IEEE Transactions on Industrial Cyber-Physical Systems最新文献_第7页

Prognostics for the Sustainability of Industrial Cyber-Physical Systems: From an Artificial Intelligence Perspective 工业网络物理系统可持续性诊断：从人工智能的角度

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-25 DOI: 10.1109/TICPS.2024.3433492

Jiusi Zhang;Jilun Tian;Hao Luo;Shimeng Wu;Shen Yin;Okyay Kaynak

{"title":"Prognostics for the Sustainability of Industrial Cyber-Physical Systems: From an Artificial Intelligence Perspective","authors":"Jiusi Zhang;Jilun Tian;Hao Luo;Shimeng Wu;Shen Yin;Okyay Kaynak","doi":"10.1109/TICPS.2024.3433492","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3433492","url":null,"abstract":"As industrial cyber-physical systems (ICPS) play an increasingly pivotal role in the new industrial paradigm, their sustainability has become the current research focus. Remaining useful life (RUL) prediction, also known as prognostics, is critically significant for the sustainability of ICPS. The prognostics involve utilizing process monitoring devices within ICPS to acquire real-time operational data. Based on the trends observed in the monitored data, the analysis predicts when potential system failures may occur. Accurate prognostics allow real-time monitoring of the system's health status, which enables early warning of possible faults and system reliability. The primary objective of this paper is to provide readers with a timely survey and review that reveals the current research status, development trends, and common challenges in the prognostics domain within ICPS. From the perspective of artificial intelligence (AI), the paper comprehensively reviews predictive approaches based on stochastic process, machine learning, and their hybrid applications. Through a comprehensive comparison of existing approaches, the paper delves into the strengths and weaknesses of these approaches. Furthermore, facing some cutting-edge issues in existing RUL prediction approaches for ICPS, this paper analyzes some pioneering investigations that have achieved great results. Finally, the paper explores the opportunities and challenges of prognostics from the perspective of artificial intelligence, which aims to drive the sustainability of ICPS.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"495-507"},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368626","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

Optimizing V2G Dynamics: An AI-Enhanced Secure Protocol for Energy Management in Industrial Cyber-Physical Systems 优化 V2G 动态：用于工业网络物理系统能源管理的人工智能增强型安全协议

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-24 DOI: 10.1109/TICPS.2024.3432851

Shafiq Ahmed;Mohammad Hossein Anisi

引用次数: 0

Game Based Task Offloading in Cyber-Physical Systems for High-Speed Railway With Dynamic Latency and Energy Cost 高速铁路网络物理系统中基于游戏的任务卸载与动态延迟和能源成本

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-22 DOI: 10.1109/TICPS.2024.3424425

Wei Wu;Haifeng Song;Min Zhou;Xiying Song;Hairong Dong

{"title":"Game Based Task Offloading in Cyber-Physical Systems for High-Speed Railway With Dynamic Latency and Energy Cost","authors":"Wei Wu;Haifeng Song;Min Zhou;Xiying Song;Hairong Dong","doi":"10.1109/TICPS.2024.3424425","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3424425","url":null,"abstract":"The intelligentization of cyber-physical systems has led to an influx of computational tasks, placing substantial strain on the systems' computational and storage resources. High-speed railway (HSR), as a representative cyber-physical system, requires increased task completion rates and reduced energy consumption within defined timeframes. Effectively managing the surge in computational tasks within cyber-physical systems has become an urgent issue requiring resolution. This paper introduces a game-based task offloading strategy that addresses dynamic latency and energy consumption. Specifically, the study employs Stochastic Network Calculus (SNC) to capture the impact of transmission latency on system performance and obtain the bounds and probability distribution of transmission latency. Subsequently, the task offloading problem is formulated as a potential game, with each task acting as a player optimizing its objective, which includes the task completion rate and energy consumption. The Nash Equilibrium state is derived to ensure the existence of a task offloading strategy. Additionally, a reinforcement learning algorithm, Multi-Agent Deep Deterministic Policy Gradient (MADDPG), is proposed to achieve the Nash Equilibrium and optimize the task offloading strategy. Finally, extensive simulations demonstrate that the MADDPG algorithm outperforms other algorithms and exhibits fast convergence. Moreover, an appropriate violation probability derived from SNC can reduce system costs.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"292-302"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141966123","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

Toward Effective 3D Object Detection via Multimodal Fusion to Automatic Driving for Industrial Cyber-Physical Systems 通过多模态融合实现有效的 3D 物体检测，为工业网络物理系统提供自动驾驶功能

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-12 DOI: 10.1109/TICPS.2024.3427060

