基于子空间的空时分组码的解码

空时码解码算法往往假设信道状态信息(CSI)已经事先估计出,信道估计需要额外的运算量,而且信道估计误差使解码性能降低.本文提出一种基于子空间方法的空时分组码直接解码算法,利用空时分组码的结构,结合子空间算法,可以不需要信道信息,直接得到码符信息的闭式解.仿真实验结果表明,随着噪声子空间求解准确度的提高,直接解码算法性能逐渐接近使用确切信道信息的相关解码器.     

多输入多输出系统中空时分组码的盲信道估计

在空时系统设计中，信道估计是一个关键技术。对一种基于空时分组码的盲信道估计算法进行了研究。在信道输出端进行子空间分解，如果信号子空间是由信道唯一决定的，那么信道参数可被唯一估计为一个常数。提出的算法是基于空时分组码的普通形式，因此可被应用于任何分组码。最后，仿真结果显示了一些特定码字的估计效果。     

基于QR分解的MIMO信道盲辨识和盲均衡方法

针对SIMO信道的经典盲估计方法,如子空间法(SS)等,都是基于接收端样本自相关阵的特征值分解(EVD)或奇异值分解(SVD)来实现信道估计的,而基于QR分解的SIMO信道盲辨识方法是最近提出的一种性能优良的新算法.本文将该算法推广为MIMO信道盲辨识算法,并且证明了在一定的假设下,即使各路源信号为空间相关且其统计特性未知时,该算法仍然保持有效.实验结果表明这种MIMO辨识算法具有收敛速度快、计算量小、无须对噪声做额外的处理、对噪声不敏感等优点.我们还将这种算法与经典的MIMO辨识算法进行了性能比较.     

MIMO系统中基于干扰子空间投影的盲空时多用户检测算法

该文针对多天线码分多址系统,提出了一种盲空时多用户检测算法。该算法结合空域、时域二维信息,利用Alamouti空时分组码与MIMO信道的结构特征,通过跟踪干扰子空间对多天线信道进行估计。在此基础上,通过将接收信号投影到干扰子空间的正交补空间上,实现了抑制多址干扰的空时多用户检测。该算法不仅能够自适应地跟踪信道的变化,且具有较低的算法复杂度。仿真结果表明该算法能够有效地抑制多天线码分多址系统中的多址干扰,改善系统的性能。     

MIMO系统中一种改进的盲MMSE空时多用户检测算法

针对MIMO系统,提出了一种改进的基于子空间的盲MMSE空时多用户检测算法。该算法结合MIMO系统的空间分集技术与Alamouti空时分组码方案,预估计MIMO信道信息并对信号子空间进行预处理,使用正交性能和稳态性能较好的NOOja算法跟踪信号子空间,在自适应过程中对特征值矩阵进行优化,去除迭代带来的噪声,解决了跟踪过程中信号特征值矩阵的近似估计会带来检测器性能恶化的问题。仿真结果表明这种算法,能有效地抑制多址干扰,抗远近效应能力强,尤其在低信噪比、远近效应明显的恶劣环境下,有稳定良好的性能表现。      

一种正交空时分组码盲识别方法

由于正交空时分组码(OSTBC)编码矩阵具有正交特性,可得正交空时分组码的虚拟信道矩阵的相关矩阵为对角矩阵。根据此原理,文中提出相关矩阵的非主对角元素能量与主对角元素能量之比的特征参数和基于此特征参数的正交空时分组码识别方法;同时利用最小描述长度(MDL)准则估计出信号子空间维数,即每组发射信号所含符号数。仿真结果表明,在不同发射天线数和接收天线数下,所提出的识别方法都具有较好的识别性能;符号数的估计性能与正确识别概率具有相同的变化规律。     

