IEEE Transactions on Green Communications and Networking最新文献_第5页

Data Offloading and Energy Management in MEC Networks With Distributed Power Source: A DRL Approach 分布式电源MEC网络的数据卸载与能量管理：一种DRL方法

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-10-09 DOI: 10.1109/TGCN.2025.3619838

Chuan Liu;Jing Tao;Wei Gao;Wei Peng;Shubin Zhang;Feng Shu

{"title":"Data Offloading and Energy Management in MEC Networks With Distributed Power Source: A DRL Approach","authors":"Chuan Liu;Jing Tao;Wei Gao;Wei Peng;Shubin Zhang;Feng Shu","doi":"10.1109/TGCN.2025.3619838","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3619838","url":null,"abstract":"The rapid proliferation of Internet of Things (IoT) devices has led to an exponential growth in data generation, exacerbating the inherent limitations of conventional IoT systems, particularly in computational capacity and energy sustainability. To address these challenges, this paper proposes an integrated framework combining mobile edge computing (MEC), distributed energy resources, and smart grid technology. Within this framework, distributed power sources not only supply energy to IoT devices but also engage in energy trading with the power grid. We formulate a joint optimization problem to maximize both computational efficiency and weighted energy trading benefits through optimal task offloading decisions and energy allocation strategies. The resulting mixed-integer nonlinear programming (MINLP) problem is decomposed into a top problem and a subproblem for efficient solution. A deep reinforcement learning (DRL) approach is developed to determine optimal offloading decisions, while convex optimization are employed to solve the energy allocation subproblem. Extensive experimental evaluations demonstrate that our proposed method achieves superior convergence behavior and system performance compared to existing benchmark algorithms.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1237-1249"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secrecy Rate Maximization for IRS-Enabled UAV-ISAC Systems via Phase Shifting Adjustment and Resource Allocation 基于相移调整和资源分配的irs型无人机isac系统保密率最大化

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-06-10 DOI: 10.1109/TGCN.2025.3578453

Yuxin Guo;Xiangdong Jia;Mangang Xie;Yue Li

{"title":"Secrecy Rate Maximization for IRS-Enabled UAV-ISAC Systems via Phase Shifting Adjustment and Resource Allocation","authors":"Yuxin Guo;Xiangdong Jia;Mangang Xie;Yue Li","doi":"10.1109/TGCN.2025.3578453","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3578453","url":null,"abstract":"To address the challenge of spectrum resource shortage, integrated sensing and communication (ISAC) technology significantly improves system efficiency through hardware-based spectrum sharing. However, its open-channel characteristics and functional coupling introduce security vulnerabilities and performance bottlenecks. This paper proposes an innovative architecture for an intelligent reflecting surface (IRS)-assisted autonomous aerial vehicle (UAV) cooperative dual-function base station (ISAC-BS). This architecture jointly optimizes active beamforming, IRS phase shift, and UAV trajectory to enhance both secure communication and sensing accuracy. Within this architecture, the ISAC-BS carries out secure communication and sensing tasks while using artificial noise (AN) to interfere with potential eavesdropping, further improving performance. Two optimization algorithms were designed for all link channel state information (CSI) scenarios. Under perfect CSI conditions, the alternating optimization (AO) framework provides a closed-form solution for the IRS, incorporates fractional programming-based beamforming, and utilizes successive convex approximation (SCA)-based trajectory optimization sequentially. Under imperfect CSI, the robustness of the S-process is introduced. Simulation results show that the proposed algorithm significantly improves the secrecy rate and positioning accuracy compared to the baseline scheme while ensuring convergence. This provides a new paradigm for enhancing security and enabling collaborative optimization in ISAC systems with open channels.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"289-299"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Green-Edge Computing-Based IoV Dynamic Task Collaborative Strategy 基于绿边计算的车联网动态任务协同策略

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-08-12 DOI: 10.1109/TGCN.2025.3598103

