IEEE journal on selected areas in communications : a publication of the IEEE Communications Society最新文献

IEEE Journal on Selected Areas in Communications Publication Information IEEE通讯出版信息选定领域期刊

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-09-09 DOI: 10.1109/JSAC.2025.3602255

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IEEE Communications Society Information IEEE通信学会信息

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-09-09 DOI: 10.1109/JSAC.2025.3602257

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Guest Editorial: Building a More Secure Future: Developing Unbreakable Communication Protocols for the Quantum Era 嘉宾评论：构建更安全的未来：为量子时代开发牢不可破的通信协议

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-09-05 DOI: 10.1109/JSAC.2025.3567101

David S. L. Wei;Kaiping Xue;Tao Zhang;David Elkous;Lidong Chen;Carlo Ottaviani

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IEEE Journal on Selected Areas in Communications Publication Information IEEE通讯出版信息选定领域期刊

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-09-04 DOI: 10.1109/JSAC.2025.3579193

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IEEE Communications Society Information IEEE通信学会信息

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-09-04 DOI: 10.1109/JSAC.2025.3579195

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IEEE Journal on Selected Areas in Communications Publication Information IEEE通讯出版信息选定领域期刊

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-06-18 DOI: 10.1109/JSAC.2025.3576465

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Guest Editorial: Rethinking the Information Identification, Representation, and Transmission Pipeline: New Approaches to Data Compression and Communication 客座评论：信息识别、表示和传输管道的再思考：数据压缩和通信的新方法

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-06-18 DOI: 10.1109/JSAC.2025.3557932

Jun Chen;Alexandros G. Dimakis;Yong Fang;Ashish Khisti;Ayfer Özgür;Nir Shlezinger

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IEEE Communications Society Information IEEE通信学会信息

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-06-18 DOI: 10.1109/JSAC.2025.3576467

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Progressive Goal-Oriented Communications for Reinforcement Learning Control Over Multi-Tier Computing Systems 面向渐进目标的多层计算系统强化学习控制通信

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-06-10 DOI: 10.1109/JSAC.2025.3574624

Dezhao Chen;Tongxin Huang;Jianghong Shi;Xuemin Hong;Yang Yang

{"title":"Progressive Goal-Oriented Communications for Reinforcement Learning Control Over Multi-Tier Computing Systems","authors":"Dezhao Chen;Tongxin Huang;Jianghong Shi;Xuemin Hong;Yang Yang","doi":"10.1109/JSAC.2025.3574624","DOIUrl":"10.1109/JSAC.2025.3574624","url":null,"abstract":"The converging trends of reinforcement learning (RL) control and cloud-fog automation in industrial cyber-physical systems impose multiple challenges for communications to cope with stringent requirements in latency, reliability, control effectiveness and bifurcating user demands. Progressive goal-oriented (GO) communication is a promising technology to tackle the above challenges. This paper takes a two-step approach to design the first progressive codec of GO communications tailored for RL control tasks. The first step is to design a variable-rate coding scheme that extends the boundaries of rate regimes. This step is achieved by empowering the hierarchical variational autoencoder (HVAE) framework with novel algorithms such as mutual information based soft state abstraction (MISA). The second step is to transform variable-rate encoding into progressive encoding. This is achieved by applying residual-based encoding techniques upon latent representations learned by deep neural networks. Experiments on the Cartpole Swingup task demonstrate that the proposed progressive codec can facilitate smooth transitions from the ultra-low rate regime to regular rate regime, while achieving the state-of-the-art performance in terms of rate-distortion-effectiveness tradeoff.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 9","pages":"3056-3071"},"PeriodicalIF":17.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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QFEVAL: Quantum Federated Ensembled Variational Adaptive Learning for Dynamic Security Assessment in Cyber-Physical Systems 量子联合集成变分自适应学习在信息物理系统动态安全评估中的应用

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-06-10 DOI: 10.1109/JSAC.2025.3574588

Chao Ren;Ying-Peng Tang;Yulan Gao;Xian Sun;Kun Fu;Mikael Skoglund;Zhao Yang Dong;Han Yu;Anran Li;Ming Xiao

{"title":"QFEVAL: Quantum Federated Ensembled Variational Adaptive Learning for Dynamic Security Assessment in Cyber-Physical Systems","authors":"Chao Ren;Ying-Peng Tang;Yulan Gao;Xian Sun;Kun Fu;Mikael Skoglund;Zhao Yang Dong;Han Yu;Anran Li;Ming Xiao","doi":"10.1109/JSAC.2025.3574588","DOIUrl":"10.1109/JSAC.2025.3574588","url":null,"abstract":"In the era of smart cyber-physical grid, dynamic insecurity risk has become a significant concern due to the increasing integration of renewable energy sources and the inherent uncertainties in smart grid. Dynamic security assessment (DSA) has been adopted to hedge against such risks by estimating the stability of large-scale smart grids. Existing DSA approaches often involve complex high dimensional models which incur high communication and computational costs, hindering their practical adoption. In this paper, we address these limitations with the <underline>Quantum <underline>Federated <underline>Ensembled <underline>Variational <underline>Adaptive <underline>Learning (QFEVAL) approach for smart grid DSA. QFEVAL is designed to combine quantum machine learning and federated learning to handle the differential-algebraic equations that describe smart grid stability, providing an efficient way to deal with high-dimensional data and uncertainties. QFEVAL enables the training of the hybrid quantum-classical neural networks on distributed DSA datasets located at different nodes in smart grids, without requiring large numbers of parameters to be transmitted. QFEVAL accurately predicts the stability of the smart grid under various conditions, enabling the implementation of preventive stability control measures. Through extensive experiments, we demonstrate that QFEVAL achieves comparable performance to 9 state-of-the-art DSA approaches with more than 2 orders of magnitude fewer model parameter transmissions. QFEVAL paves the way for reliable, secure, and continuous electricity supply, offering a robust solution to the challenges of DSA in smart grids.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 9","pages":"3200-3213"},"PeriodicalIF":17.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0