STBC MC-DS-CDMA系统基于子空间信道盲估计的多用户检测

该文提出了一种空时分组码(STBC)多载波(MC)DS-CDMA(STBC MC-DS-CDMA)系统结构,并通过在STBC MC-DS-CDMA下构造的统一的信号模型,实现了系统上行链路基于子空间的信道盲估计,仿真结果表明了方法行之有效。基于信道估计的结果进一步实现了系统最小均方差(MMSE)多用户检测,使系统的BER性能得到很大提高。     

频率选择性衰落信道下一种空时分组码的直接解码

该文针对 3个发射天线,1个接收天线的空时分组码系统,提出了频率选择性衰落信道下,无需信道估计,直接对空时分组码进行解码的方法,把子空间方法应用于空时编码当中,从信号处理和空时编码两个方面考虑空时分组码的直接解码问题,利用空时分组码所特有的正交设计,较为方便地从子空间中解出信号信息,从单载波的角度,解决了频率选择性衰落下空时分组码的解码问题。Monte-Carlo仿真给出了直接解码算法的性能,并与使用准确信道信息的解码算法做了性能比较。     

基于高阶累积量的正交空时分组码盲识别

针对多输入多输出(Multiple-Input-Multiple-Output,MIMO)系统中的空时码盲识别问题,提出一种基于高阶累积量的正交空时分组码(Orthogonal Space-Time Block Code,OSTBC)盲识别方法.推导给出了接收信号的两种高阶累积量公式,该高阶累积量包含发射信号信息,由此提出识别OSTBC信号的两个特征参数;利用MIMO信道估计得到空间信道矩阵,并提出了两种特征参数的估计方法;最后,利用最小距离准则实现对OSTBC信号的分类识别.仿真结果表明:所提出方法的正确识别率高于已有的识别方法,具有良好的识别性能.     

异步DS-CDMA系统盲空时信道估计及多用户检测

该文提出了适用于频率选择性瑞利衰落信道中的异步DS-CDMA系统盲空时信道估计及多用户检测算法。通过研究多径信号码空间和数据矢量空间,采用噪声子空间技术进行异步DS-CDMA系统盲空时信道参数估计,同时利用了多径传播和接收机同步失调的特性,以利于把盲线性滤波优化技术应用于稳健的干扰抑制。使用一种修改的ULV更新算法进行噪声子空间跟踪,该算法不需要相关矩阵的秩估计,直接估计噪声子空间,不进行信号子空间跟踪。并且研究了线性约束最小方差(LCMV)盲空时多用户检测及其基于Householder变换约束最小均方算法(HCLMS)的自适应实现。仿真结果验证了该文算法的有效性。     

QR factorization based blind channel identification withsecond-order statistics

Most eigenstructure-based blind channel identification and equalization algorithms with second-order statistics need SVD or EVD of the correlation matrix of the received signal. In this paper, we address new algorithms based on QR factorization of the received signal directly without calculating the correlation matrix. This renders the QR factorization-based algorithms more robust against ill-conditioned channels, i.e., those channels with almost common zeros among the subchannels. First, we present a block algorithm that performs the QR factorization of the received data matrix as a whole. Then, a recursive algorithm is developed based on the QR factorization by updating a rank-revealing ULV decomposition. Compared with existing algorithms in the same category, our algorithms are computationally more efficient. The computation in each recursion of the recursive algorithm is on the order of O(m²) if only equalization is required, where m is the dimension of the received signal vector. Our recursive algorithm preserves the fast convergence property of the subspace algorithms, thus converging faster than other adaptive algorithms such as the super-exponential algorithm with comparable computational complexities. Moreover, our proposed algorithms do not require noise variance estimation. Numerical simulations demonstrate the good performance of the proposed algorithms     

一种低复杂度的空时分组码检测算法

空时分组码能够提供分集和编码增益,但所用的ML译码算法计算量太大,不能满足高速实时通信系统的要求。为了有效地降低译码复杂度,提出了一种新的检测算法,该算法通过对系统模型进行变换,利用等效的信道响应矩阵的QR分解来进行译码,从而避免了对所有的调制信号进行穷举搜索的过程,使计算复杂度有了很大降低,而且新算法的误码率性能与ML算法近乎相同。最后,通过仿真验证了所提算法的有效性和实用性。     

