相干光正交频分复用系统定时同步的改进算法

分析了当前相干光正交频分复用(CO-OFDM)系统中Schmidl&Cox经典算法在光纤信道的定时同步,针对其产生平波现象和对光纤色散(CD)的缺点,提出了改进算法,并通过MATLAB在光纤信道中对两种算法进行了仿真,结果表明,在光纤信道下,改进方法的性能有远优于经典算法,因此改进方法更适合于光纤信道。     

基于压缩感知的同频干扰下LTE系统信道估计算法

本文提出一种基于压缩感知的同频干扰下长期演进系统信道估计方法。与现有方法不同,本文将干扰和噪声区别对待,利用干扰信号结构建立该系统下多小区信道估计模型(MCCE)。由于无线信道在宽带系统下表现出较为明显的稀疏特性,本文将压缩感知技术应用于上述模型,通过求解新的感知矩阵,并利用多输入多输出信道共有非零支撑集的特性,提出了适用于长期演进系统的联合改进子空间追踪算法(J-MSP),解决了上述模型下字典矩阵列相关度较高的问题;由于所提模型中含有未知的干扰符号,因此还需解决信道和干扰符号的联合估计。仿真结果和分析表明,本文方法在干扰与本小区同步时相比单小区信道估计方法性能显著提升,异步时与最大似然算法性能相当,同时也适用于无干扰场景。      

修正恒模算法分数间隔盲均衡

分数间隔均衡(FSE)能够降低对时间同步误差的敏感性,本文通过修正恒模算法(CMA)的误差函数,提出了一种分数间隔盲均衡修正恒模算法(MCMA).利用通信信号8PSK,对修正恒模算法和传统恒模算法进行了盲均衡性能比较,在噪声环境下的频率选择性信道中,MCMA比CMA具有更强的鲁棒性.模拟结果显示MCMA能够通过提高收敛速率、降低稳态均方误差和符号间干扰(ISI),有效地改进了信道均衡性能.     

基于信道分布空间的OFDM信道半盲估计方法

信道分布空间的构建将OFDM系统中发送信号的有限符号特性和信道响应的统计相关特性集中到信道分布平面中.算法使用了一个初始OFDM 导频符号在信道分布平面中建立信道频域响应参考曲线,信道的估计是利用相邻OFDM符号对应的信道响应有相关延续特性,通过在更新的信道分布平面上更新参考曲线来实现.计算机仿真表明,算法的性能和基于最小均方误差的信道估计算法相当,由于避免了矩阵求逆运算,该方法在工程实现中有较大的应用价值.     

用于无线快衰落信道的快速自适应估计新算法

提出了一种用于无线快衰落信道的快速自适应估计新算法--拆分基展开递推最小二乘法.该算法通过利用多径信道之间相互独立的特性,减少了传统基展开递推最小二乘法在相关运算过程中的冗余.实验结果表明,提出的新算法与传统的基展开递推最小二乘法比较,在得到同样信道估计精确度的条件下,算法的复杂度降低了约80%.     

基于LASSO算法的波束空间信道估计

在大规模毫米波(mmWave)天线阵列通信中,多输入多输出(Multiple Input Multiple Output,MIMO)系统可以使用透镜天线阵列大幅减少射频链的数量,但由于天线的数量远远大于射频链路的数量,信道估计具有挑战性。由于波束空间信道具有稀疏的特性,那么用于求解稀疏信号恢复问题的算法,可以作为波束空间信道估计问题的解决方法。波束空间信道估计问题建立的模型是基于l₀-范数的非凸性问题,该问题为NP-hard。通常用l₁-范数代替l₀-范数,将该问题转化为凸优化问题。该凸优化问题可以用传统的贪心算法方法进行求解。然而,这些贪心算法估计精度差。而且随着稀疏度的增加,计算复杂度也会增加。文章提出了最小角回归(Least Angle Regression,LARS)算法和改进的最小绝对收缩和选择算子(Least Absolute Shrinkage and Selection Operator,LASSO)算法,高效地解决了稀疏信号的恢复问题,即波束信道的估计。实验仿真结果表明,LARS算法和所提出的改进LAS...     

一种基于子空间跟踪的认知MIMO预编码算法

针对多用户认知MIMO下行系统,提出一种适用于慢衰落信道环境的预编码算法。该算法基于子空间跟踪思想,利用信道时域相关性,通过递推方式自适应更新预编码矩阵来跟踪和适应慢衰落信道环境的变化。分别从算法收敛速度、收敛精度、跟踪性能以及计算复杂度四个方面进行了理论推导和仿真分析。结果表明,所提算法可以有效的跟踪信道环境的动态变化,与传统算法相比,在保证系统性能的同时显著降低了处理复杂度。     

时变条件下MIMO-OFDM系统中的信道估计算法

本文设计了时变多径衰落条件下MIMO-OFDM系统中一种新的信道估计算法.该算法结合递归EM算法和Kalman预测对时变信道进行跟踪.借助软球形译码器(List Sphere Decoder,LSD)产生的搜索列表,递归EM算法序贯遍历搜索列表中可能的符号组合来估计各个子载波上的信道频率响应;基于获得的信道频率响应估计,Kalman预测器利用衰落信道的时域二阶统计特性进一步跟踪信道时变.仿真结果表明:本文设计的算法可以有效跟踪信道时变,性能优于传统的软输入Kalman滤波算法.     