Pingzheng Ge;Haoyu Quan;Qingmiao Zhang;Junhui Zhao

{"title":"Green-Edge Computing-Based IoV Dynamic Task Collaborative Strategy","authors":"Pingzheng Ge;Haoyu Quan;Qingmiao Zhang;Junhui Zhao","doi":"10.1109/TGCN.2025.3598103","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3598103","url":null,"abstract":"With the rapid development of Internet of Vehicles (IoV), the demand for data processing and computing power is increasing exponentially, creating significant challenges to edge computing, with the most prominent being resource constraints and the urgent need for green computing. To tackle the challenges mentioned above, this article proposes a dynamic task collaborative strategy based on green edge computing for IoV. Specifically, the strategy encompasses a novel multi-dimensional dynamic offloading decision system that facilitates collaborative offloading between edge servers and vehicles on the road, thereby overcoming resource constraints. Additionally, we have integrated energy harvesting technology into edge servers and constructed a new energy harvesting model by taking into account both environmental and device factors, enabling more accurate predictions of the harvesting energy. Meanwhile, the article focuses on the dynamic offloading scenario during the movement of vehicles, applying latency and energy consumption as evaluation indicators to minimize system cost. To solve above optimization problem, an improved hippopotamus algorithm is proposed. Finally, simulation results demonstrate the effectiveness of proposed algorithm and strategy in enhancing the performance and sustainability of IoV.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"780-790"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Unsupervised Machine Learning to Optimize Hybrid Quantum Noise Clusters for Gaussian Quantum Channel 高斯量子信道混合量子噪声聚类优化的无监督机器学习

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-07-10 DOI: 10.1109/TGCN.2025.3587144

Mouli Chakraborty;Anshu Mukherjee;Ioannis Krikidis;Avishek Nag;Subhash Chandra

{"title":"An Unsupervised Machine Learning to Optimize Hybrid Quantum Noise Clusters for Gaussian Quantum Channel","authors":"Mouli Chakraborty;Anshu Mukherjee;Ioannis Krikidis;Avishek Nag;Subhash Chandra","doi":"10.1109/TGCN.2025.3587144","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3587144","url":null,"abstract":"Quantum communication systems are essential for secure information transmission, but their performance is significantly impeded by complex hybrid quantum noise (HQN) in Gaussian quantum channels. Accurately modeling this noise is crucial for optimizing channel achievable rates, a problem compounded by the complex nature of hybrid noise, which combines quantum shot noise and classical additive-white-Gaussian noise (AWGN) and is typically represented as an infinite mixture of Gaussian distributions. This work focuses on optimizing the HQN model to improve the achievable rate of Gaussian quantum channels using Machine Learning (ML) optimized clusters. The work specifically leverages Gaussian mixture model (GMM) and the Expectation-Maximization (EM) algorithm to model the complex noise characteristics of quantum channels. The study proposes modeling of hybrid noise as an infinite mixture of Gaussian distributions weighted by Poissonian parameters, with a novel cluster reduction technique that minimizes the number of Gaussian components while maintaining accuracy within acceptable error tolerances. Simulation results demonstrate that the GMM-EM enhanced clustering method significantly improves channel achievable rates in general quantum communication and satellite-based quantum communication systems. The GMM-EM method substantially outperforms conventional clustering techniques, including K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms, in terms of maximizing achievable rates and characterizing noise accuracy. This advancement provides a practical and efficient solution for real-time quantum noise modeling, representing a significant improvement over existing state-of-the-art approaches in quantum communication network optimization.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"440-456"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Maximizing Decision-Making Time Overhead-Aware Computation Rate in Wireless-Powered Mobile Edge Computing 无线移动边缘计算中决策时间开销感知计算率最大化

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-01-19 DOI: 10.1109/TGCN.2026.3654993

Haishuo Zhang;Ke Xiong;Xiang Zhang;Wei Chen;Pingyi Fan;Derrick Wing Kwan Ng;Khaled Ben Letaief

{"title":"Maximizing Decision-Making Time Overhead-Aware Computation Rate in Wireless-Powered Mobile Edge Computing","authors":"Haishuo Zhang;Ke Xiong;Xiang Zhang;Wei Chen;Pingyi Fan;Derrick Wing Kwan Ng;Khaled Ben Letaief","doi":"10.1109/TGCN.2026.3654993","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3654993","url":null,"abstract":"Wireless powered mobile edge computing (WPMEC) has emerged as a promising paradigm to mitigate the energy and computation limitations of small-scale Internet-of-Things (IoT) devices. Typical WPMEC operates in a discrete-time manner: at the beginning of each time block, a resource optimization algorithm determines system configuration parameters, and the remainder time block is exploited to perform computation and communication tasks based on optimized settings. Although various decision-making algorithms have been proposed for WPMECs, the associated computational time overhead is often overlooked in existing studies. However, the execution time of decision-making algorithms occupies a non-negligible portion of time in practice, particularly in fast fading channels. In view of this, we define a unified performance evaluation metric termed decision-making time overhead-aware computation rate (DTO-ComRate), which jointly accounts for decision accuracy and algorithmic complexity. Then, we first formulate an optimization problem to maximize the weighted sum DTO-ComRate by jointly optimizing offloading and channel resources. To efficiently address this problem, we further propose a candidate solution set (CSS)-guided deep learning (CSS-DL) algorithm, in which linear relaxation is leveraged to construct the CSS, and a deep neural network (DNN) is trained to assign channel resources in an online self-learning manner. Experiments show that CSS-DL significantly outperforms baseline algorithms and maintains robust performance as the number of IoT devices increases, demonstrating its scalability to large-scale wireless systems.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1937-1950"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transmit Power Minimization With QoS Guarantees in STAR-RIS-Aided RSMA Networks star - ris辅助RSMA网络中具有QoS保证的传输功率最小化

