卫星下行链路因其开放性、广域覆盖性而面临严峻的窃听威胁,传统以加密技术为核心的卫星下行链路防窃听方案在计算复杂度与抗量子攻击能力上存在双重瓶颈,且现有卫星下行链路物理层安全防窃听方案的应用场景存在局限性。针对以上问题,...卫星下行链路因其开放性、广域覆盖性而面临严峻的窃听威胁,传统以加密技术为核心的卫星下行链路防窃听方案在计算复杂度与抗量子攻击能力上存在双重瓶颈,且现有卫星下行链路物理层安全防窃听方案的应用场景存在局限性。针对以上问题,通过基于动态扩展因子的扰码与编码级联设计,提出一种基于信道状态信息(Channel State Information,CSI)和协作中继的卫星下行链路防窃听方案。首先,通过部署地面中继基站,建立基于协作中继的卫星下行链路通信模型,扩大合法链路与窃听链路的CSI随机性差异;其次,通过合法链路CSI对准循环低密度奇偶校验码扩展因子进行动态调控,增加编码随机性,进而增加窃听者译码难度;最后,通过动态扩展因子与合法链路CSI在卫星端与用户端构建加扰与解扰机制,使窃听者因缺乏合法链路CSI而无法解扰保密信息。仿真结果表明,在用户端误码率低至10-6的情况下,利用扰码对CSI的依赖性构建窃听者解扰壁垒,可使窃听者误码率接近0.5。所提方案凭借对CSI与地面协作中继的协同设计,既具备抵御量子计算攻击的潜在能力,又契合卫星通信网络工程部署对高效低耗的需求,能够有效平衡卫星下行链路信息传输可靠性与安全性的矛盾,可为未来6G空天地一体化场景下的信息安全传输提供具备工程实践价值的技术参考路径。展开更多
Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditiona...Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.展开更多
针对车联网场景下多入多出-正交时频空(Multiple-Input Multiple-Output-Orthogonal Time Frequency Space,MIMO-OTFS)系统的信道状态信息(Channel State Information,CSI)反馈问题,提出了一种面向时延-多普勒(Delay-Dopler,DD)域CSI反...针对车联网场景下多入多出-正交时频空(Multiple-Input Multiple-Output-Orthogonal Time Frequency Space,MIMO-OTFS)系统的信道状态信息(Channel State Information,CSI)反馈问题,提出了一种面向时延-多普勒(Delay-Dopler,DD)域CSI反馈的时间差分架构Transformer网络(Time-differencing Architecture Delay-Doppler Transformer Network,TA-DD-TransNet),引入分时反馈机制,将残差信息建模与压缩反馈相结合。网络结构融合Transformer的全局建模能力与卷积神经网络的局部特征提取优势,在保持CSI重构精度的同时显著降低了反馈比特数与计算复杂度。在不同车速、信噪比及非完美信道估计条件下的仿真实验结果表明,所提方法在归一化均方误差(Normalized Mean Squared Error,NMSE)和余弦相似度指标上均优于CsiNet、CsiNet+和BCsiNet。在60 km/h、30 dB信噪比、1/4压缩率下,TA-DD-TransNet的NMSE约-27 dB,余弦相似度达0.96。复杂度分析显示,TA-DD-TransNet在1/4压缩率下的编码器和解码器浮点运算次数分别为1.809×10^(7)和2.281×10^(7),参数量均为8.4×10~6左右,显著低于CsiNet+。所提方法能满足车联网中对高可靠低时延通信的实际需求。展开更多
通过压缩信道状态信息(Channel Status Information,CSI)传输码字降低大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的CSI反馈开销,可以有效减少计算资源的使用和信息传输时间的消耗。针对如何使用轻量化模型准确估计...通过压缩信道状态信息(Channel Status Information,CSI)传输码字降低大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的CSI反馈开销,可以有效减少计算资源的使用和信息传输时间的消耗。针对如何使用轻量化模型准确估计低压缩比条件下CSI反馈的问题,通过设计的轻量化迭代交叉网络(Iterative Cross Network,ICNet)模型,在用户端使用设计的迭代压缩模块压缩CSI反馈,基站端使用设计的迭代重建模块估计CSI反馈,以较高的准确率和较低的时间消耗估计了低压缩比条件下的CSI反馈。在COST2100模型生成的数据样本下评估了ICNet在低压缩比条件下的鲁棒性,实验表明,在较小的1/64压缩比条件下,ICNet的归一化均方误差比次优值降低了8.48%,ICNet的参数量降低了35%左右。展开更多
针对卷积神经网络(convolutional neural network,CNN)计算复杂度高和内存占用大的问题,本文提出了一种基于轻量级CNN的信道状态信息(channel state information,CSI)图像指纹被动定位(LCNNLoc)方法.离线训练阶段,将幅值差矩阵和相位矩...针对卷积神经网络(convolutional neural network,CNN)计算复杂度高和内存占用大的问题,本文提出了一种基于轻量级CNN的信道状态信息(channel state information,CSI)图像指纹被动定位(LCNNLoc)方法.离线训练阶段,将幅值差矩阵和相位矩阵构造成类似于“RGB”的三通道特征图像;同时设计了一个轻量级CNN架构,利用特征图像作为该框架的输入进行训练,在训练结束时将CNN模型保存为指纹数据库.在线定位阶段,采用概率加权质心方法实现了实时的位置估计.实验结果表明,相较于传统方法,LCNNLoc不仅提升了定位精度,还降低了算法运行耗时.展开更多
为满足不同应用场景对环境γ辐射监测的需求,完善区域监测产品体系,本文开展了一种基于CsI(Tl)闪烁体的γ射线环境剂量当量监测仪的研制工作,基于结构紧凑、成本可控、响应稳定等设计目标实现了探测器的原理与结构设计及原理样机制造。...为满足不同应用场景对环境γ辐射监测的需求,完善区域监测产品体系,本文开展了一种基于CsI(Tl)闪烁体的γ射线环境剂量当量监测仪的研制工作,基于结构紧凑、成本可控、响应稳定等设计目标实现了探测器的原理与结构设计及原理样机制造。使用蒙特卡洛粒子输运程序(Monte Carlo N-Particle Transport Code,MCNP)构建高精度的仿真模型,分别在实验与模拟条件下使用标准γ辐射场开展剂量响应测试与仿真,对比验证了该仿真模型的可靠性(灵敏度误差小于5%)。针对CsI(Tl)闪烁体本征能谱响应的非线性问题,基于脉冲幅度分段赋权法开发了能量响应补偿技术,经该技术补偿后变异系数小于6%,能量响应晃动小于12%,有效提升了剂量当量测量的准确性。结果表明,本研究方法可在显著改善CsI(Tl)探测器的能量依赖性的同时无需进行能谱展开,为其他类型剂量监测设备提供一种低成本、高可靠性的校准技术路径。展开更多
