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
Codebooks have been indispensable for wireless communication standard since the first release of the Long-Term Evolution in 2009.They offer an efficient way to acquire the channel state information(CSI)for multiple an...Codebooks have been indispensable for wireless communication standard since the first release of the Long-Term Evolution in 2009.They offer an efficient way to acquire the channel state information(CSI)for multiple antenna systems.Nowadays,a codebook is not limited to a set of pre-defined precoders,it refers to a CSI feedback framework,which is more and more sophisticated.In this paper,we review the codebooks in 5G New Radio(NR)standards.The codebook timeline and the evolution trend are shown.Each codebook is elaborated with its motivation,the corresponding feedback mechanism,and the format of the precoding matrix indicator.Some insights are given to help grasp the underlying reasons and intuitions of these codebooks.Finally,we point out some unresolved challenges of the codebooks for future evolution of the standards.In general,this paper provides a comprehensive review of the codebooks in 5G NR and aims to help researchers understand the CSI feedback schemes from a standard and industrial perspective.展开更多
In this study,we comprehensively characterized and optimized a cryogenic pure CsI(pCsI)detector.We utilized a 2 cm×2 cm×2 cm cube crystal coupled with a HAMAMATSU R11065 photomultiplier tube,achieving a rema...In this study,we comprehensively characterized and optimized a cryogenic pure CsI(pCsI)detector.We utilized a 2 cm×2 cm×2 cm cube crystal coupled with a HAMAMATSU R11065 photomultiplier tube,achieving a remarkable light yield of 35.2 PE/ke V_(ee)and an unprecedented energy resolution of 6.9%at 59.54 ke V.Additionally,we measured the scintillation decay time of pCsI,which was significantly shorter than that of CsI(Na)at room temperature.Furthermore,we investigated the impact of temperature,surface treatment and crystal shape on light yield.Notably,the light yield peaked at approximately 20 K and remained stable within the range of 70–100 K.The light yield of the polished crystals was approximately 1.5 times greater than that of the ground crystals,whereas the crystal shape exhibited minimal influence on the light yield.These results are crucial for the design of the 10 kg pCsI detector for the future CLOVERS(coherent elastic neutrino(V)-nucleus scattering at China Spallation Neutron Source(CSNS))experiment.展开更多
为应对大规模多输入多输出(Multiple⁃input multiple⁃output,MIMO)系统中信道状态信息(Channel state information,CSI)反馈开销的日益增长,基于深度学习的CSI反馈网络(如Transformer网络)受到了广泛的关注,是一种非常有应用前景的智能...为应对大规模多输入多输出(Multiple⁃input multiple⁃output,MIMO)系统中信道状态信息(Channel state information,CSI)反馈开销的日益增长,基于深度学习的CSI反馈网络(如Transformer网络)受到了广泛的关注,是一种非常有应用前景的智能传输技术。为此,本文提出了一种基于数据聚类的CSI反馈Transformer网络的简化方法,采用基于聚类的近似矩阵乘法(Approximate matrix multiplication,AMM)技术,以降低反馈过程中Transformer网络的计算复杂度。本文主要对Transformer网络的全连接层计算(等效为矩阵乘法),应用乘积量化(Product quantization,PQ)和MADDNESS等简化方法,分析了它们对计算复杂度和系统性能的影响,并针对神经网络数据的特点进行了算法优化。仿真结果表明,在适当的参数调整下,基于MADDNESS方法的CSI反馈网络性能接近精确矩阵乘法方法,同时可大幅降低计算复杂度。展开更多
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 by the Fundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China under Grant 62071191
文摘Codebooks have been indispensable for wireless communication standard since the first release of the Long-Term Evolution in 2009.They offer an efficient way to acquire the channel state information(CSI)for multiple antenna systems.Nowadays,a codebook is not limited to a set of pre-defined precoders,it refers to a CSI feedback framework,which is more and more sophisticated.In this paper,we review the codebooks in 5G New Radio(NR)standards.The codebook timeline and the evolution trend are shown.Each codebook is elaborated with its motivation,the corresponding feedback mechanism,and the format of the precoding matrix indicator.Some insights are given to help grasp the underlying reasons and intuitions of these codebooks.Finally,we point out some unresolved challenges of the codebooks for future evolution of the standards.In general,this paper provides a comprehensive review of the codebooks in 5G NR and aims to help researchers understand the CSI feedback schemes from a standard and industrial perspective.
基金supported by the National Key R&D Program of China(No.2022YFA1602204)the National Natural Science Foundation of China(Nos.12175241,12221005)+2 种基金the Fundamental Research Funds for the Central Universitiesthe International Partnership Program of the Chinese Academy of Sciences(No.211134KYSB20200057)the Double First-Class University Project Foundation of USTC。
文摘In this study,we comprehensively characterized and optimized a cryogenic pure CsI(pCsI)detector.We utilized a 2 cm×2 cm×2 cm cube crystal coupled with a HAMAMATSU R11065 photomultiplier tube,achieving a remarkable light yield of 35.2 PE/ke V_(ee)and an unprecedented energy resolution of 6.9%at 59.54 ke V.Additionally,we measured the scintillation decay time of pCsI,which was significantly shorter than that of CsI(Na)at room temperature.Furthermore,we investigated the impact of temperature,surface treatment and crystal shape on light yield.Notably,the light yield peaked at approximately 20 K and remained stable within the range of 70–100 K.The light yield of the polished crystals was approximately 1.5 times greater than that of the ground crystals,whereas the crystal shape exhibited minimal influence on the light yield.These results are crucial for the design of the 10 kg pCsI detector for the future CLOVERS(coherent elastic neutrino(V)-nucleus scattering at China Spallation Neutron Source(CSNS))experiment.
