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
Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matri...Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.展开更多
针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系...针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系统信号检测算法(LU-IMMSE)。该算法依据时延多普勒域稀疏信道矩阵的特征,采用一种低复杂度的LU分解方法,以避免MMSE均衡器求解矩阵逆的过程,在保证均衡器性能的前提下降低了均衡器复杂度。在OTFS系统中引入一种IMMSE均衡器,通过不断迭代更新发送符号均值和方差这些先验信息来逼近MMSE均衡器最优估计值。LU-IMMSE算法通过调节迭代次数可以有效降低误比特率。在比特信噪比为8 dB时,5次迭代后的LU-IMMSE均衡器误比特率相比传统的MMSE均衡器降低了约11 dB。随着迭代次数的增大,较传统IMMSE算法降低了计算复杂度。在最大时延系数为4、符号数为16的情况下,与直接求逆相比,所提出的低复杂度LU分解方法降低了约91.72%的矩阵求逆计算复杂度。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
最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩...最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩阵分块思想和理查德森(Richardson,RI)算法,提出了一种预处理的理查德森(Pretreatment-Richardson,P-RI)迭代算法,该算法首先基于矩阵分块思想构造了一种新形式的线性迭代,然后用此线性迭代对理查德森算法进行预处理,有效提升了算法的收敛速度。实验结果显示,与现有的RI算法相比,该算法的检测性能更好。展开更多
We propose a cavity length demodulation method that combines virtual reference interferometry（VRI） and minimum mean square error（MMSE） algorithm for fiber-optic Fabry–Perot（F-P） sensors. In contrast to the conv...We propose a cavity length demodulation method that combines virtual reference interferometry（VRI） and minimum mean square error（MMSE） algorithm for fiber-optic Fabry–Perot（F-P） sensors. In contrast to the conventional demodulating method that uses fast Fourier transform（FFT） for cavity length estimation,our method employs the VRI technique to obtain a raw cavity length, which is further refined by the MMSE algorithm. As an experimental demonstration, a fiber-optic F-P sensor based on a sapphire wafer is fabricated for temperature sensing. The VRI-MMSE method is employed to interrogate cavity lengths of the sensor under different temperatures ranging from 28°C to 1000°C. It eliminates the ＂mode jumping＂ problem in the FFT-MMSE method and obtains a precision of 4.8 nm, corresponding to a temperature resolution of 2.0°C over a range of 1000°C. The experimental results reveal that the proposed method provides a promising, high precision alternative for demodulating fiber-optic F-P sensors.展开更多
The turbo equalization approach is studied for Orthogonal Frequency Division Multiplexing （OFDM） system with combined error control coding and linear precoding. While previous literatures employed linear precodcr of...The turbo equalization approach is studied for Orthogonal Frequency Division Multiplexing （OFDM） system with combined error control coding and linear precoding. While previous literatures employed linear precodcr of small size for complexity reasons, this paper proposes to use a linear precoder of size larger than or equal to the maximum length of the equivalent discrete-time channel in order to achieve full frequency diversity and reduce complexities of the error control coder/decoder. Also a low complexity Linear Minimum Mean Square Error （LMMSE） turbo equalizer is derived for the receiver. Through simulation and performance analysis, it is shown that the performance of the proposed scheme over frequency selective fading channel reaches the matched filter bound; compared with the same coded OFDM without linear precoding, the proposed scheme shows an Signal-to-Noise Ratio （SNR） improvement of at least 6dB at a bit error rate of 10 6 over a multipath channel with exponential power delay profile. Convergence behavior of the proposed scheme with turbo equalization using various type of linear precoder/transformer, various interleaver size and error control coder of various constraint length is also investigated.展开更多
工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小...工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.展开更多
针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中最小均方误差(Minimum Mean Squared Error,MMSE)信道估计算法误码率(BER)高的问题,提出一种平均最小均方误差(Averaged-Minimum Mean Squared Error,A-MMSE)...针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中最小均方误差(Minimum Mean Squared Error,MMSE)信道估计算法误码率(BER)高的问题,提出一种平均最小均方误差(Averaged-Minimum Mean Squared Error,A-MMSE)信道估计算法。该算法首先基于802.11n标准而构造了一种新的导频结构,收发两端分别进行降采样和过采样处理,利用已知训练序列和导频获得信道频域响应。仿真结果表明,所提出的A-MMSE信道估计算法与传统的MMSE算法相比,在BER为10^(-3)时,信噪比改善了约8dB。因而所提出的信道估计算法能明显改善系统的BER性能。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This work is supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX18_0467)Jiangsu Province,China.During the revision of this paper,the author is supported by China Scholarship Council(No.201906840021)China to continue some research related to data processing.
文摘Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.
