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
This paper investigates optical transport in metamaterial waveguide arrays(MMWAs)exhibiting Bloch-like oscillations(BLOs).The MMWAs is fabricated by laterally combining metal and dielectric layers in a Fibonacci seque...This paper investigates optical transport in metamaterial waveguide arrays(MMWAs)exhibiting Bloch-like oscillations(BLOs).The MMWAs is fabricated by laterally combining metal and dielectric layers in a Fibonacci sequence.By mapping the field distribution of Gaussian wave packets in these arrays,we directly visualize the mechanical evolution in a classical wave environment.Three distinct oscillation modes are observed at different incident positions in the ninth-generation Fibonacci structure,without introducing thickness or refractive index gradient in any layer.Additionally,the propagation period of BLOs increases with a redshift of the incident wavelength for both ninth-and tenth-generation Fibonacci MMWAs.These findings provide a valuable method for manipulating BLOs and offer new insights into optical transport in metamaterials,with potential applications in optical device and wave control technologies.展开更多
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This paper investigates optical transport in metamaterial waveguide arrays(MMWAs)exhibiting Bloch-like oscillations(BLOs).The MMWAs is fabricated by laterally combining metal and dielectric layers in a Fibonacci sequence.By mapping the field distribution of Gaussian wave packets in these arrays,we directly visualize the mechanical evolution in a classical wave environment.Three distinct oscillation modes are observed at different incident positions in the ninth-generation Fibonacci structure,without introducing thickness or refractive index gradient in any layer.Additionally,the propagation period of BLOs increases with a redshift of the incident wavelength for both ninth-and tenth-generation Fibonacci MMWAs.These findings provide a valuable method for manipulating BLOs and offer new insights into optical transport in metamaterials,with potential applications in optical device and wave control technologies.
基金supported by the National Natural Science Foundation of China (51972170)the State Key Laboratory of MaterialsOriented Chemical Engineering (SKL-MCE-23A04)+3 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)the Jiangsu Specially-Appointed Professor Program. CHEN Zhi-gang thanks the financial support from the Australian Research Council, and QUT Capacity Building Professor Programsupport from the Chongqing Research Program of Basic Research and Frontier Technology (cstc2021jcyj-msxm X0641)the Doctoral “through train” scientific research project of Chongqing (CSTB2022BSXM-JCX0085).
