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A blockchain-based user-centric identity management toward 6G networks 认领 引用
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作者 Guoqiang Zhang Qiwei Hu +1 位作者 Yu Zhang Tao Jiang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期1-10,共10页
The developing Sixth-Generation(6G)network aims to establish seamless global connectivity for billions of humans,machines,and devices.However,the rich digital service and the explosive heterogeneous connection between... The developing Sixth-Generation(6G)network aims to establish seamless global connectivity for billions of humans,machines,and devices.However,the rich digital service and the explosive heterogeneous connection between various entities in 6G networks can not only induce increasing complications of digital identity management,but also raise material concerns about the security and privacy of the user identity.In this paper,we design a user-centric identity management that returns the sole control to the user self and achieves identity sovereignty toward 6G networks.Specifically,we propose a blockchain-based Identity Management(IDM)architecture for 6G networks,which provides a practical method to secure digital identity management.Subsequently,we develop a fully privacy-preserving identity attribute management scheme by using zero-knowledge proof to protect the privacy-sensitive identity attribute.In particular,the scheme achieves an identity attribute hiding and verification protocol to support users in obtaining and applying their identity attributes without revealing concrete data.Finally,we analyze the security of the proposed architecture and implement a prototype system to evaluate its performance.The results demonstrate that our architecture ensures effective user digital identity management in 6G networks. 展开更多
关键词 The sixth generation(6G)network User-centric identity management Blockchain Decentralized identity Privacy preservation
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Federated Learning for Vision-Based Applications in 6G Networks: A Simulation-Based Performance Study 认领 引用 被引量：1
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作者 Manuel J.C.S.Reis Nishu Gupta 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期4225-4243,共19页
The forthcoming sixth generation(6G)of mobile communication networks is envisioned to be AInative,supporting intelligent services and pervasive computing at unprecedented scale.Among the key paradigms enabling this vi... The forthcoming sixth generation(6G)of mobile communication networks is envisioned to be AInative,supporting intelligent services and pervasive computing at unprecedented scale.Among the key paradigms enabling this vision,Federated Learning(FL)has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data,thereby preserving privacy and reducing the need for centralized storage.This capability is particularly attractive for vision-based applications,where image and video data are both sensitive and bandwidth-intensive.However,the integration of FL with 6G networks presents unique challenges,including communication bottlenecks,device heterogeneity,and trade-offs between model accuracy,latency,and energy consumption.In this paper,we developed a simulation-based framework to investigate the performance of FL in representative vision tasks under 6G-like environments.We formalize the system model,incorporating both the federated averaging(FedAvg)training process and a simplified communication costmodel that captures bandwidth constraints,packet loss,and variable latency across edge devices.Using standard image datasets(e.g.,MNIST,CIFAR-10)as benchmarks,we analyze how factors such as the number of participating clients,degree of data heterogeneity,and communication frequency influence convergence speed and model accuracy.Additionally,we evaluate the effectiveness of lightweight communication-efficient strategies,including local update tuning and gradient compression,in mitigating network overhead.The experimental results reveal several key insights:(i)communication limitations can significantly degrade FL convergence in vision tasks if not properly addressed;(ii)judicious tuning of local training epochs and client participation levels enables notable improvements in both efficiency and accuracy;and(iii)communication-efficient FL strategies provide a promising pathway to balance performance with the stringent latency and reliability requirements expected in 6G.These findings highlight the synergistic role of AI and nextgeneration networks in enabling privacy-preserving,real-time vision applications,and they provide concrete design guidelines for researchers and practitioners working at the intersection of FL and 6G. 展开更多
