Pub Date : 2026-01-20DOI: 10.1109/TGCN.2026.3656314
Haoyue Yang;Junfeng Zhang;Chongxiang Yu;Wei Xing
This paper addresses the leader-following consensus problem for T-S fuzzy positive multi-agent systems using a model prediction control (MPC) approach. First, a positive multi-agent system model with a leader-follower structure is formulated via the T-S fuzzy modeling approach. Considering that the states of agents are not fully measurable, positive observers and distributed T-S fuzzy controllers are designed to achieve leader-following consensus. The main contributions of this work lie in the development of a novel MPC framework, which integrates a linear performance index, a co-positive Lyapunov function, and a linear programming solution technique. It has a simpler form and lower computational burden than existing methods based on quadratic indices and functions. Within this framework, an MPC algorithm is proposed to solve the min-max optimization problem associated with the performance index. Furthermore, the gain matrices for both the observer and the controller are systematically derived using a matrix decomposition technique. In view of a co-positive Lyapunov function, the positivity and consensus of the T-S fuzzy positive multi-agent system are rigorously proved under the proposed MPC strategy. Finally, an example is given to verify the effectiveness of the MPC design.
{"title":"Leader-Following Consensus for T-S Fuzzy Positive Multi-Agent Systems: A Model Prediction Control Approach","authors":"Haoyue Yang;Junfeng Zhang;Chongxiang Yu;Wei Xing","doi":"10.1109/TGCN.2026.3656314","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3656314","url":null,"abstract":"This paper addresses the leader-following consensus problem for T-S fuzzy positive multi-agent systems using a model prediction control (MPC) approach. First, a positive multi-agent system model with a leader-follower structure is formulated via the T-S fuzzy modeling approach. Considering that the states of agents are not fully measurable, positive observers and distributed T-S fuzzy controllers are designed to achieve leader-following consensus. The main contributions of this work lie in the development of a novel MPC framework, which integrates a linear performance index, a co-positive Lyapunov function, and a linear programming solution technique. It has a simpler form and lower computational burden than existing methods based on quadratic indices and functions. Within this framework, an MPC algorithm is proposed to solve the min-max optimization problem associated with the performance index. Furthermore, the gain matrices for both the observer and the controller are systematically derived using a matrix decomposition technique. In view of a co-positive Lyapunov function, the positivity and consensus of the T-S fuzzy positive multi-agent system are rigorously proved under the proposed MPC strategy. Finally, an example is given to verify the effectiveness of the MPC design.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1909-1921"},"PeriodicalIF":6.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082232","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}
Pub Date : 2026-01-19DOI: 10.1109/TGCN.2026.3655393
Meihui Li;Meng Li;Qi Li;Pengbo Si;Haijun Zhang;F. Richard Yu
The emergence of Sixth Generation (6G) mobile communication technologies, along with the burgeoning proliferation of Internet of Things (IoT) devices, has significantly increased the requirements for computing resources. This growing demand places considerable pressure on the computing capacity. To address diverse and intensive application requirements, the integration of satellite and terrestrial networks is a key focus in the 6G vision. Within this context, the combination of a geostationary orbit (GEO) satellite and a low Earth orbit (LEO) satellite constellation is a crucial facilitator of IoT applications, addresses their computational demands while offering global coverage, low latency, seamless scalability, and strong reliability. However, several challenges arise in such networks: 1) LEO satellites are battery-powered and resource-constrained; 2) the number of LEO computing nodes is large and distributed; and 3) the network environment is highly dynamic and complex. To address these issues, a collaborative architecture for an LEO satellite constellation and a GEO satellite (CALG) is proposed. Based on the CALG, an energy-balancing strategy is designed to identify the optimal orbital plane for task offloading as well as an efficient routing path among satellites. The task offloading and routing selection are modeled by a Markov Decision Process, with a Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning is applied to optimize decision-making in a dynamic environment. Experimental results demonstrate that the energy-balancing strategy based on PPO meets system performance requirements and outperforms baseline strategies.