OFDM系统中一种新的迭代信道估计算法

本文提出了OFDM系统中一种新的基于软信息迭代处理的信道估计算法。该算法将面向判决最小二乘估计算法和盲估计算法相结合,在估计器中构造了一种新的置信度量函数,根据解码和软映射重构的反馈信号置信度大小在两种估计算法中自适应选择,这样估计的信道频响可以有效提高软信息迭代接收性能,大大降低信道估计训练开销。仿真结果表明,本文提出的算法能有效跟踪信道时变,限制传统面向判决估计的错误传播,达到好的系统性能。     

基于子空间信道矩阵迭代的阶数估计算法

针对二阶统计量信道盲辨识算法中有效阶数估计问题,该文提出了一种基于子空间信道矩阵迭代的阶数估计(SS-CMR)算法.基于过估计条件下的Q矩阵特殊结构和其解空间向量可等价为真实信道冲激响应与公共零点信道的卷积,该算法首先利用子空间算法获得估计的信道矩阵,然后构建迭代形式的代价函数进行阶数估计.仿真表明,SS-CMR算法较CMR算法性能提升,且明显优于现有其他阶数估计算法;解析分析表明算法复杂度较CMR算法明显降低,对首尾系数很小的信道的阶数估计尤为有效.     

基于自适应粒子群优化的RBF毫米波信道建模研究

基于毫米波室内无线信道测量数据,将机器学习(machine learning,ML)中的径向基函数(radial basis function,RBF)方法应用于毫米波信道建模中,建立了基于自适应粒子群优化 (adaptive particle swarm optimization, APSO)的RBF神经网络信道参数预测模型,并与传统RBF算法的预测结果进行了比较. 利用APSO-RBF模型对信道大尺度参数（large-scale channel parameter,LSCP）如路径损耗(path loss, PL)、时延扩展(delay spread, DS)等数据的特征进行了学习和预测. 结果表明,APSO-RBF模型信道参数的预测值与实际测量值非常吻合,均方根误差(root-mean-square error,RMSE)较小,且预测曲线与原始测量值曲线的拟合度较好,该算法的学习性能和预测效果均优于传统RBF算法. 另外,APSO-RBF模型在数据量波动较大的情况下对信道参数的变化有着良好的适应性,对5G毫米波信道参数可以取得较好的预测效果.     

Schur type algorithms for spatial LS estimation with highlypipelined architectures

A family of Schur-type spatial least-squares algorithms is presented for solving the spatial LS estimation problem, in which the correlation matrix is neither Toeplitz nor near-Toeplitz, by order recursion. Normalized spatial Levinson- and Schur-type algorithms are also derived. Highly pipelined architectures are designed to realize these recursions. The reflection coefficients are first computed using the spatial Schur type recursions. Then, the forward and backward filter parameters are calculated by the spatial Levinson-type recursions. A pyramid systolic array is demonstrated to calculate not only the filter parameters but also the LDU decomposition of the inverse cross-correlation matrix at every clock phase. This pyramid array can be mapped onto a two-dimensional systolic array which has a simpler structure. A square systolic array is developed to implement the Levinson- and Schur-type temporal recursive LS (RLS) algorithms. A highly concurrent architecture which exploits the parallelism of the spatial Schur-type recursions is illustrated to perform the LDU decomposition of the cross-correlation matrix     

ARMA-cepstrum Recursion Algorithm for the Estimation of the MA Parameters of 2-D ARMA Models

This paper considers the problem of estimating the moving average (MA) parameters of a two-dimensional autoregressive moving average (2-D ARMA) model. To solve this problem, a new algorithm that is based on a recursion relating the ARMA parameters and cepstral coefficients of a 2-D ARMA process is proposed. On the basis of this recursion, a recursive equation is derived to estimate the MA parameters from the cepstral coefficients and the autoregressive (AR) parameters of a 2-D ARMA process. The cepstral coefficients are computed benefiting from the 2-D FFT technique. Estimation of the AR parameters is performed by the 2-D modified Yule–Walker (MYW) equation approach. The development presented here includes the formulation for real-valued homogeneous quarter-plane (QP) 2-D ARMA random fields, where data are propagated using only the past values. The proposed algorithm is computationally efficient especially for the higher-order 2-D ARMA models, and has the advantage that it does not require any matrix inversion for the calculation of the MA parameters. The performance of the new algorithm is illustrated by some numerical examples, and is compared with another existing 2-D MA parameter estimation procedure, according to three performance criteria. As a result of these comparisons, it is observed that the MA parameters and the 2-D ARMA power spectra estimated by using the proposed algorithm are converged to the original ones     

