为满足全球无缝覆盖、应急通信、物联网与航空海事等新兴业务需求,3GPP R19阶段对NTN进行了系统性增强。相比R18以透明转发为主的架构,3GPP R19 NR NTN首次引入星上再生处理架构,实现接入网与核心网功能在卫星上的灵活部署,显著降低端...为满足全球无缝覆盖、应急通信、物联网与航空海事等新兴业务需求,3GPP R19阶段对NTN进行了系统性增强。相比R18以透明转发为主的架构,3GPP R19 NR NTN首次引入星上再生处理架构,实现接入网与核心网功能在卫星上的灵活部署,显著降低端到端时延并提升系统容量,并对R18多个方面进行了增强和扩展,使得5G技术方案更加完整,技术能力进一步增强,为5G NR NTN商用提供了坚实的技术基础。以当前6G卫星互联网发展中面临的挑战作为出发点,重点围绕R19 NR NTN核心架构、空口与性能优化、移动性与服务连续性、终端与场景扩展等方面展开分析,最后对R20的技术发展进行了展望。展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
Integrated Sensing and Communication(ISAC)is considered a key technology in 6G networks.An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems.The widely used Ge...Integrated Sensing and Communication(ISAC)is considered a key technology in 6G networks.An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems.The widely used Geometry-Based Stochastic Model(GBSM),typically applied in standardized channel modeling,mainly focuses on the statistical fading characteristics of the channel.However,it fails to capture the characteristics of targets in ISAC systems,such as their positions and velocities,as well as the impact of the targets on the background.To address this issue,this paper proposes an Extended-GBSM(E-GBSM)sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework.In this framework,the sensing channel is divided into target and background channels.For the target channel,the model introduces a concatenated modeling approach,while for the background channel,a parameter called the power control factor is introduced to assess impact of the target on the background channel,making the modeling framework applicable to both mono-static and bi-static sensing modes.To validate the proposed model’s effectiveness,measurements of target and background channels are conducted across a wide range of indoor and outdoor scenarios,covering various sensing targets such as metal plates,reconfigurable intelligent surfaces,human bodies,unmanned aerial vehicles,and vehicles.The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.展开更多
Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pa...Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pattern projection.However,the imaging speed of conventional fringe projection profilometry(FPP)remains limited by the native sensor refresh rates due to the inherent"one-to-one"synchronization mechanism between pattern projection and image acquisition in standard structured light techniques.Here,we present dual-frequency angular-multiplexed fringe projection profilometry(DFAMFPP),a deep learning-enabled 3D imaging technique that achieves high-speed,high-precision,and large-depth-range absolute 3D surface measurements at speeds 16 times faster than the sensor's native frame rate.By encoding multi-timeframe 3D information into a single multiplexed image using multiple pairs of dual-frequency fringes,high-accuracy absolute phase maps are reconstructed using specially trained two-stage number-theoretical-based deep neural networks.We validate the effectiveness of DFAMFPP through dynamic scene measurements,achieving 10,000 Hz 3D imaging of a running turbofan engine prototype with only a 625 Hz camera.By overcoming the sensor hardware bottleneck,DFAMFPP significantly advances high-speed and ultra-high-speed 3D imaging,opening new avenues for exploring dynamic processes across diverse scientific disciplines.展开更多
Grating fringe projection 3D measurement techniques are extensively applied in various fields.However,in high dynamic range scenarios with significant surface reflectivity variations,uneven greyscale distribution may ...Grating fringe projection 3D measurement techniques are extensively applied in various fields.However,in high dynamic range scenarios with significant surface reflectivity variations,uneven greyscale distribution may lead to phase errors and poor reconstruction results.To address this problem,an adaptive fringe projection method is introduced.The method involves projecting two sets of dark and light fringes onto the object,enabling the full-field projection intensity map to be generated adaptively based on greyscale analysis.First,dark fringes are projected onto the object to extend exposure time as long as possible without causing overexposure in the image.Subsequently,bright fringes are projected under the same exposure settings to detect overexposed pixels,and the greyscale distribution of these overexposed points from the previous dark fringe projection is analyzed to calculate the corresponding projection intensities.Finally,absolute phase information from orthogonal fringes is used for coordinate matching,enabling the generation of adaptive projection fringe patterns.Experiments on various high dynamic range objects show that compared to conventional fringe projection binocular reconstruction method,the proposed algorithm achieves complete reconstruction of high dynamic range surfaces and shows robust performance against phase calculation errors caused by overexposure and low modulation.展开更多
文摘为满足全球无缝覆盖、应急通信、物联网与航空海事等新兴业务需求,3GPP R19阶段对NTN进行了系统性增强。相比R18以透明转发为主的架构,3GPP R19 NR NTN首次引入星上再生处理架构,实现接入网与核心网功能在卫星上的灵活部署,显著降低端到端时延并提升系统容量,并对R18多个方面进行了增强和扩展,使得5G技术方案更加完整,技术能力进一步增强,为5G NR NTN商用提供了坚实的技术基础。以当前6G卫星互联网发展中面临的挑战作为出发点,重点围绕R19 NR NTN核心架构、空口与性能优化、移动性与服务连续性、终端与场景扩展等方面展开分析,最后对R20的技术发展进行了展望。
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金supported in part by the Young Scientists Fund of the National Natural Science Foundation of China(No.62201087)in part by the National Natural Science Foundation of China(No.62525101,62341128)+3 种基金in part by the National Key R&D Program of China(No.2023YFB2904803)in part by the Guangdong Major Project of Basic and Applied Basic Research(No.2023B0303000001)in part by the Beijing Natural Science Foundation(No.L243002)in part by the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint innovation Center.