{"title":"Intelligent Energy-Balancing Offloading and Routing for IoT in Collaborative GEO-LEO Satellite Networks","authors":"Meihui Li;Meng Li;Qi Li;Pengbo Si;Haijun Zhang;F. Richard Yu","doi":"10.1109/TGCN.2026.3655393","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3655393","url":null,"abstract":"The emergence of Sixth Generation (6G) mobile communication technologies, along with the burgeoning proliferation of Internet of Things (IoT) devices, has significantly increased the requirements for computing resources. This growing demand places considerable pressure on the computing capacity. To address diverse and intensive application requirements, the integration of satellite and terrestrial networks is a key focus in the 6G vision. Within this context, the combination of a geostationary orbit (GEO) satellite and a low Earth orbit (LEO) satellite constellation is a crucial facilitator of IoT applications, addresses their computational demands while offering global coverage, low latency, seamless scalability, and strong reliability. However, several challenges arise in such networks: 1) LEO satellites are battery-powered and resource-constrained; 2) the number of LEO computing nodes is large and distributed; and 3) the network environment is highly dynamic and complex. To address these issues, a collaborative architecture for an LEO satellite constellation and a GEO satellite (CALG) is proposed. Based on the CALG, an energy-balancing strategy is designed to identify the optimal orbital plane for task offloading as well as an efficient routing path among satellites. The task offloading and routing selection are modeled by a Markov Decision Process, with a Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning is applied to optimize decision-making in a dynamic environment. Experimental results demonstrate that the energy-balancing strategy based on PPO meets system performance requirements and outperforms baseline strategies.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1841-1853"},"PeriodicalIF":6.7,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026525","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}
Pub Date : 2026-01-19DOI: 10.1109/TGCN.2026.3655209
Si-Nian Jin;Yiling Chen;Ziyi Tian;Jitong Ma;Dian-Wu Yue;Moran Ju
Reconfigurable intelligent surface (RIS) have emerged as a transformative technology to enhance the performance of Internet-of-Things devices in service dead zones within cell-free massive multiple-input multiple-output (CFmMIMO) network, particularly in ultra-reliable and low-latency communication (URLLC) scenarios. Consequently, this paper investigates the rate performance of a RIS-aided CFmMIMO-URLLC network over Rician fading channel. First, we propose a low-overhead channel estimator and subsequently derive closed-form expressions for the downlink achievable rate under maximum ratio transmission (MRT) and zero-forcing (ZF) precoding schemes. According to these analytical expressions, we formulate a resource allocation problem aimed at maximizing the sum achievable rate by jointly optimizing the power control coefficient of base station and the phase shift of RIS. To tackle this non-convex problem, we propose an alternating optimization (AO) algorithm based on path-following method to approximate the complicated objective function as a logarithmic function. The resulting problem can be further decomposed into two subproblems, which are solved using an iterative optimization framework incorporating successive convex approximation and semidefinite relaxation techniques. Extensive numerical simulations validate the accuracy of the derived closed-form expressions and highlight the superior performance of ZF precoding in the short-packet regime. Finally, the proposed AO algorithm demonstrates significant improvements in rate performance for both MRT and ZF precoding schemes compared to several benchmark algorithms.