Honghao Gao;Yan Sun;Junsheng Xiao;Danqing Fang;Yueshen Xu;Wei Wei

{"title":"Toward Effective 3D Object Detection via Multimodal Fusion to Automatic Driving for Industrial Cyber-Physical Systems","authors":"Honghao Gao;Yan Sun;Junsheng Xiao;Danqing Fang;Yueshen Xu;Wei Wei","doi":"10.1109/TICPS.2024.3427060","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3427060","url":null,"abstract":"AI-empowered automatic driving has experienced rapid development in industrial cyber-physical systems (CPSs), especially in safety vehicles and driverless technologies. 3D object detection is an important task for perceiving the surrounding environment and supporting decision-making when vehicles are on the road, and is also a focus in CPSs. Light detection and ranging (LiDAR)-based detection methods usually lack semantic information, resulting in high uncertainty with incorrect outputs. Thus, handling complex road scenes is difficult. Some data fusion-based methods have been developed to solve these issues. However, the spatiotemporal data misalignment between different sensors is prone to losing information during data fusion. This paper proposes exploiting multimodal information to learn more high-level features to address these issues thus reducing the uncertainty of 3D object detection. First, the VxMLA (voxel and multilevel attention) framework is employed to improve point cloud identification and modeling during 3D object detection. Second, the MF-CAMRL (modal fusion-based channel attention and multidimensional regression loss) model is proposed with two subnetworks. Our model encompasses two strategies, i.e., a multimodal fusion and a deep learning model based on CAMRL. One focuses on the semantic complementarity and geometric proximity for decisionlevel fusion. The other focuses on weighted ensemble bounding boxes to fully utilize the highlevel decision information derived from both modalities and reduce the information loss incurred during modal fusion. Finally, sufficient experiments are performed on the KITTI dataset and presented. The results show that our method is superior to baseline methods.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"281-291"},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964882","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

Attack-Detection-Based Event-Triggered Transmission Scheme for Stabilizing Cyber-Physical Systems Under Denial of Service Attacks 基于攻击检测的事件触发传输方案，用于在拒绝服务攻击下稳定网络物理系统

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-11 DOI: 10.1109/TICPS.2024.3419057

Hong-Tao Sun;Chen Peng;Yitao Shen

{"title":"Attack-Detection-Based Event-Triggered Transmission Scheme for Stabilizing Cyber-Physical Systems Under Denial of Service Attacks","authors":"Hong-Tao Sun;Chen Peng;Yitao Shen","doi":"10.1109/TICPS.2024.3419057","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3419057","url":null,"abstract":"This article investigates an attack-detection-based event-triggered transmission scheme (AD-ETS) for cyber-physical systems (CPSs) subject to malicious denial of service (DoS) attacks. Due to the fact that DoS attacks can prevent transmissions without any specific rules, we characterize the effects caused by such attacks as arbitrarily bounded successive triggered-packet dropouts. On the one hand, a novel DoS attack detection strategy based on a transmission acknowledgement (ACK) scheme is developed to govern the switching of the event-triggered thresholds in real time. Compared with the existing resilient event-triggered transmission and switching-like event-triggered schemes, the proposed AD-ETS will make full use of historical ACK signals to distinguish DoS attacks from probabilistic packet dropouts and make a decision on switching event-triggered transmission strategy. Thus, both timely transmission and communication efficiency can be accomplished by using AD-ETS. On the other hand, the maximum allowable event-triggered parameter, which can guarantee the stability of the nonlinear CPSs, is derived by exploiting the input delay approach. Based on the derived maximum allowable event-triggered parameter, the relation between the secure event-triggered parameter and the number of successive DoS-incurred packet dropouts is pursued which supplies the basis of secure event design for event-triggered transmission under DoS attacks. Compared with the existing works, the secure event-triggered transmission strategy by considering successive DoS-incurred packet dropouts just depends on the event-triggered-threshold rather than nonlinear dynamics. At last, simulations on stabilization of vehicle longitudinal dynamics are conducted to verify the effectiveness of the proposed AD-ETS.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"176-184"},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602418","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

Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems 工业网络物理系统诊断和健康管理基础模型概览

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-10 DOI: 10.1109/TICPS.2024.3425326