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-03-04 DOI: 10.1109/TGCN.2026.3670569

Donghyun Lee;Chihyun Song;The Vi Nguyen;Junsuk Oh;Yunseong Lee;Dongwook Won;Wonjong Noh;Sungrae Cho

{"title":"Transmit Power Minimization With QoS Guarantees in STAR-RIS-Aided RSMA Networks","authors":"Donghyun Lee;Chihyun Song;The Vi Nguyen;Junsuk Oh;Yunseong Lee;Dongwook Won;Wonjong Noh;Sungrae Cho","doi":"10.1109/TGCN.2026.3670569","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3670569","url":null,"abstract":"Rate-splitting multiple access (RSMA) and simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) have emerged as promising technologies for multiple access and relaying in sixth-generation (6G) networks. This paper investigates a STAR-RIS-aided RSMA system that minimizes transmit power while satisfying users’ quality of service (QoS) requirements under spatially correlated channels. The formulated non-convex optimization problem jointly optimizes the precoding vector, common message ratio, phase shift matrix, and transmission and reflection ratio. To solve this, the problem is decomposed into three subproblems: 1) precoding and common message ratio control at the base station (BS), 2) phase shift matrix design, 3) transmission/reflection ratio control at the STAR-RIS. These subproblems are transformed into convex forms via penalty functions and linear approximations. A stationary solution is obtained using interior-point initialization, successive convex approximation, and alternating optimization algorithms. Through extensive simulations under various channel environments, including spatially correlated scenarios, the proposed method achieves much lower transmit power while meeting QoS requirements, outperforming conventional (STAR-)RIS-aided RSMA, non-orthogonal multiple access, and space-division multiple access. The performance gain becomes more significant when the number of STAR-RIS elements or BS antennas is limited.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"2333-2349"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Learning Algorithms for Task Scheduling in MEC-Enabled IoT Networks 支持mec的物联网网络任务调度的鲁棒学习算法

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-07-15 DOI: 10.1109/TGCN.2025.3589467

Vikky Masih;Arghyadip Roy

{"title":"Robust Learning Algorithms for Task Scheduling in MEC-Enabled IoT Networks","authors":"Vikky Masih;Arghyadip Roy","doi":"10.1109/TGCN.2025.3589467","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3589467","url":null,"abstract":"Reinforcement Learning (RL) based task scheduling approaches can be adopted in a Multi-access Edge Computing (MEC) based Internet of Things (IoT) network to address the inherent trade-off between energy consumption and deadline violation. However, such solutions may be sensitive in terms of energy consumption in the presence of varying task arrival rates. To this end, in this paper, we provide a Robust-Return Constrained Markov Decision Process (R2CMDP) formulation to minimize the worst-case total discounted power consumption subject to a constraint on the total discounted deadline violation. Since the task arrival statistics may be unknown, we propose a novel robust RL based task allocation algorithm. Contrary to traditional RL algorithms, the proposed robust RL based task allocation algorithm provides robust performance in the face of varying task arrival rates. We prove that the proposed algorithm converges to the optimal solution of the R2CMDP formulation. However, since the algorithm becomes computationally expensive as the size of the state space grows, we propose another robust learning task scheduling heuristic with significantly lower computational complexity. Network Simulator-3 (ns-3) based simulation results are presented to demonstrate the efficacy of the proposed algorithms in comparison to the state-of-the-art algorithms under realistic network scenarios.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"497-510"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient Adaptive Secure Control for MIMO Switched CPSs Under Unknown Deception Attacks 未知欺骗攻击下MIMO交换cps的弹性自适应安全控制

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-09-29 DOI: 10.1109/TGCN.2025.3615157

