Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the d...Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the design of mobile communication systems from first generation(1G)to fifth generation(5G).However,this approach relies on offline channel measurements in specific environments,and thus,the system passively adapts to new environments,resulting in deviation from the optimal performance.As sixth generation(6G)expands into ubiquitous environments and pursues higher capacity,numerous sensing and artificial intelligence(AI)-based methods have emerged to combat random channel fading.However,there remains an urgent need for a proactive and online system design paradigm.From a system perspective,we propose an environment intelligence communication(EIC)based on wireless environmental information theory(WEIT)for 6G.The proposed EIC architecture operates in three steps.First,wireless environmental information(WEI)is acquired using sensing techniques.Then,leveraging WEI and channel data,AI techniques are employed to predict channel fading,thereby mitigating channel uncertainty.Finally,the communication system autonomously determines the optimal air-interface transmission strategy based on real-time channel predictions,enabling intelligent interaction with the physical environment.To make this attractive paradigm shift from theory to practice,we establish WEIT for the first time by answering three key problems:How should WEI be defined?Can it be quantified?Does it hold the same properties as statistical communication information?Subsequently,EIC aided by WEI(EIC-WEI)is validated across multiple air-interface tasks,including channel state information prediction,beam prediction,and radio resource management.Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization.Finally,several open problems and challenges,including regarding its accuracy,complexity,and generalization,are discussed.This work explores a novel and promising way for integrating communication,sensing,and AI capability in 6G.展开更多
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
Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the so...Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the source data rather than the complete set of bits(symbols).However,traditional deep-learning-based SemCom systems present challenges such as limited generalization,low robustness,and inadequate reasoning capabilities,primarily due to the inherently discriminative nature of deep neural networks.To address these limitations,generative artificial intelligence(GAI)is seen as a promising solution,offering notable advantages in learning complex data distributions,transforming data between high-and low-dimensional spaces,and generating high-quality content.This paper explores the applications of GAI in SemCom and presents a comprehensive study.It begins by introducing three widely used SemCom systems enabled by classical GAI models:variational autoencoders,generative adversarial networks,and diffusion models.For each system,the fundamental concept of the GAI model,the corresponding SemCom architecture,and a literature review of recent developments are provided.Subsequently,a novel generative SemCom system is proposed,incorporating cutting-edge GAI technology—large language models(LLMs).This system features LLM-based artificial intelligence(AI)agents at both the transmitter and receiver,which act as“brains”to enable advanced information understanding and content regeneration capabilities,respectively.Unlike traditional systems that focus on bitstream recovery,this design allows the receiver to directly generate the desired content from the coded semantic information sent by the transmitter.As a result,the communication paradigm shifts from“information recovery”to“information regeneration,”marking a new era in generative SemCom.A case study on point-to-point video retrieval is presented to demonstrate the effectiveness of the proposed system,showing a 99.98%reduction in communication overhead and a 53%improvement in average retrieval accuracy compared to traditional communication systems.Furthermore,four typical application scenarios for generative SemCom are described,followed by a discussion of three open issues for future research.In summary,this paper provides a comprehensive set of guidelines for applying GAI in SemCom,laying the groundwork for the efficient deployment of generative SemCom in future wireless networks.展开更多
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for...A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems.展开更多
Cooperative integrated sensing and communication(ISAC),an advanced version of ISAC,is becoming an inevitable paradigm in sixth-generation mobile information networks.Based on the foundation of largescale deployed mobi...Cooperative integrated sensing and communication(ISAC),an advanced version of ISAC,is becoming an inevitable paradigm in sixth-generation mobile information networks.Based on the foundation of largescale deployed mobile networks,cooperative ISAC holds promise to realize ubiquitous sensing,thus becoming a significant step in promoting the transformation from connected things to connected intelligence.In this paper,we depict a sweeping panorama of cooperative ISAC,including the concept,key technologies,a performance evaluation framework,and field trials.We start by introducing the application scenarios of cooperative ISAC,which are the motivation for its commercialization.Next,from the perspective of technical development,we trace the evolution of cooperative ISAC,noting that cooperation within sensing and communication is an objective trend.We reveal the four core features of cooperative ISAC-denoted herein as network-enabled,integration,cooperation,and everything-and provide a general system model.Regarding key technologies,we introduce our contributions to antenna array design,cooperative clustering,synchronization,and data fusion,as well as interference management and networking.We also propose an evaluation framework and define several key performance indicators for cooperative ISAC.Through system-level simulations and field trials,we show the practical application feasibility of cooperative ISAC.Finally,we provide guidance on future research directions in cooperative ISAC.展开更多