Artificial intelligence(AI)is pivotal in advancing fifth-generation(5G)-Advanced and sixthgeneration systems,capturing substantial research interest.Both the 3rd Generation Partnership Project(3GPP)and leading corpora...Artificial intelligence(AI)is pivotal in advancing fifth-generation(5G)-Advanced and sixthgeneration systems,capturing substantial research interest.Both the 3rd Generation Partnership Project(3GPP)and leading corporations champion AI’s standardization in wireless communication.This piece delves into AI’s role in channel state information(CSI)prediction,a sub-use case acknowledged in 5GAdvanced by the 3GPP.We offer an exhaustive survey of AI-driven CSI prediction,highlighting crucial elements like accuracy,generalization,and complexity.Further,we touch on the practical side of model management,encompassing training,monitoring,and data gathering.Moreover,we explore prospects for CSI prediction in future wireless communication systems,entailing integrated design with feedback,multitasking synergy,and predictions in rapid scenarios.This article seeks to be a touchstone for subsequent research in this burgeoning domain.展开更多
With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural net...With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural network(CNN)based on channel state information(CSI)images,which contains more feature information by constituting a new CSI image with amplitude and angle of arrival information of CSI information collected at known points.Moreover,the global mean filtering(GMC)algorithm with median filtering proposed in this paper is used to filter and reduce the noise of CSI images to obtain clearer images for network training.To extract more features from the CSI images,the traditional single-channel network is extended,and a two-channel design is introduced to extract feature information between adjacent subcarriers.Experimental evaluation is performed in a typical indoor environment,and the proposed method is experimentally proven to have good localization performance.展开更多
文摘卫星下行链路因其开放性、广域覆盖性而面临严峻的窃听威胁,传统以加密技术为核心的卫星下行链路防窃听方案在计算复杂度与抗量子攻击能力上存在双重瓶颈,且现有卫星下行链路物理层安全防窃听方案的应用场景存在局限性。针对以上问题,通过基于动态扩展因子的扰码与编码级联设计,提出一种基于信道状态信息(Channel State Information,CSI)和协作中继的卫星下行链路防窃听方案。首先,通过部署地面中继基站,建立基于协作中继的卫星下行链路通信模型,扩大合法链路与窃听链路的CSI随机性差异;其次,通过合法链路CSI对准循环低密度奇偶校验码扩展因子进行动态调控,增加编码随机性,进而增加窃听者译码难度;最后,通过动态扩展因子与合法链路CSI在卫星端与用户端构建加扰与解扰机制,使窃听者因缺乏合法链路CSI而无法解扰保密信息。仿真结果表明,在用户端误码率低至10-6的情况下,利用扰码对CSI的依赖性构建窃听者解扰壁垒,可使窃听者误码率接近0.5。所提方案凭借对CSI与地面协作中继的协同设计,既具备抵御量子计算攻击的潜在能力,又契合卫星通信网络工程部署对高效低耗的需求,能够有效平衡卫星下行链路信息传输可靠性与安全性的矛盾,可为未来6G空天地一体化场景下的信息安全传输提供具备工程实践价值的技术参考路径。
基金supported in part by the Natural Science Foundation of China under Grant Nos.U2468201 and 62221001ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240420002。
文摘Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.