Ruonan Liu;Quanhu Zhang;Te Han;Boyuan Yang;Weidong Zhang;Shen Yin;Donghua Zhou

{"title":"Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems","authors":"Ruonan Liu;Quanhu Zhang;Te Han;Boyuan Yang;Weidong Zhang;Shen Yin;Donghua Zhou","doi":"10.1109/TICPS.2024.3425326","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3425326","url":null,"abstract":"Industrial Cyber-Physical Systems (ICPS) integrating disciplines such as computer science, communication technology, and engineering, have become a crucial component of modern manufacturing and industry. However, ICPS faces numerous challenges during long-term operation, including equipment faults, performance degradation, and security threats, etc. To achieve efficient maintenance and management, prognostics and health management (PHM) has been widely applied in the critical tasks of ICPS such as fault prediction, health monitoring, and maintenance decision-making. The emergence of large-scale foundation models (LFMs) like BERT and GPT marks a significant advancement in artificial intelligence (AI) technology, demonstrating substantial application potential in multiple fields. The accumulation of AI technology, rapid development of LFMs, and the abundance of industrial data and industrial process knowledge provide the foundational conditions for the construction and advancement of industrial LFMs. However, there is currently a lack of consensus on applying LFMs of PHM in ICPS, necessitating a systematic review and roadmap to clarify future development directions. To bridge this gap, this survey provides a comprehensive survey and understanding of the recent advances in LFMs of PHM in ICPS. It provides valuable references for decision makers and researchers in the industry, and helps to further improve the reliability, availability and safety of ICPS.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"264-280"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965675","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

Securing Cyber-Physical Systems: A Decentralized Framework for Collaborative Intrusion Detection With Privacy Preservation 确保网络物理系统安全：保护隐私的协作式入侵检测分散框架

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-10 DOI: 10.1109/TICPS.2024.3425794

Zia Ul Islam Nasir;Adnan Iqbal;Hassaan Khaliq Qureshi

{"title":"Securing Cyber-Physical Systems: A Decentralized Framework for Collaborative Intrusion Detection With Privacy Preservation","authors":"Zia Ul Islam Nasir;Adnan Iqbal;Hassaan Khaliq Qureshi","doi":"10.1109/TICPS.2024.3425794","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3425794","url":null,"abstract":"The widespread adoption of networked technology has led to a digital revolution in interconnected systems, resulting in a significant increase in the attack surface and a corresponding rise in the number and sophistication of cyber-attacks. The integration of cyber-physical systems (CPS) into critical infrastructure has made their security against intrusions of paramount importance. To address this issue, the analysis of network traffic through Intrusion Detection Systems (IDS) has emerged as a critical element in the arsenal of network security tools. In response to the growing rate and complexity of cyber-attacks, researchers have turned to Machine Learning (ML) and Deep Learning (DL) methods to develop IDS capable of addressing network attacks. However, the effectiveness of these models is reliant on the availability of data. This study emphasizes an empirical analysis of a decentralized learning framework for detecting intrusions in CPS. The proposed approach adopts a comprehensive framework that utilizes federated learning to overcome the limitations imposed by centralized data. The study also incorporates privacy mechanisms, such as differential privacy, to strengthen intrusion detection systems. The analysis of centralized and decentralized learning scenarios reveals nuanced insights into detection performance, offering a novel perspective on securing CPS network environments. While the centralized approach demonstrates slightly better detection performance, its impact on data privacy jeopardizes its suitability for real-world implementation. The outcomes highlight the efficiency and efficacy of the devised framework, establishing a model capable of effectively classifying distinct benign and intrusive traffic patterns without inter-organizational exchange of data.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"303-311"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965063","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

Deep Learning Detection and Robust MPC Mitigation for EV-Based Load-Altering Attacks on Wind-Integrated Power Grids 深度学习检测和鲁棒 MPC 缓解基于电动汽车的风网负载调整攻击

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-09 DOI: 10.1109/TICPS.2024.3424769

Ahmadreza Abazari;Mohammad Mahdi Soleymani;Mohsen Ghafouri;Danial Jafarigiv;Ribal Atallah;Chadi Assi