With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT ...With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT data are typically scarce in abnormal samples and noisy,making unsupervised learning a common solution.The security challenges of IIoT data in AIoT environments require robust unsupervised anomaly detection methods.While Variational Autoencoders(VAEs)excel in noise resilience,they face two critical challenges in IIoT data:difficulties in single-variable time-series modeling and conflicts between static prior assumptions and dynamic temporal features.To address these challenges,we propose the Greater Cane Rat Algorithm-enhanced FourierWavelet Conditional Variational Autoencoder(GCRA-FWVAE).Our method introduces a time-frequency dualbranch architecture that synergistically combines wavelet transforms for localized transient feature extraction and Fourier transforms for global spectral characterization.These complementary representations jointly regulate the Conditional Variational Autoencoder(CVAE)reconstruction process,effectively preserving critical anomaly signatures while suppressing noise interference.The architecture is further optimized through bioinspired Greater Cane Rat Algorithm(GCRA)to improve adaptive learning capabilities.Extensive validation on the Yahoo benchmark indicates state-of-the-art performance,achieving an F1-score of 93.6%(an improvement of 4.5% over baseline VAEs)and a precision of 95.1%.These improvements significantly increase anomaly detection accuracy and robustness,particularly in the AIoT environment,where it effectively handles more complex and dynamic industrial data.展开更多
While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easi...While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easily be imagined that,without proper design,the enrichment of 6G will further increase system complexity.To address this issue,we propose the Agentic-AI Core(A-Core),an artificial intelligence(AI)-empowered,mission-oriented core network architecture for next-generation mobile telecommunications.In A-Core,network capabilities can be added and updated on the fly and further programmed into missions for enabling and offering diverse services to customers.These missions are created and executed by autonomous network agents according to the customer's intent,which may be expressed in natural language.The agents resolve intents from customers into workflows of network capabilities by leveraging a large-scale network AI model and follow the workflows to execute the mission.As an open,agile system architecture,A-Core holds promise for accelerating innovation and greatly reducing standard release times.The advantages of A-Core are demonstrated through two use cases.展开更多
基金supported by the National Natural Science Foundation of China(62525101 and 62401084)the National Key Research and Development Program of China(2023YFB2904805)the Beijing University of Posts and Telecommunications-China Mobile Communications Group Joint Innovation Center。
摘要Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the design of mobile communication systems from first generation(1G)to fifth generation(5G).However,this approach relies on offline channel measurements in specific environments,and thus,the system passively adapts to new environments,resulting in deviation from the optimal performance.As sixth generation(6G)expands into ubiquitous environments and pursues higher capacity,numerous sensing and artificial intelligence(AI)-based methods have emerged to combat random channel fading.However,there remains an urgent need for a proactive and online system design paradigm.From a system perspective,we propose an environment intelligence communication(EIC)based on wireless environmental information theory(WEIT)for 6G.The proposed EIC architecture operates in three steps.First,wireless environmental information(WEI)is acquired using sensing techniques.Then,leveraging WEI and channel data,AI techniques are employed to predict channel fading,thereby mitigating channel uncertainty.Finally,the communication system autonomously determines the optimal air-interface transmission strategy based on real-time channel predictions,enabling intelligent interaction with the physical environment.To make this attractive paradigm shift from theory to practice,we establish WEIT for the first time by answering three key problems:How should WEI be defined?Can it be quantified?Does it hold the same properties as statistical communication information?Subsequently,EIC aided by WEI(EIC-WEI)is validated across multiple air-interface tasks,including channel state information prediction,beam prediction,and radio resource management.Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization.Finally,several open problems and challenges,including regarding its accuracy,complexity,and generalization,are discussed.This work explores a novel and promising way for integrating communication,sensing,and AI capability in 6G.
基金supported in part by the Basic Research Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone(HZQB-KCZYZ-2021067)the National Natural Science Foundation of China(62293482,62301471,and 62471423)+4 种基金the Shenzhen Outstanding Talents Training Fund(202002)the Guangdong Research Projects(2017ZT07X 152 and 2019CX01X104)the Guangdong Provincial Key Laboratory of Future Networks of Intelligence(2022B1212010001)the Shenzhen Key Laboratory of Big Data and Artificial Intelligence(ZDSYS201707251409055)the National Science and Technology Major Project—Mobile Information Networks(2024ZD1300700)。
摘要Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the source data rather than the complete set of bits(symbols).However,traditional deep-learning-based SemCom systems present challenges such as limited generalization,low robustness,and inadequate reasoning capabilities,primarily due to the inherently discriminative nature of deep neural networks.To address these limitations,generative artificial intelligence(GAI)is seen as a promising solution,offering notable advantages in learning complex data distributions,transforming data between high-and low-dimensional spaces,and generating high-quality content.This paper explores the applications of GAI in SemCom and presents a comprehensive study.It begins by introducing three widely used SemCom systems enabled by classical GAI models:variational autoencoders,generative adversarial networks,and diffusion models.For each system,the fundamental concept of the GAI model,the corresponding SemCom architecture,and a literature review of recent developments are provided.Subsequently,a novel generative SemCom system is proposed,incorporating cutting-edge GAI technology—large language models(LLMs).This system features LLM-based artificial intelligence(AI)agents at both the transmitter and receiver,which act as“brains”to enable advanced information understanding and content regeneration capabilities,respectively.Unlike traditional systems that focus on bitstream recovery,this design allows the receiver to directly generate the desired content from the coded semantic information sent by the transmitter.As a result,the communication paradigm shifts from“information recovery”to“information regeneration,”marking a new era in generative SemCom.A case study on point-to-point video retrieval is presented to demonstrate the effectiveness of the proposed system,showing a 99.98%reduction in communication overhead and a 53%improvement in average retrieval accuracy compared to traditional communication systems.Furthermore,four typical application scenarios for generative SemCom are described,followed by a discussion of three open issues for future research.In summary,this paper provides a comprehensive set of guidelines for applying GAI in SemCom,laying the groundwork for the efficient deployment of generative SemCom in future wireless networks.
