Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmissio...Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.展开更多
In this article,the empirical Bayes（EB）estimators are constructed for the estimable functions of the parameters in partitioned normal linear model.The superiorities of the EB estimators over ordinary least-squares...In this article,the empirical Bayes（EB）estimators are constructed for the estimable functions of the parameters in partitioned normal linear model.The superiorities of the EB estimators over ordinary least-squares（LS）estimator are investigated under mean square error matrix（MSEM）criterion.展开更多
针对现有的正交时频空(Orthogonal Time Frequency Space,OTFS)调制系统中最大比合并(Maximal Ratio Combining,MRC)均衡算法收敛速度慢、误码率高的问题,提出一种基于水声OTFS系统的分块线性最小均方误差的最大比合并(Maximal Ratio Co...针对现有的正交时频空(Orthogonal Time Frequency Space,OTFS)调制系统中最大比合并(Maximal Ratio Combining,MRC)均衡算法收敛速度慢、误码率高的问题,提出一种基于水声OTFS系统的分块线性最小均方误差的最大比合并(Maximal Ratio Combining Based on Block Linear Minimum Mean Square Error,BLMMSE-MRC)均衡算法。该算法基于水声信道的稀疏性,利用分块线性最小均方误差算法进行预处理,将输出结果作为MRC检测的初始估计值,然后在延迟多普勒空间中估计发射信号的多径分量,并利用MRC进行合并检测。实验结果表明,与已有零填充最大比合并算法(Maximal Ratio Combining Based on Zero Padding,ZP-MRC)和零填充块线性最小均方误差算法(Block Linear Minimum Mean Square Error Based on Zero Padding,ZP-BLMMSE)相比,所提算法能快速收敛,在10-4误码率条件下,信噪比提升了2 dB以上。展开更多
In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias es...In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias estimator. Some important properties are discussed. By appropriate choices of bias parameters, we construct many interested and useful biased linear estimators, which are the extension of ordinary biased linear estimators in the full_rank linear model to the deficient_rank linear model. At last, we give a numerical example in geodetic adjustment.展开更多
针对高速移动场景中正交时频空间(Orthogonal Time Frequency Space, OTFS)系统线性最小均方误差(Linear Minimum Mean Square Error, LMMSE)检测复杂度过高而难以快速有效实现的问题,利用零填充(Zero Padding, ZP)OTFS系统时域信道矩...针对高速移动场景中正交时频空间(Orthogonal Time Frequency Space, OTFS)系统线性最小均方误差(Linear Minimum Mean Square Error, LMMSE)检测复杂度过高而难以快速有效实现的问题,利用零填充(Zero Padding, ZP)OTFS系统时域信道矩阵呈块对角稀疏特性提出一种逐块迭代的对称逐次超松弛(Symmetric Successive over Relaxation, SSOR)迭代算法,在降低系统复杂度的同时获得与LMMSE检测近似的性能。仿真结果表明,与逐次超松弛(Successive over Relaxation, SOR)算法相比,所提算法对松弛参数不敏感且具有更快的收敛速度,在迭代次数为10次时误码性能几乎达到LMMSE误码性能,显著降低了检测器的复杂度。展开更多
基金supported by the 2011 China Aerospace Science and Technology Foundationthe Certain Ministry Foundation under Grant No.20212HK03010
文摘Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.
基金the Knowledge Innovation Program of the Chinese Academy of Sciences(KJCX3-SYW-S02)the Youth Foundation of USTC
文摘In this article,the empirical Bayes（EB）estimators are constructed for the estimable functions of the parameters in partitioned normal linear model.The superiorities of the EB estimators over ordinary least-squares（LS）estimator are investigated under mean square error matrix（MSEM）criterion.
文摘针对现有的正交时频空(Orthogonal Time Frequency Space,OTFS)调制系统中最大比合并(Maximal Ratio Combining,MRC)均衡算法收敛速度慢、误码率高的问题,提出一种基于水声OTFS系统的分块线性最小均方误差的最大比合并(Maximal Ratio Combining Based on Block Linear Minimum Mean Square Error,BLMMSE-MRC)均衡算法。该算法基于水声信道的稀疏性,利用分块线性最小均方误差算法进行预处理,将输出结果作为MRC检测的初始估计值,然后在延迟多普勒空间中估计发射信号的多径分量,并利用MRC进行合并检测。实验结果表明,与已有零填充最大比合并算法(Maximal Ratio Combining Based on Zero Padding,ZP-MRC)和零填充块线性最小均方误差算法(Block Linear Minimum Mean Square Error Based on Zero Padding,ZP-BLMMSE)相比,所提算法能快速收敛,在10-4误码率条件下,信噪比提升了2 dB以上。
文摘In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias estimator. Some important properties are discussed. By appropriate choices of bias parameters, we construct many interested and useful biased linear estimators, which are the extension of ordinary biased linear estimators in the full_rank linear model to the deficient_rank linear model. At last, we give a numerical example in geodetic adjustment.
文摘针对高速移动场景中正交时频空间(Orthogonal Time Frequency Space, OTFS)系统线性最小均方误差(Linear Minimum Mean Square Error, LMMSE)检测复杂度过高而难以快速有效实现的问题,利用零填充(Zero Padding, ZP)OTFS系统时域信道矩阵呈块对角稀疏特性提出一种逐块迭代的对称逐次超松弛(Symmetric Successive over Relaxation, SSOR)迭代算法,在降低系统复杂度的同时获得与LMMSE检测近似的性能。仿真结果表明,与逐次超松弛(Successive over Relaxation, SOR)算法相比,所提算法对松弛参数不敏感且具有更快的收敛速度,在迭代次数为10次时误码性能几乎达到LMMSE误码性能,显著降低了检测器的复杂度。