关键词 Federated learning 6G networks edge intelligence vision-based applications communication-efficient learning privacy-preserving AI
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A Predictive 6G Network with Environment Sensing Enhancement:From Radio Wave Propagation Perspective 认领 引用 被引量：12
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作者 Gaofeng Nie Jianhua Zhang +6 位作者 Yuxiang Zhang Li Yu Zhen Zhang Yutong Sun Lei Tian Qixing Wang Liang Xia 《China Communications》 SCIE CSCD 2022年第6期105-122,共18页
In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get... In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally. 展开更多
关键词 6G network electromagnetic waves propagation characteristics prediction environment information sensing enhancement
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Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management 认领 引用 被引量：3
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作者 Xiangnan Liu Haijun Zhang +3 位作者 Min Sheng Wei Li Saba Al-Rubaye Keping Long 《China Communications》 SCIE CSCD 2023年第10期262-275,共14页
With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integ... With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks. 展开更多
关键词 satellite-enabled 6G networks network architecture resource optimization interference management
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A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments 认领 引用 被引量：2
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作者 Borja Bordel Sánchez Ramón Alcarria Tomás Robles 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期631-654,共24页
Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control l... Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks. 展开更多
关键词 6G networks noise injection attacks Gaussian mixture model Bessel function traffic filter Volterra filter
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Intelligent Decision Making Framework for 6G Network 认领 引用 被引量：3
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作者 Zheng Hu Ping Zhang +4 位作者 Chunhong Zhang Benhui Zhuang Jianhua Zhang Shangjing Lin Tao Sun 《China Communications》 SCIE CSCD 2022年第3期16-35,共20页
Sixth Generation(6G)wireless communication network has been expected to provide global coverage,enhanced spectral efficiency,and AI(Artificial Intelligence)-native intelligence,etc.To meet these requirements,the compu... Sixth Generation(6G)wireless communication network has been expected to provide global coverage,enhanced spectral efficiency,and AI(Artificial Intelligence)-native intelligence,etc.To meet these requirements,the computational concept of Decision-Making of cognition intelligence,its implementation framework adapting to foreseen innovations on networks and services,and its empirical evaluations are key techniques to guarantee the generationagnostic intelligence evolution of wireless communication networks.In this paper,we propose an Intelligent Decision Making(IDM)framework,acting as the role of network brain,based on Reinforcement Learning modelling philosophy to empower autonomous intelligence evolution capability to 6G network.Besides,usage scenarios and simulation demonstrate the generality and efficiency of IDM.We hope that some of the ideas of IDM will assist the research of 6G network in a new or different light. 展开更多
关键词 6G wireless communication network reinforcement learning cognition intelligence
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Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks 认领 引用
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作者 Mrim M.Alnfiai 《Computers, Materials & Continua》 SCIE EI 2023年第3期5723-5740,共18页
In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperativ... In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters. 展开更多
关键词 6G networks communication signal modulation recognition deep learning machine learning parameter optimization
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Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks 认领 引用 被引量：5
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作者 Yunus Dursun Fang Fang Zhiguo Ding 《China Communications》 SCIE CSCD 2022年第10期12-20,共9页
Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks... Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA. 展开更多
关键词 NOMA MEC MIMO Generalized sin-gular value decomposition sixth generation networks(6G) delay minimization
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Artificial Intelligence (AI)-Enabled Unmanned Aerial Vehicle (UAV) Systems for Optimizing User Connectivity in Sixth-Generation (6G) Ubiquitous Networks 认领 引用 被引量：1