Ning Xu;Yuhang Wu;Guangdeng Zong;Ben Niu;Xudong Zhao

{"title":"Resilient Adaptive Secure Control for MIMO Switched CPSs Under Unknown Deception Attacks","authors":"Ning Xu;Yuhang Wu;Guangdeng Zong;Ben Niu;Xudong Zhao","doi":"10.1109/TGCN.2025.3615157","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3615157","url":null,"abstract":"This paper investigates the control problem of multi-input multi-output (MIMO) switched cyber-physical systems (CPSs) subject to unknown sensor and actuator deception attacks. First, due to the presence of unknown deception attacks, the system states become compromised, preventing sensors from obtaining accurate state variables for feedback. To solve this problem, a novel coordinate transformation and attack compensator are designed using the compromised variables. Furthermore, radial basis function neural networks (RBF NNs) and dynamic surface control (DSC) techniques are integrated to mitigate actuator deception attacks. Considering limited communication resources, a two-bit-triggered (TBT) mechanism is proposed to further save communication resources. Under an arbitrary switching rule and the proposed control scheme, it is guaranteed that all signals in the closed-loop system are uniformly ultimately bounded, and the stability error converges to a small neighborhood of the origin. Finally, the effectiveness of the proposed scheme is demonstrated through a simulation example.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1160-1170"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CILAD: Adaptive Feature Distillation and Prototype Replay-Based Incremental Automatic Modulation Recognition 自适应特征提取和基于原型重放的增量自动调制识别

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-07-13 DOI: 10.1109/TGCN.2026.3711969

Xianglin Wei;Chao Wang;Zhen Wang;Xiangjun Chen;Yongyang Hu

{"title":"CILAD: Adaptive Feature Distillation and Prototype Replay-Based Incremental Automatic Modulation Recognition","authors":"Xianglin Wei;Chao Wang;Zhen Wang;Xiangjun Chen;Yongyang Hu","doi":"10.1109/TGCN.2026.3711969","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3711969","url":null,"abstract":"To improve transmission efficiency, modulation classes adopted in wireless communications are becoming increasingly complex, while new modulation classes continue to emerge. These trends necessitate incremental automatic modulation recognition (IAMR) to understand and adapt to dynamically changing communication environments. However, most automatic modulation recognition (AMR) methods are only suitable for identifying fixed modulation classes. Moreover, direct application of computer vision (CV)-oriented incremental learning methods to AMR leads to severe catastrophic forgetting, resulting in degraded AMR accuracy. To address the aforementioned issues, a class-incremental learning (CIL) method based on adaptive feature distillation and prototype replay (CILAD) is proposed. First, a new feature extraction network for IAMR is designed. Second, in the incremental learning stage, a small number of representative old-class exemplars are preserved for subsequent training to enable the incremental model to retain representative features of old modulation classes. Third, an adaptive feature distillation method is utilized to constrain the output representations of each hidden layer and output layer of the model, thereby overcoming feature drift and improving recognition accuracy. Experimental results on both public and private datasets collected from a software-defined radio platform demonstrate that CILAD achieves higher average recognition accuracy and lower forgetting rate than four incremental learning methods.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"3727-3741"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Analysis of THz-AUV RSMA-Based Underwater Acoustic Communication System 基于太赫兹- auv rsma的水声通信系统性能分析

IF 6.9 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-08-06 DOI: 10.1109/TGCN.2026.3720615

Isha Khirwar;Akash Gupta;Aryan Bansal;Parul Garg;Bharat Verma

{"title":"Performance Analysis of THz-AUV RSMA-Based Underwater Acoustic Communication System","authors":"Isha Khirwar;Akash Gupta;Aryan Bansal;Parul Garg;Bharat Verma","doi":"10.1109/TGCN.2026.3720615","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3720615","url":null,"abstract":"This paper proposes a non-terrestrial network (NTN) assisted hybrid Terahertz (THz)–acoustic communication framework for reliable data transmission to underwater autonomous vehicles (AUVs). In the proposed architecture, the NTN node communicates with a floating vessel (FV) using THz carrier, while the FV forwards the information to multiple underwater AUVs via the acoustic channel. To efficiently serve multiple AUVs under limited underwater bandwidth conditions, downlink rate-splitting multiple access (RSMA) technique is employed at the FV, enabling improved spectral efficiency and robust interference management. The FV operates as a decode-and-forward relay, ensuring reliable signal reception from the NTN node and subsequent transmission to underwater AUVs. The THz wireless link connecting NTN node to the FV is modeled using the <inline-formula> <tex-math>$alpha -mu $ </tex-math></inline-formula> fading distribution and accounts various impairments such as free-space-path-loss, molecular absorption, and pointing loss, which are prevalent at THz frequencies. Further, the underwater acoustic link from the FV to the AUVs is modeled using the <inline-formula> <tex-math>$alpha -mathcal {F}$ </tex-math></inline-formula> fading distribution, which captures the harsh and highly variable nature of the underwater acoustic channel. To comprehensively assess system performance under realistic channel conditions, we present novel mathematical expressions for the outage probability, sum ergodic capacity, and throughput. The theoretical plots of the performance metrics have been verified through extensive Monte-Carlo simulations. Further, we have demonstrated the effectiveness of the proposed system compared to power-domain non-orthogonal multiple access under both perfect and imperfect channel state information.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"3904-3914"},"PeriodicalIF":6.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0