文摘针对车联网场景下多入多出-正交时频空(Multiple-Input Multiple-Output-Orthogonal Time Frequency Space,MIMO-OTFS)系统的信道状态信息(Channel State Information,CSI)反馈问题,提出了一种面向时延-多普勒(Delay-Dopler,DD)域CSI反馈的时间差分架构Transformer网络(Time-differencing Architecture Delay-Doppler Transformer Network,TA-DD-TransNet),引入分时反馈机制,将残差信息建模与压缩反馈相结合。网络结构融合Transformer的全局建模能力与卷积神经网络的局部特征提取优势,在保持CSI重构精度的同时显著降低了反馈比特数与计算复杂度。在不同车速、信噪比及非完美信道估计条件下的仿真实验结果表明,所提方法在归一化均方误差(Normalized Mean Squared Error,NMSE)和余弦相似度指标上均优于CsiNet、CsiNet+和BCsiNet。在60 km/h、30 dB信噪比、1/4压缩率下,TA-DD-TransNet的NMSE约-27 dB,余弦相似度达0.96。复杂度分析显示,TA-DD-TransNet在1/4压缩率下的编码器和解码器浮点运算次数分别为1.809×10^(7)和2.281×10^(7),参数量均为8.4×10~6左右,显著低于CsiNet+。所提方法能满足车联网中对高可靠低时延通信的实际需求。
文摘针对卷积神经网络(convolutional neural network,CNN)计算复杂度高和内存占用大的问题,本文提出了一种基于轻量级CNN的信道状态信息(channel state information,CSI)图像指纹被动定位(LCNNLoc)方法.离线训练阶段,将幅值差矩阵和相位矩阵构造成类似于“RGB”的三通道特征图像;同时设计了一个轻量级CNN架构,利用特征图像作为该框架的输入进行训练,在训练结束时将CNN模型保存为指纹数据库.在线定位阶段,采用概率加权质心方法实现了实时的位置估计.实验结果表明,相较于传统方法,LCNNLoc不仅提升了定位精度,还降低了算法运行耗时.
文摘为满足不同应用场景对环境γ辐射监测的需求,完善区域监测产品体系,本文开展了一种基于CsI(Tl)闪烁体的γ射线环境剂量当量监测仪的研制工作,基于结构紧凑、成本可控、响应稳定等设计目标实现了探测器的原理与结构设计及原理样机制造。使用蒙特卡洛粒子输运程序(Monte Carlo N-Particle Transport Code,MCNP)构建高精度的仿真模型,分别在实验与模拟条件下使用标准γ辐射场开展剂量响应测试与仿真,对比验证了该仿真模型的可靠性(灵敏度误差小于5%)。针对CsI(Tl)闪烁体本征能谱响应的非线性问题,基于脉冲幅度分段赋权法开发了能量响应补偿技术,经该技术补偿后变异系数小于6%,能量响应晃动小于12%,有效提升了剂量当量测量的准确性。结果表明,本研究方法可在显著改善CsI(Tl)探测器的能量依赖性的同时无需进行能谱展开,为其他类型剂量监测设备提供一种低成本、高可靠性的校准技术路径。
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62261160576the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022-1 and BE2023022+3 种基金the Fundamental Research Funds for the Central Universities under Grant 2242023K5003The work was also supported in part by the NSFC under Grant 62401640in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515110732This work of Zhang Jun was supported partly by the Hong Kong Research Grants Council,Hong Kong,China under the NSFC/RGC Collaborative Research Scheme grant CRS HKUST603/22.
文摘Artificial intelligence(AI)is pivotal in advancing fifth-generation(5G)-Advanced and sixthgeneration systems,capturing substantial research interest.Both the 3rd Generation Partnership Project(3GPP)and leading corporations champion AI’s standardization in wireless communication.This piece delves into AI’s role in channel state information(CSI)prediction,a sub-use case acknowledged in 5GAdvanced by the 3GPP.We offer an exhaustive survey of AI-driven CSI prediction,highlighting crucial elements like accuracy,generalization,and complexity.Further,we touch on the practical side of model management,encompassing training,monitoring,and data gathering.Moreover,we explore prospects for CSI prediction in future wireless communication systems,entailing integrated design with feedback,multitasking synergy,and predictions in rapid scenarios.This article seeks to be a touchstone for subsequent research in this burgeoning domain.
基金supported by Natural Science Foundation of Hunan Province under Grant(NO:2021JJ31142).
文摘With the increasing demand for indoor localization,indoor location based on Wi-Fi has gained wide attention due to its convenience of access.In this paper,we propose a new multi-feature fusion convolutional neural network(CNN)based on channel state information(CSI)images,which contains more feature information by constituting a new CSI image with amplitude and angle of arrival information of CSI information collected at known points.Moreover,the global mean filtering(GMC)algorithm with median filtering proposed in this paper is used to filter and reduce the noise of CSI images to obtain clearer images for network training.To extract more features from the CSI images,the traditional single-channel network is extended,and a two-channel design is introduced to extract feature information between adjacent subcarriers.Experimental evaluation is performed in a typical indoor environment,and the proposed method is experimentally proven to have good localization performance.