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作者 Zeeshan Ali Haider Inam Ullah +2 位作者 Ahmad Abu Shareha Rashid Nasimov Sufyan Ali Memon 《Computers, Materials & Continua》 SCIE EI 2026年第1期534-549,共16页
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener... The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment. 展开更多
关键词 6G networks UAV-based communication cooperative reinforcement learning network optimization user connectivity energy efficiency
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Information Diffusion Models and Fuzzing Algorithms for a Privacy-Aware Data Transmission Scheduling in 6G Heterogeneous ad hoc Networks 认领 引用 被引量：1
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作者 Borja Bordel Sánchez Ramón Alcarria Tomás Robles 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1214-1234,共21页
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h... In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services. 展开更多
关键词 6G networks ad hoc networks privacy scheduling algorithms diffusion models fuzzing algorithms
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Personalized Generative AI Services Through Federated Learning in 6G Edge Networks 认领 引用 被引量：3
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作者 Li Zeshen Chen Zihan +1 位作者 Hu Xinyi Howard H.Yang 《China Communications》 SCIE EI CSCD 2025年第7期1-13,共13页
Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse ... Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse service requirements,6G network architecture should offer personalized services to various mobile devices.Federated learning(FL)with personalized local training,as a privacypreserving machine learning(ML)approach,can be applied to address these challenges.In this paper,we propose a meta-learning-based personalized FL(PFL)method that improves both communication and computation efficiency by utilizing over-the-air computations.Its“pretraining-and-fine-tuning”principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy.Experiment results demonstrate the outperformance and efficacy of the proposed algorithm,and notably indicate enhanced communication efficiency without compromising accuracy. 展开更多
关键词 generative artificial intelligence personalized federated learning 6G networks
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The Dawn of 6G:Empowering a User-Centric Ecosystem with Agentic AI 认领 引用
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作者 Gao Yin Chen Jiajun +1 位作者 Liu Yansheng Xiang Jiying 《ZTE Communications》 2026年第2期43-51,共9页
The convergence of artificial intelligence(AI)with the physical world is reshaping the future of intelligent systems through realtime perception,interaction,and control within physical environments.To support this new... The convergence of artificial intelligence(AI)with the physical world is reshaping the future of intelligent systems through realtime perception,interaction,and control within physical environments.To support this new paradigm,6G networks are envisioned as critical enablers,offering ultra-low latency,high reliability,and service-aware intelligence to facilitate seamless human-machine collaboration.This paper proposes a functional framework that integrates Agentic AI into the 6G architecture,introducing the concept of Agentic AI-Enabled 6G Network Services(AA6NS).In this framework,user intents are translated and processed across the application layer,core network(CN),and radio access network(RAN),where Agentic AI dynamically manages task-level quality of service/quality of experience(QoS/QoE),orchestrates multi-device service groups,and enables real-time network adaptation.The proposed architecture with the new 6G techniques establishes a foundation for future physical AI applications across domains such as autonomous mobility,smart manufacturing,and remote robotics. 展开更多
关键词 AI/ML Agentic AI 6G network physical AI
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Resource allocation for AI-native healthcare systems in 6G dense networks using deep reinforcement learning 认领 引用
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作者 Jianhui Lv Chien-Ming Chen +1 位作者 Saru Kumari Keqin Li 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期2016-2029,共14页
Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery,resource management in dense medical device networks stays a basic issue.Reliable communication direct... Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery,resource management in dense medical device networks stays a basic issue.Reliable communication directly affects patient outcomes in these settings;nonetheless,current resource allocation techniques struggle with complicated interference patterns and different service needs of AI-native healthcare systems.In dense installations where conventional approaches fail,this paper tackles the challenge of combining network efficiency with medical care priority.Thus,we offer a Dueling Deep Q-Network(DDQN)-based resource allocation approach for AI-native healthcare systems in 6G dense networks.First,we create a point-line graph coloringbased interference model to capture the unique characteristics of medical device communications.Building on this foundation,we suggest a DDQN approach to optimal resource allocation over multiple medical services by combining advantage estimate with healthcare-aware state evaluation.Unlike traditional graph-based models,this one correctly depicts the overlapping coverage areas common in hospital environments.Building on this basis,our DDQN design allows the system to prioritize medical needs while distributing resources by separating healthcare state assessment from advantage estimation.Experimental findings show that the suggested DDQN outperforms state-of-the-art techniques in dense healthcare installations by 14.6%greater network throughput and 13.7%better resource use.The solution shows particularly strong in maintaining service quality under vital conditions with 5.5%greater Qo S satisfaction for emergency services and 8.2%quicker recovery from interruptions. 展开更多
关键词 Resource allocation AI-native healthcare systems 6G dense networks Deep reinforcement learning
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A Task-Driven Design Approach for 6G AI-Native Architecture 认领 引用 被引量：1
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作者 Xiaoyun Wang Lu Lu +4 位作者 Qin Li Qi Sun Nanxiang Shi Ziqi Chen Tao Sun 《Engineering》 SCIE EI CSCD 2026年第1期87-103,共17页
The deep integration of mobile networks with artificial intelligence(AI)has emerged as a pivotal driving force for the sixth-generation(6G)mobile network.AI-native 6G represents a paradigm shift for mobile networks,as... The deep integration of mobile networks with artificial intelligence(AI)has emerged as a pivotal driving force for the sixth-generation(6G)mobile network.AI-native 6G represents a paradigm shift for mobile networks,as it not only embeds AI into network components to enhance network intelligence and automation but also transforms 6G into a foundational infrastructure for enabling pervasive AI applications and services.This paper proposes a novel 6G AI-native architecture.The challenges and requirements for the AI-native 6G mobile network are first analyzed,followed by the development of a task-driven approach for architecture design based on insights from system theory.Then,a 6G AI-native architecture is proposed,featuring the integration of distributed AI data and computing components with layered centralized collaborative control and flexible on-demand deployment.Key components and procedures for the 6G AI-native architecture are also discussed in detail.Finally,standardization practices for the convergence of mobile networks and AI in fifth-generation(5G)networks are analyzed,and an outlook on the standardization of AI-native design in 6G is given.This paper aims to provide not only theoretical insights into AI-native architecture design methodology but also a comprehensive 6G AI-native architecture that lays a foundation for the transition from mobile communications toward mobile information services in the 6G era. 展开更多
关键词 Sixth-generation(6G)mobile network Artificial intelligence 6G AI-native architecture Task-driven approach
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面向6G的空天地一体化网络:人工智能赋能优化机制研究 认领 引用 被引量：3
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作者 李琳佩 张海君 +2 位作者 孙春蕾 管婉青 张美杰 《工程科学学报》 EI CAS CSCD 北大核心 2026年第2期316-330,共15页
随着第六代移动通信系统(6th generation mobile communication system, 6G)通信技术的发展,空天地一体化网络(Spaceair-ground integrated network, SAGIN)作为6G的重要组成部分,旨在实现卫星、空中平台与地面系统的无缝互联,在应急通... 随着第六代移动通信系统(6th generation mobile communication system, 6G)通信技术的发展,空天地一体化网络(Spaceair-ground integrated network, SAGIN)作为6G的重要组成部分,旨在实现卫星、空中平台与地面系统的无缝互联,在应急通信、环境监测、智能交通等领域展现出巨大的潜力.然而,SAGIN具有异构结构、链路动态性高、资源分布广泛等特征,给网络的高效管理与优化带来巨大的挑战.近年来,人工智能(Artificial intelligence, AI)技术凭借强大的感知、学习与自主决策能力应用于通信网络,为SAGIN的智能演进提供了新契机.本文首先系统介绍SAGIN网络架构的基本组成与关键特征,并梳理当前主流AI技术在网络优化中的主要技术体系与适配优势,包括机器学习、图神经网络以及强化学习.其次,本文深入探讨了AI技术在SAGIN中智能资源管理、移动性管理与路由优化、空中平台路径规划、任务卸载与计算协同等典型场景中的应用与最新进展.最后,本文总结了AI技术应用在SAGIN网络中面临的挑战并展望了AI与SAGIN融合发展的未来方向.本文概述了AI技术在SAGIN网络中应用的优势与进展,旨在为AI赋能的SAGIN研究与应用发展提供技术参考. 展开更多
关键词 6G 空天地一体化网络 人工智能 深度强化学习 资源管理
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RIS-assisted cellular networks with multiple D2D pairs:Outage and ergodic achievable rate 认领 引用
