Pub Date : 2025-10-15DOI: 10.1109/JSYST.2025.3597254
{"title":"IEEE Systems Journal Information for Authors","authors":"","doi":"10.1109/JSYST.2025.3597254","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3597254","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 3","pages":"C4-C4"},"PeriodicalIF":4.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11204757","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-15DOI: 10.1109/JSYST.2025.3597256
{"title":"IEEE Systems Council Information","authors":"","doi":"10.1109/JSYST.2025.3597256","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3597256","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 3","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11204755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-06DOI: 10.1109/JSYST.2025.3607118
Hong Zhao;Hongbin Chen;Shichao Li;Ling Zhan
Uncrewedaerial vehicle (UAV)-carried intelligent reflecting surfaces (U-IRSs) can be utilized to assist blocked communications between sensor nodes (SNs) and the fusion center in wireless sensor networks (WSNs). This article investigates a U-IRS-assisted data collection system in WSNs that employs the hover priority scheme. Given the energy constraints of UAV, the combined energy consumption from UAV moving/hovering and IRS reflecting elements configuration circuitry poses significant challenges to improving the system’s energy efficiency (EE). To address this challenge, we formulate a multiobjective optimization problem under the constraints of UAV and SN power budgets to make a tradeoff between EE and spectral efficiency. Due to the nonconvexity of the formulated problem, we divide the main problem into three subproblems: user association, the number of reflecting elements, and UAV trajectory optimization. An alternating optimization algorithm integrating the genetic algorithm, the CJ-BS-based cyclic iteration algorithm, Dinkelbach’s algorithm, and the successive convex approximation method is proposed to solve these subproblems. Simulation results demonstrate that the proposed solution outperforms the UAV hovering directly above each SN scheme.
{"title":"Joint Optimization of UAV Trajectory and Number of Reflecting Elements for UAV-Carried IRS-Assisted Data Collection in WSNs Under Hover Priority Scheme","authors":"Hong Zhao;Hongbin Chen;Shichao Li;Ling Zhan","doi":"10.1109/JSYST.2025.3607118","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3607118","url":null,"abstract":"Uncrewedaerial vehicle (UAV)-carried intelligent reflecting surfaces (U-IRSs) can be utilized to assist blocked communications between sensor nodes (SNs) and the fusion center in wireless sensor networks (WSNs). This article investigates a U-IRS-assisted data collection system in WSNs that employs the hover priority scheme. Given the energy constraints of UAV, the combined energy consumption from UAV moving/hovering and IRS reflecting elements configuration circuitry poses significant challenges to improving the system’s energy efficiency (EE). To address this challenge, we formulate a multiobjective optimization problem under the constraints of UAV and SN power budgets to make a tradeoff between EE and spectral efficiency. Due to the nonconvexity of the formulated problem, we divide the main problem into three subproblems: user association, the number of reflecting elements, and UAV trajectory optimization. An alternating optimization algorithm integrating the genetic algorithm, the CJ-BS-based cyclic iteration algorithm, Dinkelbach’s algorithm, and the successive convex approximation method is proposed to solve these subproblems. Simulation results demonstrate that the proposed solution outperforms the UAV hovering directly above each SN scheme.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 3","pages":"963-974"},"PeriodicalIF":4.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-17DOI: 10.1109/JSYST.2025.3597583
Miaomiao Ma;Ruoxin Hao;Xiangjie Liu;Kwang Y. Lee
This article studies the event-triggered distributed model predictive control (ET-DMPC) strategy for the load frequency control (LFC) issue of multiarea interconnected power systems (IPSs) subject to bounded disturbances and load reference set-point constraints. The entire IPS comprises multiple dynamically coupled subsystems, each exchanging information with interconnected subsystems through the communication network. Local DMPC controllers are designed with robust constraints and load reference set-point constraints, where robust constraints limit the impact of tie-line power deviations and disturbances. To further alleviate the computational and communication burdens of subsystems, a distributed event-triggered mechanism is proposed, in which the threshold integrates the information of current subsystem state, disturbances, and tie-line power deviations between areas. By comparing this threshold with the deviation between the actual trajectory and the optimal prediction, the instants of optimization problem solving and information transmission are determined, effectively balancing control performance and resource utilization. Moreover, the theoretical conditions guaranteeing Zeno-free behavior, recursive feasibility, and closed-loop stability are analyzed. Finally, simulation results and analysis for a three-area IPS demonstrate that computational and communication burdens are significantly reduced while achieving a satisfactory LFC objective, which validates the effectiveness of the ET-DMPC strategy.