{"title":"Resource Allocation for RIS-Aided Cell-Free Massive MIMO-URLLC Network","authors":"Si-Nian Jin;Yiling Chen;Ziyi Tian;Jitong Ma;Dian-Wu Yue;Moran Ju","doi":"10.1109/TGCN.2026.3655209","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3655209","url":null,"abstract":"Reconfigurable intelligent surface (RIS) have emerged as a transformative technology to enhance the performance of Internet-of-Things devices in service dead zones within cell-free massive multiple-input multiple-output (CFmMIMO) network, particularly in ultra-reliable and low-latency communication (URLLC) scenarios. Consequently, this paper investigates the rate performance of a RIS-aided CFmMIMO-URLLC network over Rician fading channel. First, we propose a low-overhead channel estimator and subsequently derive closed-form expressions for the downlink achievable rate under maximum ratio transmission (MRT) and zero-forcing (ZF) precoding schemes. According to these analytical expressions, we formulate a resource allocation problem aimed at maximizing the sum achievable rate by jointly optimizing the power control coefficient of base station and the phase shift of RIS. To tackle this non-convex problem, we propose an alternating optimization (AO) algorithm based on path-following method to approximate the complicated objective function as a logarithmic function. The resulting problem can be further decomposed into two subproblems, which are solved using an iterative optimization framework incorporating successive convex approximation and semidefinite relaxation techniques. Extensive numerical simulations validate the accuracy of the derived closed-form expressions and highlight the superior performance of ZF precoding in the short-packet regime. Finally, the proposed AO algorithm demonstrates significant improvements in rate performance for both MRT and ZF precoding schemes compared to several benchmark algorithms.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1795-1810"},"PeriodicalIF":6.7,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026517","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}
The evolution of 6G networks is a pivotal time to align technological advances with global sustainability objectives, specifically the United Nations’ Sustainable Development Goals (SDGs). This Systematic Literature Review (SLR) examines how 6G technologies approach sustainability through energy efficiency, reducing e-waste, reducing emissions, smart cities, and governance frameworks. We identify that SDG 9 (Industry, Innovation, and Infrastructure), SDG 7 (Affordable and Clean Energy), and SDG 13 (Climate Action) are the most frequently targeted SDGs in 6G and its development. In addition to the latest key innovations in 6G, our findings reveal research gaps and directions forward. The “drips on the heatplate” analogy represents how individually small but well-timed sustainability efforts can accumulate to reduce future communication networks’ environmental strain significantly. This review highlights the existing trends of SDG integration in 6G and emphasizes the need for sustainability to be a core part of 6G design in the future.
{"title":"6G for Sustainability: A Drip on the Heatplate, Small Steps With a Big Impact","authors":"Ihsane Gryech;Abdul Saboor;Alexander Marinšek;Franco Minucci;François Rottenberg;Véronique Moeyaert;Bruno Quoitin;Véronique Georlette;Sofie Pollin","doi":"10.1109/TGCN.2026.3654530","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3654530","url":null,"abstract":"The evolution of 6G networks is a pivotal time to align technological advances with global sustainability objectives, specifically the United Nations’ Sustainable Development Goals (SDGs). This Systematic Literature Review (SLR) examines how 6G technologies approach sustainability through energy efficiency, reducing e-waste, reducing emissions, smart cities, and governance frameworks. We identify that SDG 9 (Industry, Innovation, and Infrastructure), SDG 7 (Affordable and Clean Energy), and SDG 13 (Climate Action) are the most frequently targeted SDGs in 6G and its development. In addition to the latest key innovations in 6G, our findings reveal research gaps and directions forward. The “drips on the heatplate” analogy represents how individually small but well-timed sustainability efforts can accumulate to reduce future communication networks’ environmental strain significantly. This review highlights the existing trends of SDG integration in 6G and emphasizes the need for sustainability to be a core part of 6G design in the future.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1811-1828"},"PeriodicalIF":6.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026516","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}
Pub Date : 2026-01-16DOI: 10.1109/TGCN.2026.3654945
Hong Zhao;Hongbin Chen;Shichao Li;Ling Zhan
To address the non-line-of-sight (NLOS) transmission challenge between wireless sensor nodes (SNs) and a fusion center in smart agriculture, the uncrewed aerial vehicle (UAV)-mounted intelligent reflecting surfaces (IRS) is applied to assist data collection in wireless sensor networks in this paper. The fly-hover-communicate protocol (FHCP) is considered, where the UAV visits a set of hovering positions and communicates with one corresponding SN while hovering at each position. The optimization problem of making a trade-off between spectrum and energy efficiency (EE) under the transmission prioritized scheme (TPS) in FHCP is analyzed. For the single-SN case, the problem is decomposed into two sub-problems: UAV trajectory and number of reflecting elements (NoRE) optimization. For the multi-SN case, it is decomposed into three sub-optimization problems: user association, NoRE, and UAV trajectory planning. An efficient alternating optimization algorithm incorporating the genetic algorithm, conditional judgment-binary search algorithm, and successive convex approximation algorithm are applied to tackle this problem. Simulation results indicate that the TPS effectively increases EE compared with that when the UAV hovers directly above each SN.