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作者 Yaxuan Liu Yiyang Ni +2 位作者 Haitao Zhao Yuxi Wang Yan Cai 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期52-65,共14页
Reconfigurable Intelligent Surface(RIS)is envisioned as a promising technology to improve the system capacity of 6G network,by controlling the electromagnetic wave propagation.Most existing works use the Central Limit... Reconfigurable Intelligent Surface(RIS)is envisioned as a promising technology to improve the system capacity of 6G network,by controlling the electromagnetic wave propagation.Most existing works use the Central Limit Theorem(CLT)to analyze the performance of RIS-assisted systems for large number of reflective elements.However,the assumption of extremely large number of elements may not be practical in the actual situation.In addition,the CLT-based approximation yields an inaccurate scaling law of the outage probability when the transmit Signal-to-Noise Ratio(SNR)tends to infinity.Motivated by these limitations,in this paper,we investigate the performance of RIS-assisted cellular networks with multiple Device-to-Device(D2D)users under the general fading channels,i.e.,Nakagami-m fading channels.We propose a tractable solution to evaluate the outage probability and the ergodic achievable rate,which is accurate for any number of reflective elements,any network topology,as well as any SNR.In addition,the accurate approximations for the high SNR case and the large number of reflective elements case are further derived in simpler closed form.Numerical results verify the accuracy of our analytical results and analyze the performance between CLT and the proposed method. 展开更多
关键词 Reconfigurable intelligent surface 6G cellular networks Device-to-device Outage probability Ergodic achievable rate Nakagami-fading
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6G工业互联驱动的多层级制造网络与自适应协同优化控制方法研究 认领 引用 被引量：1
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作者 郭振刚 张映锋 +2 位作者 梁伟聪 林海峰 黎小华 《机械工程学报》 EI CAS CSCD 北大核心 2026年第11期90-101,共12页
面向定制化生产的离散型航空产品制造企业面临的诸如产品复杂度高、物料种类繁多、跨代混线生产、零部件配套关系复杂、数字化水平参差不齐、数据互联壁垒高等瓶颈,导致生产中断频繁发生、快速响应中断难、跨企业协作难,为其均衡生产和... 面向定制化生产的离散型航空产品制造企业面临的诸如产品复杂度高、物料种类繁多、跨代混线生产、零部件配套关系复杂、数字化水平参差不齐、数据互联壁垒高等瓶颈,导致生产中断频繁发生、快速响应中断难、跨企业协作难,为其均衡生产和准时交付带来了巨大挑战。结合国家航空强国和制造强国重大战略需求,针对我国航空产品制造企业在集团、主机、配套等多层级网络化制造协同能力和中断响应快速恢复能力方面的新需求和挑战,围绕面向交付计划的网络化制造协同体系和智能化管控问题,提出一种6G工业互联驱动的多层级制造网络与自适应协同优化控制方法,通过构建多层级复杂网络广义模型,研究多层级制造网络协同机理,利用人工智能算法分析协同性能,建立变粒度多层级自适应优化控制模型响应中断并快速恢复,为具有主动感知、敏捷响应、协同优化、智能管控的网络化制造协同体系提供重要的理论与技术支持。 展开更多
关键词 6G物联网 工业互联网 智能制造 复杂网络 协同优化
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面向6G空天地一体化网络的光电融合传输技术与发展趋势 认领 引用
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作者 吴启晖 冯斯梦 +4 位作者 王婉婷 方正皓 刘小利 李宝龙 董超 《数据采集与处理》 CSCD 北大核心 2026年第2期288-302,共15页
面向第六代移动通信(Sixth generation of communication system,6G)网络全域立体覆盖与海量连接的需求,构建空天地一体化的高效传输体系已成为重要发展方向。然而,单一射频(Radio frequency,RF)或自由空间光(Free-space optical,FSO)... 面向第六代移动通信(Sixth generation of communication system,6G)网络全域立体覆盖与海量连接的需求,构建空天地一体化的高效传输体系已成为重要发展方向。然而,单一射频(Radio frequency,RF)或自由空间光(Free-space optical,FSO)通信技术均存在固有局限,难以独立满足未来网络对超高速率、超高可靠与广域动态接入的综合要求。在此背景下,融合RF与FSO通信的互补优势构建智能协同的空天地一体化光电融合传输网络成为突破现有技术瓶颈的关键路径。本文系统综述了该领域的国内外研究进展,针对空天地一体化网络特征构建了基于光电融合的认知软件定义网络体系架构,重点阐述了适用于空天地异构环境的RF信道与FSO信道建模方法,深入剖析了高动态链路精准对准、异构资源智能分配、极端环境鲁棒传输等核心挑战。进而,围绕光电融合波束跟踪、自适应光电切换、光电并行协同传输及场景化链路选择等关键技术进行了详细论述。最后,展望了智能算法深度赋能、跨域抗扰动传输增强以及效能综合优化等未来发展趋势。研究表明,光电融合技术能够有效提升空天地一体化网络的综合性能,但其走向规模化应用仍需在跨层协同机制、动态资源管控及系统级效能评估等方面持续深化研究。 展开更多
关键词 6G 空天地一体化网络 光电融合通信
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6G绿色内生网络技术研究 认领 引用
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作者 吕婷 李福昌 +1 位作者 张忠皓 曹亘 《邮电设计技术》 2026年第2期11-16,共6页
作为新一代信息服务网络,6G网络在性能跃升的同时面临能耗上升的严峻挑战。为了促进6G可持续发展,分析了6G绿色内生网络的驱动力与挑战,从绿色组网架构、通感算智协同调度、端网协同的绿色空口、基于AI的智能节能几个方面介绍了6G绿色... 作为新一代信息服务网络,6G网络在性能跃升的同时面临能耗上升的严峻挑战。为了促进6G可持续发展,分析了6G绿色内生网络的驱动力与挑战,从绿色组网架构、通感算智协同调度、端网协同的绿色空口、基于AI的智能节能几个方面介绍了6G绿色内生网络技术,涉及6G网络设计、部署、运行、编排管理等各环节,系统性构建6G节能技术体系。 展开更多
关键词 6G 绿色内生 绿色组网 绿色空口 智能节能
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面向6G室分通信的太赫兹技术应用挑战与破局 认领 引用
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作者 杨占春 张婧 +3 位作者 吕沛锦 杨晓康² 胡德显 唐俊 《长江信息通信》 2026年第4期16-19,共4页
随着6G技术发展,室内通信面临频谱资源紧张、覆盖不均和干扰管控等挑战。太赫兹频段因其超大带宽和超低时延,成为突破6G室分网络容量与速率瓶颈的关键。文章系统分析了太赫兹技术在6G室分网络中用于超高速传输、高精度通信及网络重构的... 随着6G技术发展,室内通信面临频谱资源紧张、覆盖不均和干扰管控等挑战。太赫兹频段因其超大带宽和超低时延,成为突破6G室分网络容量与速率瓶颈的关键。文章系统分析了太赫兹技术在6G室分网络中用于超高速传输、高精度通信及网络重构的潜力,并深入探讨了其在信号衰减、硬件实现、波束管理及协议兼容等方面的核心瓶颈。针对这些挑战,研究提出了混合组网、智能反射面辅助传输及AI增强动态补偿等关键技术方案。并证明方案能有效扩展覆盖、提升系统吞吐量并优化移动性。 展开更多
关键词 太赫兹通信 6G 室分网络 混合多频组网 智能反射面 波束管理
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