{"title":"Deep Learning Detection and Robust MPC Mitigation for EV-Based Load-Altering Attacks on Wind-Integrated Power Grids","authors":"Ahmadreza Abazari;Mohammad Mahdi Soleymani;Mohsen Ghafouri;Danial Jafarigiv;Ribal Atallah;Chadi Assi","doi":"10.1109/TICPS.2024.3424769","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3424769","url":null,"abstract":"Large-scale deployment of electric vehicles (EVs) provides power grid operators with several opportunities, such as bidirectional energy transfers and frequency and voltage ancillary services. To fully realize these advantages, information and communication technologies (ICTs) between EV ecosystems and smart power grids have been developed, making power grids an appealing target for cyber attacks. On this basis, this paper studies the impact of a new family of EV-based load-altering attacks (EV-LAAs) against the subsynchronous control interaction (SSCI) of the wind-integrated power grid. First, the cyber-physical connections between the EV ecosystem and the power grid are discussed in detail to represent a threat model for coordinated EV-LAAs that can excite the SSCI modes of the system. Then, a convolutional neural network (CNN) is trained based on data from phasor measurement units (PMUs) at wind farm substations for detecting this attack, separating it from benign events, e.g., fault or line disconnection, and estimating attack vectors. The developed CNN detection model may neglect a few EV-LAAs due to the huge number of attack vectors with different combinations of amplitudes and frequencies during uncertainties in wind speeds and the number of WTG outages, leading to generating false negatives. As such, a robust model predictive controller (RMPC) is developed as a supplementary solution for mitigation purposes based on linear-matrix inequalities (LMIs). Possible uncertainties in wind speed and wind turbine generator (WTG) outages during different amplitudes of EV-LAAs are investigated when defining these LMIs. The performance of mitigation schemes is evaluated and compared with recent wide-area damping controllers, e.g., the two-degree freedom (2DOF), linear quadratic regulator (LQR), and \u0000<inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula>\u0000 under the co-simulation of EMTP-RV and MATLAB/Simulink.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"244-263"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965674","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

Finite-Time Switching Resilient Control for Networked Teleoperation System With Time-Varying Delays and Random DoS Attacks 具有时变延迟和随机 DoS 攻击的网络远程操作系统的有限时间切换弹性控制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-04 DOI: 10.1109/TICPS.2024.3422928

Lingyan Hu;Jiarun Huang;Shuang Hao;Shichao Liu;Jiecheng Lu;Bingyang Chen

{"title":"Finite-Time Switching Resilient Control for Networked Teleoperation System With Time-Varying Delays and Random DoS Attacks","authors":"Lingyan Hu;Jiarun Huang;Shuang Hao;Shichao Liu;Jiecheng Lu;Bingyang Chen","doi":"10.1109/TICPS.2024.3422928","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3422928","url":null,"abstract":"This paper presents a finite-time switching resilient controller for the networked teleoperation system control under time-varying delays and denial-of-service (DoS) attacks. The proposed controller comprises a proportional-differential plus damping (PD+d-like) controller and a switching resilient compensator. The first component, a PD+d-like controller, uses a continuous non-smooth function on the state errors and velocity signals to guarantee the global finite-time convergence. The latter part of the proposed controller, a switching resilient compensator, combines the zero-order holder (ZOH) with the continuous-time proportional-derivative (PD) regulator. This proposed controller could maintain global finite-time stability (GFTS) when time-varying delays and random DoS attacks simultaneously occur. Furthermore, we obtain the system stability criterion and establish relationships between controller parameters and maximum stability delay using Linear Matrix Inequality (LMI) technology for parameter tuning guidance. Both simulation and experimental results validate the resiliency of the proposed controller to time-varying delays and random DoS attacks.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"232-243"},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729843","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

Deep Learning Aided Minimum Mean Square Error Estimation of Gaussian Source in Industrial Internet-of-Things Networks 工业物联网网络中高斯源的深度学习辅助最小均方误差估计

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-07-01 DOI: 10.1109/TICPS.2024.3420823

Majumder Haider;Md. Zoheb Hassan;Imtiaz Ahmed;Jeffrey H. Reed;Ahmed Rubaai;Danda B. Rawat

{"title":"Deep Learning Aided Minimum Mean Square Error Estimation of Gaussian Source in Industrial Internet-of-Things Networks","authors":"Majumder Haider;Md. Zoheb Hassan;Imtiaz Ahmed;Jeffrey H. Reed;Ahmed Rubaai;Danda B. Rawat","doi":"10.1109/TICPS.2024.3420823","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3420823","url":null,"abstract":"This article investigates the problem of estimating complex-valued Gaussian signals in an industrial Internet of Things (IIoT) environment, where the channel fading is temporally correlated and modeled by a finite state Markov process. To address the non-trivial problem of estimating channel fading states and signals simultaneously, we propose two deep learning (DL)-aided minimum mean square error (MMSE) estimation schemes. More specifically, our proposed framework consists of two steps, (i) a DL-aided channel fading state estimation and prediction step, followed by (ii) a linear MMSE estimation step to estimate the source signals for the learned channel fading states. Our proposed framework employs three DL models, namely the fully connected deep neural network (DNN), long short-term memory (LSTM) integrated DNN, and temporal convolution network (TCN). Extensive simulations show that these three DL models achieve similar accuracy in predicting the states of wireless fading channels. Our proposed data-driven approaches exhibit a reasonable performance gap in normalized mean square error (NMSE) compared to the genie-aided scheme, which considers perfect knowledge of instantaneous channel fading states.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"185-195"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602417","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