Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in...Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.展开更多
Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel ...Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.展开更多
An“Intrusion Detection System”(IDS)is a security measure designed to perceive and be aware of unauthorized access or malicious activity on a computer system or network.Signature-based IDSs employ an attack signature...An“Intrusion Detection System”(IDS)is a security measure designed to perceive and be aware of unauthorized access or malicious activity on a computer system or network.Signature-based IDSs employ an attack signature database to identify intrusions.This indicates that the system can only identify known attacks and cannot identify brand-new or unidentified assaults.In Wireless 6G IoT networks,signature-based IDSs can be useful to detect a wide range of known attacks such as viruses,worms,and Trojans.However,these networks have specific requirements and constraints,such as the need for real-time detection and low-power operation.To meet these requirements,the IDS algorithm should be designed to be efficient in terms of resource usage and should include a mechanism for updating the attack signatures to keep up with evolving threats.This paper provides a solution for a signature-based intrusion detection system in wireless 6G IoT Networks,in which three different algorithms were used and implemented by using python and JavaScript programming languages and an accuracy of 98.9%is achieved.展开更多
In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with...In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.展开更多
Intent-Based Networks(IBNs),which are originally proposed to introduce Artificial Intelligence(AI)into the sixth-generation(6G)wireless networks,can effectively solve the challenges of traditional networks in terms of...Intent-Based Networks(IBNs),which are originally proposed to introduce Artificial Intelligence(AI)into the sixth-generation(6G)wireless networks,can effectively solve the challenges of traditional networks in terms of efficiency,flexibility,and security.IBNs are mainly used to transform users’business intent into network configuration,operation,and maintenance strategies,which are prominent for designing the AI-enabled 6G networks.In particular,in order to meet the massive,intelligent service demands and overcome the time-varying radio propagation,IBNs can continuously learn and adapt to the time-varying network environment based on the massive collected network data in real-time.From the aspects of both the core network and radio access network,this article comprehensively surveys the architectures and key techniques of IBNs for 6G.In particular,the demonstration platforms of IBNs,such as the Apstra Operating System,Forward Networks Verification Platform,and One Convergence Service Interaction Platform,are presented.Moreover,the industrial development of IBNs is elaborated,including the emerging new products and startups to solve the problems of open data platforms,automated network operations,and preemptive network fault diagnosis.Finally,several open issues and challenges are identified as well to spur future researches.展开更多
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
文摘Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.
文摘Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.
文摘An“Intrusion Detection System”(IDS)is a security measure designed to perceive and be aware of unauthorized access or malicious activity on a computer system or network.Signature-based IDSs employ an attack signature database to identify intrusions.This indicates that the system can only identify known attacks and cannot identify brand-new or unidentified assaults.In Wireless 6G IoT networks,signature-based IDSs can be useful to detect a wide range of known attacks such as viruses,worms,and Trojans.However,these networks have specific requirements and constraints,such as the need for real-time detection and low-power operation.To meet these requirements,the IDS algorithm should be designed to be efficient in terms of resource usage and should include a mechanism for updating the attack signatures to keep up with evolving threats.This paper provides a solution for a signature-based intrusion detection system in wireless 6G IoT Networks,in which three different algorithms were used and implemented by using python and JavaScript programming languages and an accuracy of 98.9%is achieved.
基金supported by the Natural Science Foundation of Beijing Municipality under Grant L192034。
文摘In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.
基金This work was supported in part by the State Major Science and Technology Special Project(Grant No.2018ZX03001002-004 and 2018ZX03001023)the National Natural Science Foundation of China under No.61921003,61925101,61831002,and 61901044+1 种基金the Beijing Natural Science Foundation under No.JQ18016and the National Program for Special Support of Eminent Professionals.
文摘Intent-Based Networks(IBNs),which are originally proposed to introduce Artificial Intelligence(AI)into the sixth-generation(6G)wireless networks,can effectively solve the challenges of traditional networks in terms of efficiency,flexibility,and security.IBNs are mainly used to transform users’business intent into network configuration,operation,and maintenance strategies,which are prominent for designing the AI-enabled 6G networks.In particular,in order to meet the massive,intelligent service demands and overcome the time-varying radio propagation,IBNs can continuously learn and adapt to the time-varying network environment based on the massive collected network data in real-time.From the aspects of both the core network and radio access network,this article comprehensively surveys the architectures and key techniques of IBNs for 6G.In particular,the demonstration platforms of IBNs,such as the Apstra Operating System,Forward Networks Verification Platform,and One Convergence Service Interaction Platform,are presented.Moreover,the industrial development of IBNs is elaborated,including the emerging new products and startups to solve the problems of open data platforms,automated network operations,and preemptive network fault diagnosis.Finally,several open issues and challenges are identified as well to spur future researches.
基金supported by National Key Research and Development Project 2018YFE0205503Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘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.