{"title":"Event-Triggered Distributed Model Predictive Control for LFC of Interconnected Power System","authors":"Miaomiao Ma;Ruoxin Hao;Xiangjie Liu;Kwang Y. Lee","doi":"10.1109/JSYST.2025.3597583","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3597583","url":null,"abstract":"This article studies the event-triggered distributed model predictive control (ET-DMPC) strategy for the load frequency control (LFC) issue of multiarea interconnected power systems (IPSs) subject to bounded disturbances and load reference set-point constraints. The entire IPS comprises multiple dynamically coupled subsystems, each exchanging information with interconnected subsystems through the communication network. Local DMPC controllers are designed with robust constraints and load reference set-point constraints, where robust constraints limit the impact of tie-line power deviations and disturbances. To further alleviate the computational and communication burdens of subsystems, a distributed event-triggered mechanism is proposed, in which the threshold integrates the information of current subsystem state, disturbances, and tie-line power deviations between areas. By comparing this threshold with the deviation between the actual trajectory and the optimal prediction, the instants of optimization problem solving and information transmission are determined, effectively balancing control performance and resource utilization. Moreover, the theoretical conditions guaranteeing Zeno-free behavior, recursive feasibility, and closed-loop stability are analyzed. Finally, simulation results and analysis for a three-area IPS demonstrate that computational and communication burdens are significantly reduced while achieving a satisfactory LFC objective, which validates the effectiveness of the ET-DMPC strategy.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 3","pages":"983-994"},"PeriodicalIF":4.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article investigates the dynamic event-triggered consensus of linear leader-following multiagent systems under matrix-weighted networks. In such networks, matrix weights characterize the dependencies among multidimensional states of agents. First, we propose a distributed dynamic event-triggered control protocol in which each agent employs an auxiliary system to dynamically adjust the triggering threshold. Moreover, it is ensured that the sequence of triggering times does not exhibit Zeno behavior under given conditions. Remarkably, clustering naturally occurs in matrix-weighted networks, which reflects the important role of matrix coupling in the convergence process. Furthermore, Lyapunov stability theory is applied to achieve leader-following consensus in matrix-weighted multiagent networks. Finally, a simulation is presented to verify the reliability of the obtained results.
{"title":"Dynamic Event-Triggered Consensus of Matrix-Weighted Linear Multiagent Systems","authors":"Yanting Huang;Chengjie Xu;Zi-Ang Song;Guohua Zhang","doi":"10.1109/JSYST.2025.3600449","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3600449","url":null,"abstract":"This article investigates the dynamic event-triggered consensus of linear leader-following multiagent systems under matrix-weighted networks. In such networks, matrix weights characterize the dependencies among multidimensional states of agents. First, we propose a distributed dynamic event-triggered control protocol in which each agent employs an auxiliary system to dynamically adjust the triggering threshold. Moreover, it is ensured that the sequence of triggering times does not exhibit Zeno behavior under given conditions. Remarkably, clustering naturally occurs in matrix-weighted networks, which reflects the important role of matrix coupling in the convergence process. Furthermore, Lyapunov stability theory is applied to achieve leader-following consensus in matrix-weighted multiagent networks. Finally, a simulation is presented to verify the reliability of the obtained results.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 3","pages":"975-982"},"PeriodicalIF":4.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1109/JSYST.2025.3582141
Zhiqiang Liu;Di Zhang;Jingjing Guo;Theodoros A. Tsiftsis;Yuwei Su;Battulga Davaasambuu;Sahil Garg;Takuro Sato
Massive multiple-input multiple-output (massive MIMO)-based low Earth orbit (LEO) intersatellite link communications is a promising research topic for the fifth generation (5G) and forthcoming sixth generation (6G) wireless networks. However, due to the fast relative movement between the transmitter and receiver in LEO satellites, intersatellite link communication is facing serious Doppler shifts and delays, which makes it difficult to obtain the accurate channel state information (CSI). Therefore, this article proposes an improved channel prediction method, that is, spatial-delay domain-based Prony (SDD-Prony) prediction, which not only realizes the accurate CSI acquisition for massive MIMO-based LEO intersatellite link systems but also substantially reduces the computational complexity. In particular, it is shown that the prediction error of the proposed method can converge to zero with the increase in antenna number and bandwidth. In addition, we combine the SDD-Prony prediction method with the total least squares method to reduce the influence of the noise and error components on the prediction accuracy, which further improves the prediction performance. The effectiveness of the proposed method is demonstrated by a theoretical proof and simulation results.