{"title":"Joint Optimization of UAV Trajectory and Number of Reflecting Elements for UAV-Mounted Intelligent Reflecting Surface-Assisted Data Collection in Wireless Sensor Networks Under Transmission Prioritized Scheme","authors":"Hong Zhao;Hongbin Chen;Shichao Li;Ling Zhan","doi":"10.1109/TGCN.2026.3654945","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3654945","url":null,"abstract":"To address the non-line-of-sight (NLOS) transmission challenge between wireless sensor nodes (SNs) and a fusion center in smart agriculture, the uncrewed aerial vehicle (UAV)-mounted intelligent reflecting surfaces (IRS) is applied to assist data collection in wireless sensor networks in this paper. The fly-hover-communicate protocol (FHCP) is considered, where the UAV visits a set of hovering positions and communicates with one corresponding SN while hovering at each position. The optimization problem of making a trade-off between spectrum and energy efficiency (EE) under the transmission prioritized scheme (TPS) in FHCP is analyzed. For the single-SN case, the problem is decomposed into two sub-problems: UAV trajectory and number of reflecting elements (NoRE) optimization. For the multi-SN case, it is decomposed into three sub-optimization problems: user association, NoRE, and UAV trajectory planning. An efficient alternating optimization algorithm incorporating the genetic algorithm, conditional judgment-binary search algorithm, and successive convex approximation algorithm are applied to tackle this problem. Simulation results indicate that the TPS effectively increases EE compared with that when the UAV hovers directly above each SN.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1854-1866"},"PeriodicalIF":6.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081965","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}
Pub Date : 2026-01-15DOI: 10.1109/TGCN.2026.3654603
Jing Guo;Feihang Qiu;Lei Lei;Xu Zhang
Uncrewed aerial vehicle (UAV) has been regarded as an efficient approach for enabling battery-less wireless sensor network (WSN). In this article, an energy-limited UAV is utilized to complete the information collection task of a group of battery-less sensor nodes (SNs) in a fly-while-communication scheme. A joint trajectory and velocity optimization framework decomposes the completion time minimization problem into three subproblems: cluster head selection and sorting (P1), smooth trajectory planning (P2), and velocity optimization (P3). With the cluster heads selected by an energy-based clustering algorithm, a B-spline-based trajectory of UAV is designed and optimized. Then, the velocity optimization is implemented to fulfill the communication demand of SNs and the energy consumption constraint of the UAV. Numerical results reveal that the proposed algorithm adjusts the velocity during communication and allocates more fly-while-communication time to meet different communication demands. The task completion time of the proposed method is 43% shorter than that of the fly-hover-communication-based method and is 15% shorter than that of the Bézier curve-based method.
