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
Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which ent...Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.展开更多
使用近红外光谱仪获取由高岭土、白云母和蒙脱石三种岩石矿物粉末混合成的模拟天然岩石样本的近红外漫反射光谱信息,通过标准归一化(standard normal variable)的方法对光谱数据进行预处理,采用随机森林(random forest)进行数学建模,对...使用近红外光谱仪获取由高岭土、白云母和蒙脱石三种岩石矿物粉末混合成的模拟天然岩石样本的近红外漫反射光谱信息,通过标准归一化(standard normal variable)的方法对光谱数据进行预处理,采用随机森林(random forest)进行数学建模,对岩石样本的组成成分进行预测,预测得到三种岩石成分最小均方根误差分别为:0.088 0,0.095 6,0.121 2。实验结果表明应用近红外漫反射光谱来测定天然岩石中各种矿物成分的含量是可行的,为今后岩石成分的快速检测提供了理论依据。展开更多
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.
基金supported by National Natural Science Foundation of China(62371225,62371227)。
文摘Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60475007)国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2006AA010102)。