文摘针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系统信号检测算法(LU-IMMSE)。该算法依据时延多普勒域稀疏信道矩阵的特征,采用一种低复杂度的LU分解方法,以避免MMSE均衡器求解矩阵逆的过程,在保证均衡器性能的前提下降低了均衡器复杂度。在OTFS系统中引入一种IMMSE均衡器,通过不断迭代更新发送符号均值和方差这些先验信息来逼近MMSE均衡器最优估计值。LU-IMMSE算法通过调节迭代次数可以有效降低误比特率。在比特信噪比为8 dB时,5次迭代后的LU-IMMSE均衡器误比特率相比传统的MMSE均衡器降低了约11 dB。随着迭代次数的增大,较传统IMMSE算法降低了计算复杂度。在最大时延系数为4、符号数为16的情况下,与直接求逆相比,所提出的低复杂度LU分解方法降低了约91.72%的矩阵求逆计算复杂度。
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。
文摘最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩阵分块思想和理查德森(Richardson,RI)算法,提出了一种预处理的理查德森(Pretreatment-Richardson,P-RI)迭代算法,该算法首先基于矩阵分块思想构造了一种新形式的线性迭代,然后用此线性迭代对理查德森算法进行预处理,有效提升了算法的收敛速度。实验结果显示,与现有的RI算法相比,该算法的检测性能更好。
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.61377091 and61505152)the Pre-research Field Foundation of China(No.6140243010116QT69001)the Applied Basic Research Program of Wuhan,China(No.2017010201010102)
文摘We propose a cavity length demodulation method that combines virtual reference interferometry（VRI） and minimum mean square error（MMSE） algorithm for fiber-optic Fabry–Perot（F-P） sensors. In contrast to the conventional demodulating method that uses fast Fourier transform（FFT） for cavity length estimation,our method employs the VRI technique to obtain a raw cavity length, which is further refined by the MMSE algorithm. As an experimental demonstration, a fiber-optic F-P sensor based on a sapphire wafer is fabricated for temperature sensing. The VRI-MMSE method is employed to interrogate cavity lengths of the sensor under different temperatures ranging from 28°C to 1000°C. It eliminates the ＂mode jumping＂ problem in the FFT-MMSE method and obtains a precision of 4.8 nm, corresponding to a temperature resolution of 2.0°C over a range of 1000°C. The experimental results reveal that the proposed method provides a promising, high precision alternative for demodulating fiber-optic F-P sensors.
基金Supported by the National High Technology ResearchDevelopment Program of China (863 Program)(No.2001AA 123014)
文摘The turbo equalization approach is studied for Orthogonal Frequency Division Multiplexing （OFDM） system with combined error control coding and linear precoding. While previous literatures employed linear precodcr of small size for complexity reasons, this paper proposes to use a linear precoder of size larger than or equal to the maximum length of the equivalent discrete-time channel in order to achieve full frequency diversity and reduce complexities of the error control coder/decoder. Also a low complexity Linear Minimum Mean Square Error （LMMSE） turbo equalizer is derived for the receiver. Through simulation and performance analysis, it is shown that the performance of the proposed scheme over frequency selective fading channel reaches the matched filter bound; compared with the same coded OFDM without linear precoding, the proposed scheme shows an Signal-to-Noise Ratio （SNR） improvement of at least 6dB at a bit error rate of 10 6 over a multipath channel with exponential power delay profile. Convergence behavior of the proposed scheme with turbo equalization using various type of linear precoder/transformer, various interleaver size and error control coder of various constraint length is also investigated.
文摘工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.
文摘针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中最小均方误差(Minimum Mean Squared Error,MMSE)信道估计算法误码率(BER)高的问题,提出一种平均最小均方误差(Averaged-Minimum Mean Squared Error,A-MMSE)信道估计算法。该算法首先基于802.11n标准而构造了一种新的导频结构,收发两端分别进行降采样和过采样处理,利用已知训练序列和导频获得信道频域响应。仿真结果表明,所提出的A-MMSE信道估计算法与传统的MMSE算法相比,在BER为10^(-3)时,信噪比改善了约8dB。因而所提出的信道估计算法能明显改善系统的BER性能。
文摘针对无线传感网络中传统DV-Hop(Distance Vector Hop)定位算法节点分布不均匀导致定位误差较大的问题,提出了非均匀网络中半径可调的ARDV-Hop(Adjustable Radius DV-Hop in Non-uniform Networks)定位算法。该算法通过半径可调的方式对节点间的跳数进行细化,用细化后呈小数级的跳数代替传统的整数级跳数,并建立了数据能量消耗模型,优化了网络传输性能。ARDV-Hop算法还针对节点分布不均匀的区域提出跳距优化算法:在节点密度大的区域,采用余弦定理优化跳距;密度小的区域,采用最小均方误差(Least Mean Square,LMS)来修正跳距。仿真实验表明,在同等网络环境下,与传统DV-Hop算法、GDV-Hop算法和WOA-DV-Hop算法相比,ARDV-Hop算法能更有效地降低定位误差.
