CES Transactions on Electrical Machines and Systems(CES TEMS)is an international quarterly journal,which is published by the China Electrotechnical Society(CES)and the Institute of Electrical Engineering of the Chines...CES Transactions on Electrical Machines and Systems(CES TEMS)is an international quarterly journal,which is published by the China Electrotechnical Society(CES)and the Institute of Electrical Engineering of the Chinese Academy of Sciences,and technically co-sponsored by IEEE Power Electronics Society(IEEE PELS).CES TEMS is indexed by ESCI,EI,Scopus,IEEE Xplore,INSPEC,DOAJ,CSTPCD,CSCD and Google Scholar etc..展开更多
针对IEEE 802.11p标准中导频数量有限,难以准确追踪车联万物(Vehicle-to-Everything,V2X)通信中时变信道的问题,学者们研究了数据导频辅助(Data Pilot Aided,DPA)信道估计方案。然而,这些经典DPA方案不能在完整的信噪比(Signal to Noise...针对IEEE 802.11p标准中导频数量有限,难以准确追踪车联万物(Vehicle-to-Everything,V2X)通信中时变信道的问题,学者们研究了数据导频辅助(Data Pilot Aided,DPA)信道估计方案。然而,这些经典DPA方案不能在完整的信噪比(Signal to Noise Ratio,SNR)范围内给出令人满意的效果,并且其估计结果的可靠性易受误差传播的影响。研究了一种新的信道估计方案,基于使用虚拟子载波的最小均方误差(Minimum Mean Square Error Using Virtual Pilots,MMSE-VP)方案,提出一种带有时间平均操作的改进MMSE(Improved MMSE,IMMSE)方案。IMMSE方案通过利用相邻正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)符号间信道的相关性来提高MMSE-VP方案在低SNR区域的性能,达到在整个SNR区域有良好表现的目的。联合深度学习技术,采用全连接神经网络(Fully Connected Neural Network,FCNN)作为IMMSE方案的非线性后处理模块,减少误差并获得更好的估计性能。在不同实验条件下的仿真结果表明,提出的信道估计方案可以适应调制方式和车辆速度的变化,能有效应对V2X通信中的信道估计问题。展开更多
文摘CES Transactions on Electrical Machines and Systems(CES TEMS)is an international quarterly journal,which is published by the China Electrotechnical Society(CES)and the Institute of Electrical Engineering of the Chinese Academy of Sciences,and technically co-sponsored by IEEE Power Electronics Society(IEEE PELS).CES TEMS is indexed by ESCI,EI,Scopus,IEEE Xplore,INSPEC,DOAJ,CSTPCD,CSCD and Google Scholar etc..
文摘针对IEEE 802.11p标准中导频数量有限,难以准确追踪车联万物(Vehicle-to-Everything,V2X)通信中时变信道的问题,学者们研究了数据导频辅助(Data Pilot Aided,DPA)信道估计方案。然而,这些经典DPA方案不能在完整的信噪比(Signal to Noise Ratio,SNR)范围内给出令人满意的效果,并且其估计结果的可靠性易受误差传播的影响。研究了一种新的信道估计方案,基于使用虚拟子载波的最小均方误差(Minimum Mean Square Error Using Virtual Pilots,MMSE-VP)方案,提出一种带有时间平均操作的改进MMSE(Improved MMSE,IMMSE)方案。IMMSE方案通过利用相邻正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)符号间信道的相关性来提高MMSE-VP方案在低SNR区域的性能,达到在整个SNR区域有良好表现的目的。联合深度学习技术,采用全连接神经网络(Fully Connected Neural Network,FCNN)作为IMMSE方案的非线性后处理模块,减少误差并获得更好的估计性能。在不同实验条件下的仿真结果表明,提出的信道估计方案可以适应调制方式和车辆速度的变化,能有效应对V2X通信中的信道估计问题。