{"title":"Retraction Notice: A Spatial Delay Domain-Based Prony Channel Prediction Method for Massive MIMO LEO Communications","authors":"Zhiqiang Liu;Di Zhang;Jingjing Guo;Theodoros A. Tsiftsis;Yuwei Su;Battulga Davaasambuu;Sahil Garg;Takuro Sato","doi":"10.1109/JSYST.2025.3582141","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3582141","url":null,"abstract":"Massive multiple-input multiple-output (massive MIMO)-based low Earth orbit (LEO) intersatellite link communications is a promising research topic for the fifth generation (5G) and forthcoming sixth generation (6G) wireless networks. However, due to the fast relative movement between the transmitter and receiver in LEO satellites, intersatellite link communication is facing serious Doppler shifts and delays, which makes it difficult to obtain the accurate channel state information (CSI). Therefore, this article proposes an improved channel prediction method, that is, spatial-delay domain-based Prony (SDD-Prony) prediction, which not only realizes the accurate CSI acquisition for massive MIMO-based LEO intersatellite link systems but also substantially reduces the computational complexity. In particular, it is shown that the prediction error of the proposed method can converge to zero with the increase in antenna number and bandwidth. In addition, we combine the SDD-Prony prediction method with the total least squares method to reduce the influence of the noise and error components on the prediction accuracy, which further improves the prediction performance. The effectiveness of the proposed method is demonstrated by a theoretical proof and simulation results.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 3","pages":"995-995"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11061249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-17DOI: 10.1109/JSYST.2025.3564689
{"title":"IEEE Systems Council Information","authors":"","doi":"10.1109/JSYST.2025.3564689","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3564689","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"C3-C3"},"PeriodicalIF":4.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11038956","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-17DOI: 10.1109/JSYST.2025.3564685
{"title":"IEEE Systems Journal Publication Information","authors":"","doi":"10.1109/JSYST.2025.3564685","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3564685","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"C2-C2"},"PeriodicalIF":4.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11038952","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-17DOI: 10.1109/JSYST.2025.3564691
{"title":"IEEE Systems Journal Information for Authors","authors":"","doi":"10.1109/JSYST.2025.3564691","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3564691","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"C4-C4"},"PeriodicalIF":4.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-28DOI: 10.1109/JSYST.2025.3553551
Hao Yuan;Tao Chen;Bangbang Ren;Mengmeng Zhang;Xueshan Luo
The application of artificial intelligence, Big Data, and other advanced technologies has dramatically improved the intelligence level of the combat system-of-systems and accelerated the combat rhythm, which requires higher decision speed in the support of high-quality combat communication architecture. In reality, due to the poor infrastructure conditions on the battlefield, the communication services of the combat units are usually provided by the communication units with limited communication resources. Thus, figuring out an efficient method to share the scarce communication resources among massive combat units becomes crucial. However, it is challenging to efficiently construct the connection relationship and allocate communication resources to the operational units because of the differences in communication requirements and the randomness of location movement of combat units, i.e., unable to obtain battlefield environmental information in advance. In this article, we propose an online learning (OL)-based combat communication architecture construction method, which can estimate the current state of the battlefield environment by interacting with it and dynamically construct connection relationships and allocating communication resources according to the needs and locations of operational units, so as to maximize the QoE. The evaluation results demonstrate that our proposed OL-based approach is capable of constructing the combat communication architecture in a flexible and efficient manner, surpassing existing methods in terms of efficiency and fairness by significantly enhancing the total QoE up to twice as much compared to baseline methods.
{"title":"Optimize Communication Architecture in Dynamic Combat Environment via Online Learning","authors":"Hao Yuan;Tao Chen;Bangbang Ren;Mengmeng Zhang;Xueshan Luo","doi":"10.1109/JSYST.2025.3553551","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3553551","url":null,"abstract":"The application of artificial intelligence, Big Data, and other advanced technologies has dramatically improved the intelligence level of the combat system-of-systems and accelerated the combat rhythm, which requires higher decision speed in the support of high-quality combat communication architecture. In reality, due to the poor infrastructure conditions on the battlefield, the communication services of the combat units are usually provided by the communication units with limited communication resources. Thus, figuring out an efficient method to share the scarce communication resources among massive combat units becomes crucial. However, it is challenging to efficiently construct the connection relationship and allocate communication resources to the operational units because of the differences in communication requirements and the randomness of location movement of combat units, i.e., unable to obtain battlefield environmental information in advance. In this article, we propose an online learning (OL)-based combat communication architecture construction method, which can estimate the current state of the battlefield environment by interacting with it and dynamically construct connection relationships and allocating communication resources according to the needs and locations of operational units, so as to maximize the QoE. The evaluation results demonstrate that our proposed OL-based approach is capable of constructing the combat communication architecture in a flexible and efficient manner, surpassing existing methods in terms of efficiency and fairness by significantly enhancing the total QoE up to twice as much compared to baseline methods.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"435-446"},"PeriodicalIF":4.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：