{"title":"Joint Optimization on Trajectory and Velocity for Minimum Completion Time in UAV-Enabled Wireless-Powered WSN","authors":"Jing Guo;Feihang Qiu;Lei Lei;Xu Zhang","doi":"10.1109/TGCN.2026.3654603","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3654603","url":null,"abstract":"Uncrewed aerial vehicle (UAV) has been regarded as an efficient approach for enabling battery-less wireless sensor network (WSN). In this article, an energy-limited UAV is utilized to complete the information collection task of a group of battery-less sensor nodes (SNs) in a fly-while-communication scheme. A joint trajectory and velocity optimization framework decomposes the completion time minimization problem into three subproblems: cluster head selection and sorting (P1), smooth trajectory planning (P2), and velocity optimization (P3). With the cluster heads selected by an energy-based clustering algorithm, a B-spline-based trajectory of UAV is designed and optimized. Then, the velocity optimization is implemented to fulfill the communication demand of SNs and the energy consumption constraint of the UAV. Numerical results reveal that the proposed algorithm adjusts the velocity during communication and allocates more fly-while-communication time to meet different communication demands. The task completion time of the proposed method is 43% shorter than that of the fly-hover-communication-based method and is 15% shorter than that of the Bézier curve-based method.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1829-1840"},"PeriodicalIF":6.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026528","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}
Recently, Long-Range (LoRa)-based direct-to-satellite Internet-of-Things (DtS-IoT) has garnered widespread attention from both academia and industry due to its capability to provide pervasive connectivity in an energy-efficient and cost-effective manner. A rigorous error performance analysis of such a new paradigm is quite essential for future green IoT communications. In this paper, we provide a novel analytical framework to characterize the error performance of LoRa-based DtS-IoT systems by leveraging an empirically-verified satellite-to-ground channel model. To enable a practical performance analysis, non-coherent detection is considered in the presence of interference along with the relative time and frequency offsets, where the corresponding decision metrics are theoretically derived. Based on this, closed-form symbol and bit error rate expressions are obtained by approximating the impact of the overall interference distributed within the decision metrics by that of the peak interference. Moreover, the impact of some key system parameters, such as the spreading factor (SF), bandwidth, and the end-device’s (ED’s) location, on the error performance is thoroughly investigated. The validity of our theoretical analysis is substantiated by extensive numerical simulations, where further insights are obtained into the error performance improvements of LoRa-based DtS-IoT systems.
{"title":"Error Performance Characterization of LoRa-Based Direct-to-Satellite IoT","authors":"Quantao Yu;Deepak Mishra;Hua Wang;Dongxuan He;Jinhong Yuan;Michail Matthaiou","doi":"10.1109/TGCN.2026.3653969","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3653969","url":null,"abstract":"Recently, Long-Range (LoRa)-based direct-to-satellite Internet-of-Things (DtS-IoT) has garnered widespread attention from both academia and industry due to its capability to provide pervasive connectivity in an energy-efficient and cost-effective manner. A rigorous error performance analysis of such a new paradigm is quite essential for future green IoT communications. In this paper, we provide a novel analytical framework to characterize the error performance of LoRa-based DtS-IoT systems by leveraging an empirically-verified satellite-to-ground channel model. To enable a practical performance analysis, non-coherent detection is considered in the presence of interference along with the relative time and frequency offsets, where the corresponding decision metrics are theoretically derived. Based on this, closed-form symbol and bit error rate expressions are obtained by approximating the impact of the overall interference distributed within the decision metrics by that of the peak interference. Moreover, the impact of some key system parameters, such as the spreading factor (SF), bandwidth, and the end-device’s (ED’s) location, on the error performance is thoroughly investigated. The validity of our theoretical analysis is substantiated by extensive numerical simulations, where further insights are obtained into the error performance improvements of LoRa-based DtS-IoT systems.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1879-1893"},"PeriodicalIF":6.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081968","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}
Pub Date : 2026-01-12DOI: 10.1109/TGCN.2026.3653056
Xiaoxuan Hu;Liang Shan;Jialin Hua;Jin Qi;Zhenjiang Dong;Bin Xu;Yanfei Sun
To address the challenges of computational task offloading for resource-constrained, heterogeneous terminals in the 6G Industrial Internet of Things (IIoT), which generate computation-intensive and resource-efficient tasks in real time, we propose a Digital Twin(DT)-driven cloud-edge-end collaborative resource allocation and task offloading (RATO) model that accounts for both latency and energy consumption. First, we establish a cloud-edge-end collaborative communication and computation framework by integrating cloud computing with edge computing, accommodating terminal and edge server heterogeneity, and employing Non-Orthogonal Multiple Access (NOMA) communication along with key authentication mechanisms to ensure secure communications. Next, Digital Twin technology is utilized for real-time monitoring of the physical environment, considering simulation bias to construct accurate DT entities. Finally, we employ a DT-driven multi-agent deep deterministic policy gradient (DT-MADDPG) algorithm to derive the optimal task scheduling strategy. Simulation results demonstrate that the proposed model significantly outperforms existing schemes in terms of delay, energy cost, load balancing of edge servers, and Quality of Service (QoS) for terminals.