基金the Special Research Fund for the Na-tional Key Research and Development Program of China(No.2022ZD0119001)。
摘要A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems.
基金funding from China Mobile Communications Group Co.,Ltd。
摘要Cooperative integrated sensing and communication(ISAC),an advanced version of ISAC,is becoming an inevitable paradigm in sixth-generation mobile information networks.Based on the foundation of largescale deployed mobile networks,cooperative ISAC holds promise to realize ubiquitous sensing,thus becoming a significant step in promoting the transformation from connected things to connected intelligence.In this paper,we depict a sweeping panorama of cooperative ISAC,including the concept,key technologies,a performance evaluation framework,and field trials.We start by introducing the application scenarios of cooperative ISAC,which are the motivation for its commercialization.Next,from the perspective of technical development,we trace the evolution of cooperative ISAC,noting that cooperation within sensing and communication is an objective trend.We reveal the four core features of cooperative ISAC-denoted herein as network-enabled,integration,cooperation,and everything-and provide a general system model.Regarding key technologies,we introduce our contributions to antenna array design,cooperative clustering,synchronization,and data fusion,as well as interference management and networking.We also propose an evaluation framework and define several key performance indicators for cooperative ISAC.Through system-level simulations and field trials,we show the practical application feasibility of cooperative ISAC.Finally,we provide guidance on future research directions in cooperative ISAC.
基金supported by the National Natural Science Foundation of China(No.62472118)the Guangxi Science and Technology Program(No.AB24010315)+2 种基金the Central Guidance on Local Science and Technology Development Fund of Guangxi Province(No.ZY23055008)the Innovation Project of Guangxi Graduate Education(No.YCSW2025348)the Innovation Platform and Talent Program of Guilin City(No.20220124-12).
摘要With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT data are typically scarce in abnormal samples and noisy,making unsupervised learning a common solution.The security challenges of IIoT data in AIoT environments require robust unsupervised anomaly detection methods.While Variational Autoencoders(VAEs)excel in noise resilience,they face two critical challenges in IIoT data:difficulties in single-variable time-series modeling and conflicts between static prior assumptions and dynamic temporal features.To address these challenges,we propose the Greater Cane Rat Algorithm-enhanced FourierWavelet Conditional Variational Autoencoder(GCRA-FWVAE).Our method introduces a time-frequency dualbranch architecture that synergistically combines wavelet transforms for localized transient feature extraction and Fourier transforms for global spectral characterization.These complementary representations jointly regulate the Conditional Variational Autoencoder(CVAE)reconstruction process,effectively preserving critical anomaly signatures while suppressing noise interference.The architecture is further optimized through bioinspired Greater Cane Rat Algorithm(GCRA)to improve adaptive learning capabilities.Extensive validation on the Yahoo benchmark indicates state-of-the-art performance,achieving an F1-score of 93.6%(an improvement of 4.5% over baseline VAEs)and a precision of 95.1%.These improvements significantly increase anomaly detection accuracy and robustness,particularly in the AIoT environment,where it effectively handles more complex and dynamic industrial data.
摘要While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easily be imagined that,without proper design,the enrichment of 6G will further increase system complexity.To address this issue,we propose the Agentic-AI Core(A-Core),an artificial intelligence(AI)-empowered,mission-oriented core network architecture for next-generation mobile telecommunications.In A-Core,network capabilities can be added and updated on the fly and further programmed into missions for enabling and offering diverse services to customers.These missions are created and executed by autonomous network agents according to the customer's intent,which may be expressed in natural language.The agents resolve intents from customers into workflows of network capabilities by leveraging a large-scale network AI model and follow the workflows to execute the mission.As an open,agile system architecture,A-Core holds promise for accelerating innovation and greatly reducing standard release times.The advantages of A-Core are demonstrated through two use cases.