In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic su...In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.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
针对自适应正交频分复用(orthogonal frequency division multiplexing,OFDM)系统中线性最小均方误差(linear minimum mean square error,LMMSE)算法复杂度高、数据传输速率低问题,采用改进LMMSE算法对配电网信道状态进行了信道估计,通...针对自适应正交频分复用(orthogonal frequency division multiplexing,OFDM)系统中线性最小均方误差(linear minimum mean square error,LMMSE)算法复杂度高、数据传输速率低问题,采用改进LMMSE算法对配电网信道状态进行了信道估计,通过和LMMSE算法对比,该改进LMMSE的计算复杂度相比LMMSE算法已大大地降低,在配电网信道估计的时间上节省8 s,大大缩短了信道估计的时间。展开更多
为了降低正交频分复用OFDM(Orthogonal Frequency division Multiplexing)系统中最小均方误差MMSE(Minimum Mean Square Error)信道估计算法的复杂度,并且改善由于信道的统计特性与先验知识不匹配而导致的MMSE估计性能恶化,提出了一种...为了降低正交频分复用OFDM(Orthogonal Frequency division Multiplexing)系统中最小均方误差MMSE(Minimum Mean Square Error)信道估计算法的复杂度,并且改善由于信道的统计特性与先验知识不匹配而导致的MMSE估计性能恶化,提出了一种自适应的低秩信道估计算法.该算法利用信道的时间平均相关取代统计相关,结合了基于特征值分解的低秩建模,从而近似地实现MMSE估计.借助于子空间跟踪,该算法可以自适应地估计信道相关矩阵的主特征空间及噪声方差,以迭代的方式逼近最优的MMSE估计,而且复杂度较低.进一步分析指出基于信道延时子空间跟踪的估计算法是该算法的一种特例,理论分析和仿真结果均表明这种新算法在低信噪比时可以显著改善信道估计的准确性.展开更多
基金supported by the National Natural Science Foundation of China (6096200161071088)+2 种基金the Natural Science Foundation of Fujian Province of China (2012J05119)the Fundamental Research Funds for the Central Universities (11QZR02)the Research Fund of Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (21104)
文摘In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.
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。
文摘针对自适应正交频分复用(orthogonal frequency division multiplexing,OFDM)系统中线性最小均方误差(linear minimum mean square error,LMMSE)算法复杂度高、数据传输速率低问题,采用改进LMMSE算法对配电网信道状态进行了信道估计,通过和LMMSE算法对比,该改进LMMSE的计算复杂度相比LMMSE算法已大大地降低,在配电网信道估计的时间上节省8 s,大大缩短了信道估计的时间。
文摘为了降低正交频分复用OFDM(Orthogonal Frequency division Multiplexing)系统中最小均方误差MMSE(Minimum Mean Square Error)信道估计算法的复杂度,并且改善由于信道的统计特性与先验知识不匹配而导致的MMSE估计性能恶化,提出了一种自适应的低秩信道估计算法.该算法利用信道的时间平均相关取代统计相关,结合了基于特征值分解的低秩建模,从而近似地实现MMSE估计.借助于子空间跟踪,该算法可以自适应地估计信道相关矩阵的主特征空间及噪声方差,以迭代的方式逼近最优的MMSE估计,而且复杂度较低.进一步分析指出基于信道延时子空间跟踪的估计算法是该算法的一种特例,理论分析和仿真结果均表明这种新算法在低信噪比时可以显著改善信道估计的准确性.