文摘Integrated Sensing and Communication(ISAC)is considered a key technology in 6G networks.An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems.The widely used Geometry-Based Stochastic Model(GBSM),typically applied in standardized channel modeling,mainly focuses on the statistical fading characteristics of the channel.However,it fails to capture the characteristics of targets in ISAC systems,such as their positions and velocities,as well as the impact of the targets on the background.To address this issue,this paper proposes an Extended-GBSM(E-GBSM)sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework.In this framework,the sensing channel is divided into target and background channels.For the target channel,the model introduces a concatenated modeling approach,while for the background channel,a parameter called the power control factor is introduced to assess impact of the target on the background channel,making the modeling framework applicable to both mono-static and bi-static sensing modes.To validate the proposed model’s effectiveness,measurements of target and background channels are conducted across a wide range of indoor and outdoor scenarios,covering various sensing targets such as metal plates,reconfigurable intelligent surfaces,human bodies,unmanned aerial vehicles,and vehicles.The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.
基金supported by National Key Research and Development Program of China(2022YFB2804603,2022YFB2804605)National Natural Science Foundation of China(U21B2033)+4 种基金Fundamental Research Funds forthe Central Universities(2023102001,2024202002)National Key Laborato-ry of Shock Wave and Detonation Physics(JCKYS2024212111)China Post-doctoral Science Fund(2023T160318)Open Research Fund of JiangsuKey Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX25_0695,SJCX25_0188)。
文摘Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pattern projection.However,the imaging speed of conventional fringe projection profilometry(FPP)remains limited by the native sensor refresh rates due to the inherent"one-to-one"synchronization mechanism between pattern projection and image acquisition in standard structured light techniques.Here,we present dual-frequency angular-multiplexed fringe projection profilometry(DFAMFPP),a deep learning-enabled 3D imaging technique that achieves high-speed,high-precision,and large-depth-range absolute 3D surface measurements at speeds 16 times faster than the sensor's native frame rate.By encoding multi-timeframe 3D information into a single multiplexed image using multiple pairs of dual-frequency fringes,high-accuracy absolute phase maps are reconstructed using specially trained two-stage number-theoretical-based deep neural networks.We validate the effectiveness of DFAMFPP through dynamic scene measurements,achieving 10,000 Hz 3D imaging of a running turbofan engine prototype with only a 625 Hz camera.By overcoming the sensor hardware bottleneck,DFAMFPP significantly advances high-speed and ultra-high-speed 3D imaging,opening new avenues for exploring dynamic processes across diverse scientific disciplines.
基金supported by the Science and Technology Program Project of Tianjin(No.24ZXZSSS00300).
文摘Grating fringe projection 3D measurement techniques are extensively applied in various fields.However,in high dynamic range scenarios with significant surface reflectivity variations,uneven greyscale distribution may lead to phase errors and poor reconstruction results.To address this problem,an adaptive fringe projection method is introduced.The method involves projecting two sets of dark and light fringes onto the object,enabling the full-field projection intensity map to be generated adaptively based on greyscale analysis.First,dark fringes are projected onto the object to extend exposure time as long as possible without causing overexposure in the image.Subsequently,bright fringes are projected under the same exposure settings to detect overexposed pixels,and the greyscale distribution of these overexposed points from the previous dark fringe projection is analyzed to calculate the corresponding projection intensities.Finally,absolute phase information from orthogonal fringes is used for coordinate matching,enabling the generation of adaptive projection fringe patterns.Experiments on various high dynamic range objects show that compared to conventional fringe projection binocular reconstruction method,the proposed algorithm achieves complete reconstruction of high dynamic range surfaces and shows robust performance against phase calculation errors caused by overexposure and low modulation.