{"title":"Joint Resource Allocation and Task Offloading for Heterogeneous Cloud-Edge-End Networks Assisted by NOMA","authors":"Xiaoxuan Hu;Liang Shan;Jialin Hua;Jin Qi;Zhenjiang Dong;Bin Xu;Yanfei Sun","doi":"10.1109/TGCN.2026.3653056","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3653056","url":null,"abstract":"To address the challenges of computational task offloading for resource-constrained, heterogeneous terminals in the 6G Industrial Internet of Things (IIoT), which generate computation-intensive and resource-efficient tasks in real time, we propose a Digital Twin(DT)-driven cloud-edge-end collaborative resource allocation and task offloading (RATO) model that accounts for both latency and energy consumption. First, we establish a cloud-edge-end collaborative communication and computation framework by integrating cloud computing with edge computing, accommodating terminal and edge server heterogeneity, and employing Non-Orthogonal Multiple Access (NOMA) communication along with key authentication mechanisms to ensure secure communications. Next, Digital Twin technology is utilized for real-time monitoring of the physical environment, considering simulation bias to construct accurate DT entities. Finally, we employ a DT-driven multi-agent deep deterministic policy gradient (DT-MADDPG) algorithm to derive the optimal task scheduling strategy. Simulation results demonstrate that the proposed model significantly outperforms existing schemes in terms of delay, energy cost, load balancing of edge servers, and Quality of Service (QoS) for terminals.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1765-1778"},"PeriodicalIF":6.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982211","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}
Pub Date : 2026-01-12DOI: 10.1109/TGCN.2026.3652239
Evangelos Koutsonas;Alexandros-Apostolos A. Boulogeorgos;Stylianos E. Trevlakis;George C. Alexandropoulos;Theodoros A. Tsiftsis;Rui Zhang
In this paper, a new reconfigurable intelligent surface (RIS) hardware architecture, called self-organized RIS (SORIS), is proposed. The architecture incorporates a microcontroller connected to a single-antenna receiver operating at the same frequency as the RIS unit elements, operating either in transmission or reflection mode. The transmitting RIS elements enable the low latency estimation of both the incoming and outcoming channels at the microcontroller’s side. In addition, a machine learning approach for estimating the incoming and outcoming channels involving the remaining RIS elements operating in reflection mode is devised. Specifically, by appropriately selecting a small number of elements in transmission mode, and based on the channel reciprocity principle, the respective channel coefficients are first estimated, which are then fed to a low-complexity neural network that, leveraging spatial channel correlation over RIS elements, returns predictions of the channel coefficients referring to the rest of elements. In this way, the SORIS microcontroller acquires channel state information, and accordingly reconfigures the panel’s metamaterials to assist data communication between a transmitter and a receiver, without the need for separate connections with them. Moreover, the impact of channel estimation on the proposed solution, and a detailed complexity analysis for the used model, as well as a wiring density and control signaling analysis, is performed. The feasibility and efficacy of the proposed self-organized RIS design and operation are verified by Monte Carlo simulations, providing useful guidelines on the selection of the RIS elements for operating in transmission mode for initial channel estimation.