QRD-BASED MULTICHANNEL ADAPTIVE LATTICEALGORITHMS FOR THE PARAMETERIDENTIFICATION PROBLEM

A pair of multichannel recursive least squares (RLS) adaptive lattice algorithms based on the order recursive of lattice filters and the superior numerical properties of Givens algorithms is derived in this paper. The derivation of the first algorithm is based on QR decomposition of the input data matrix directly, and the Givens rotations approach is used to compute the QR decomposition. Using first a prerotation of the input data matrix and then a repetition of the single channel Givens lattice algorithm, the second algorithm can be obtained. Both algorithms have superior numerical properties, particularly the robustness to wordlength limitations. The parameter vector to be estimated can be extracted directly from internal variables in the present algorithms without a backsolve operation with an extra triangular array. The results of computer simulation of the parameter identification of a two-channel system are presented to confirm efficiently the derivation.     

Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering

This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective.     

变遗忘因子递推最小二乘Hammerstein系统辨识算法

摘.要:本论文研究了单输入单输出非线性Hammerstein系统的辨识问题,提出了一种具有变遗忘因子的递推最小二乘算法.由于Hammerstein系统模型的非线性特征,传统的递推最小二乘算法无法直接用来解决该系统的辨识问题.为此,论文将Hammerstein系统参数进行了映射变换,使得变换后的系统参数与Hammerst...     

Joint Doppler frequency, 2D-DOD and 2D-DOA estimation for bistatic MIMO radar in spatial coloured noise

In this paper, we address the problem of four-dimensional angle and Doppler frequency estimation for L-shaped bistatic multiple input multiple output radar in spatial coloured noise. A novel method of joint estimation of Doppler frequency, two-dimensional direction of departure and two-dimensional direction of arrival based on the propagator method is discussed. Utilising the cross-correlation matrix which is formed by the adjacent outputs of matched filter in the time domain, the special matrix is constructed to eliminate the influence of spatial coloured noise. The proposed algorithm provides lower computational complexity and has very close parameter estimation to the estimation of signal parameters via rotational invariance techniques algorithm and DOA-matrix algorithm in high signal-to-noise ratio and Cramér–Rao bound is given. Furthermore, multidimensional parameters can be automatically paired by this algorithm to avoid the performance degradation resulting from wrong pairing. Numerical simulation results demonstrate the effectiveness of the proposed method.     

基于被动测量的5G宽带高铁信道分簇模型

为研究在高铁场景下的5G信道传播模型,设计了一款基于双通道的被动信道测量系统,并对运行速度约250 km/h的高铁进行了Sub-6 GHz频段的信道测量,建立了基于多径簇的信道模型. 测量系统由双通道软件定义的无线电外设(software-defined radio, SDR)、高性能移动工作站和其他设备构成,可用于在服务中的5G新无线电(5G new radio, 5G-NR)网络中精确地采集中心频率为2~4 GHz、带宽为100 MHz的5G下行链路信号. 通过对不同类型下行链路信号的后处理,获得时延、多普勒频率和到达方向角的多维信道功率谱,以及多径分量的参数估计. 最后通过对本文测量获得的5G-NR模型与现有的WINNER II模型进行对比分析,验证了本文模型在系统性能评估中的适用性.     

3D MIMO信道建模与仿真验证

现有的多输入多输出（MIMO）信道仿真模型主要基于二维（2D）平面,不能反映实际的三维（3D）电波传播环境。在WINNER 2D模型的基础上研究建立3D MIMO信道仿真平台。加入空间垂直维后,天线方向图需要从2D扩展至3D,并对3D天线阵列构成的MIMO系统进行建模。利用实验数据对城市微小区场景进行仿真验证,可以看出三维MIMO信道参数仿真与实验结果符合较好。3D MIMO信道间的相关性要比2D大,但3D MIMO的信道容量相比2D会有比较大的提升。     

适于参数识别的多信道QRD自适应格型算法

本文基于格型滤波器的阶递归特性和Givens旋转算法的优越数值性能,推导了两种多信道递归最小二乘格型算法。第一种算法的推导是直接基于对输入数据矩阵进行正交-三角分解,并利用Givens旋转方法来计算其正交-三角分解。首先对输入数据矩阵进行预旋转,然后重复利用单信道Givens格型算法,便可得到第二种算法。两种算法都具有优越的数值性能,尤其是对有限字长的稳健性。待估计的滤波器参数矢量可根据算法的内部变量直接提取,而无需额外的三角阵进行后向代入求解运算。两信道参数识别的计算机模拟结果验证了本文的推导。