacks on Regional Traffic Networks in a Connected Vehicle Environment 车联网环境下网络攻击对区域交通网络的影响

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-17 DOI: 10.1109/TNSE.2025.3560677

Liangwen Wang;Heng Ding;Xiaoyan Zheng;Weihua Zhang

{"title":"Effects of Cyberattacks on Regional Traffic Networks in a Connected Vehicle Environment","authors":"Liangwen Wang;Heng Ding;Xiaoyan Zheng;Weihua Zhang","doi":"10.1109/TNSE.2025.3560677","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3560677","url":null,"abstract":"Connected Vehicle (CV) technology aims to enhance information transmission and resource sharing. However, the open-access environment makes the CVs vulnerable to cyberattacks, causing concerns about traffic downgrading or even paralysis. Existing studies on the traffic impact of cyberattacks primarily focus on individual vehicles or queues, ignoring the macro impact of attacks on the road network. To analyze the impact of cyberattacks on road network at a regional level, this paper carries out three works. First, the traffic modelling under two cyberattack models from the queuing scale and the routing scale is provided, respectively. Second, we analyze the general analytical form of cyberattacks causing impacts on regional traffic based on Macroscopic fundamental diagram (MFD) theory and develop an evaluation index of road network efficiency under cyberattacks. Third, according to these two models and the evaluation index, the impacts of cyberattacks on the traffic performance of the road network are analyzed. The results showed that both scale models of cyberattacks affect the MFD, and the road network performance, which makes the network more vulnerable. The results also showed that the rerouting capability of CVs has a significant different effect on the road network performance under different cyberattacks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3434-3450"},"PeriodicalIF":7.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

User-Centric Clustering and Beamforming Design for Satellite-Assisted Cell-Free Networks 卫星辅助无蜂窝网络的以用户为中心的聚类和波束形成设计

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-16 DOI: 10.1109/TNSE.2025.3561505

Yunseong Lee;Chihyun Song;Donghyun Lee;Wonjong Noh;Sungrae Cho

{"title":"User-Centric Clustering and Beamforming Design for Satellite-Assisted Cell-Free Networks","authors":"Yunseong Lee;Chihyun Song;Donghyun Lee;Wonjong Noh;Sungrae Cho","doi":"10.1109/TNSE.2025.3561505","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3561505","url":null,"abstract":"The exponential growth in mobile traffic has driven significant interest in cell-free networks, particularly those integrated with satellites, to support high data rates and overcome geographic limitations. In this paper, we propose a novel dynamic clustering and beamforming control that maximizes the minimum data rate in satellite-assisted user-centric cell-free networks. Specifically, we formulate a nonconvex max-min fairness problem to optimize clustering and beamforming under a user-centric cluster size and transmit power constraints. To find a solution, this problem is decomposed into two subproblems: clustering and beamforming. A modified Gale-Shapley-based user-centric clustering algorithm is proposed for the clustering subproblem, which is solved on a long-term basis using statistic channel information. The beamforming subproblem transform is transformed into a semidefinite problem using linear approximation and a linear matrix inequality. We propose a robust beamforming algorithm that considers imperfect instantaneous channel state information, solved on a short-term basis. Lastly, we analyze the computational complexity, revealing that the proposed scheme has polynomial complexity. We evaluate its performance under user mobility in satellite-assisted cell-free networks. The proposed algorithm offers significant performance gains, achieving up to a 15.15% higher minimum data rate and a 14.14% higher average data rate than zero-forcing beamforming and distance-based clustering benchmark schemes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3467-3479"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0