{"title":"SORIS: A Self-Organized Reconfigurable Intelligent Surface Architecture for Wireless Communications","authors":"Evangelos Koutsonas;Alexandros-Apostolos A. Boulogeorgos;Stylianos E. Trevlakis;George C. Alexandropoulos;Theodoros A. Tsiftsis;Rui Zhang","doi":"10.1109/TGCN.2026.3652239","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3652239","url":null,"abstract":"In this paper, a new reconfigurable intelligent surface (RIS) hardware architecture, called self-organized RIS (SORIS), is proposed. The architecture incorporates a microcontroller connected to a single-antenna receiver operating at the same frequency as the RIS unit elements, operating either in transmission or reflection mode. The transmitting RIS elements enable the low latency estimation of both the incoming and outcoming channels at the microcontroller’s side. In addition, a machine learning approach for estimating the incoming and outcoming channels involving the remaining RIS elements operating in reflection mode is devised. Specifically, by appropriately selecting a small number of elements in transmission mode, and based on the channel reciprocity principle, the respective channel coefficients are first estimated, which are then fed to a low-complexity neural network that, leveraging spatial channel correlation over RIS elements, returns predictions of the channel coefficients referring to the rest of elements. In this way, the SORIS microcontroller acquires channel state information, and accordingly reconfigures the panel’s metamaterials to assist data communication between a transmitter and a receiver, without the need for separate connections with them. Moreover, the impact of channel estimation on the proposed solution, and a detailed complexity analysis for the used model, as well as a wiring density and control signaling analysis, is performed. The feasibility and efficacy of the proposed self-organized RIS design and operation are verified by Monte Carlo simulations, providing useful guidelines on the selection of the RIS elements for operating in transmission mode for initial channel estimation.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1749-1764"},"PeriodicalIF":6.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026590","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}
Pub Date : 2026-01-12DOI: 10.1109/TGCN.2026.3653184
Habtamu Demeke Mihertie;Zhengqiang Wang;Mohamed Amine Ouamri;Elhadj Moustapha Diallo;Xingwang Li
Energy efficiency (EE) is a critical requirement for next-generation wireless networks, motivating the use of rate-splitting multiple access (RSMA) and intelligent reflecting surfaces (IRSs) as low-power, interference-resilient technologies. This paper studies EE maximization in a UAV-mounted IRS-assisted multi-user MISO downlink under practical hardware impairments. A communication-centric EE metric is adopted, and the optimization of RSMA precoders, common-rate allocation, IRS phase shifts, and UAV placement is formulated as a non-convex problem. To solve it efficiently, we develop an alternating optimization framework based on successive convex approximation (SCA) and rank-one relaxation. Simulation results reveal that the proposed aerial IRS-assisted RSMA design achieves substantial EE gains over NOMA and SDMA baselines and remains robust to distortion, IRS size variations, and dynamic user conditions, highlighting its suitability for energy-constrained 6G deployments.
{"title":"Energy Efficiency Maximization for Aerial Intelligent Reflecting Surface-Assisted MISO Systems","authors":"Habtamu Demeke Mihertie;Zhengqiang Wang;Mohamed Amine Ouamri;Elhadj Moustapha Diallo;Xingwang Li","doi":"10.1109/TGCN.2026.3653184","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3653184","url":null,"abstract":"Energy efficiency (EE) is a critical requirement for next-generation wireless networks, motivating the use of rate-splitting multiple access (RSMA) and intelligent reflecting surfaces (IRSs) as low-power, interference-resilient technologies. This paper studies EE maximization in a UAV-mounted IRS-assisted multi-user MISO downlink under practical hardware impairments. A communication-centric EE metric is adopted, and the optimization of RSMA precoders, common-rate allocation, IRS phase shifts, and UAV placement is formulated as a non-convex problem. To solve it efficiently, we develop an alternating optimization framework based on successive convex approximation (SCA) and rank-one relaxation. Simulation results reveal that the proposed aerial IRS-assisted RSMA design achieves substantial EE gains over NOMA and SDMA baselines and remains robust to distortion, IRS size variations, and dynamic user conditions, highlighting its suitability for energy-constrained 6G deployments.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1894-1908"},"PeriodicalIF":6.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081970","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}
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