基于高阶统计特征的空时分组码盲识别方法

空时分组码的盲识别是认知无线电领域一个新的重要问题。多数现有算法在多接收天线下进行识别,然而这些算法并不完全适用于单接收天线条件。针对上述问题,该文提出一种同时适用于单接收天线和多接收天线的空时分组码盲识别方法。利用空时分组码矩阵内元素的相关性,提出四阶统计量作为盲识别的特征参数,并通过最小欧氏距离的方式检验四阶统计量的差异,达到识别的目的。蒙特卡洛仿真表明,算法识别性能较好,且不需要预先知道信道信息、噪声信息和调制信息,对多普勒频移和相位噪声具有一定的适应性。     

Robust blind channel estimation algorithm for linear STBC systems using fourth order cumulant matrices

A novel blind channel estimation algorithm, based on fourth-order cumulant matrices, is proposed and applied to linear Space–Time Block Coded (STBC) for Multiple Input Multiple Output systems. Contrary to subspace and Second-Order Statistics (SOS) methods, the presented approach estimates the channel matrix without any modification of the transmitter. It takes advantage of the statistical independence of the signals in front of the space–time encoding. In this paper, the presented algorithm estimates the channel matrix by minimizing a cost function based on the higher cumulant matrices after Zero-Forcing equalization to mitigate the computational complexity and improve the performance. We employ the proposed method to the STBC systems including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBC systems. Symbol error rate and Normalized Mean Square Error simulations of the proposed algorithm are shown for a different number of users, signal to noise ratios and different number of symbols per user in comparison with subspace and Second-Order Statistics (SOS) methods. The results show that the presented method performs well and outperforms other methods in estimating the channel matrix from the received data. Moreover, the proposed method presents high convergence speed in estimating the channel matrix.     

Novel semi-blind channel estimation schemes for Rician fading MIMO channel

In this paper, we propose two novel semi-blind channel estimation techniques based on QR decomposition for Rician fading Multiple Input Multiple Output (MIMO) channel. In the first technique, the MIMO channel matrix H can be decomposed as an upper triangular matrix R and unitary rotation matrix Q as H = RQ. The matrix R is estimated blindly from only received data by using the orthogonal matrix triangularization based Householder QR decomposition, while the optimum rotation matrix Q is estimated exclusively from the algorithm of Orthogonal Pilot Maximum Likelihood Estimator (OPML) based on pilot information. In the second technique, the joint semi-blind channel and data estimation is performed by using the Least Square (LS) algorithm based on QR decomposition. The simulation results obtained for 4-PSK data modulation scheme using two transmitters and six receiver antennas for different Rice factor (K) have shown that the BER performance increases with an increase in the Rice factor. Finally, we compare these two new techniques with the conventional semi-blind channel estimation technique based on Whitening Rotation (WR), and the results show that the first proposed technique outperforms and the second technique achieves a very nearby performance as compared to the technique based on whitening rotation.     

基于虚拟多用户模型的长码直扩信号伪码估计

针对长码直接序列扩频(DSSS)信号的伪随机码(PN)盲估计问题,提出了一种新的周期长码直接序列扩频信号(PLC DSSS)模型.该模型等同于虚拟多用户短码扩频DS - CDMA系统,采用DS- CDMA的谱范数来估计同步偏移,该算法不需要更多假定限制,利用相关函数二阶矩、特征值分解和模糊酉矩阵方法就可以完成伪码序列的...     

一种用于MIMO检测中的QR快速分解算法

针对多输入多输出系统中常用的非线性检测算法,如排序QR分解(Sorted QR Decomposition,SQRD)、球型译码（Sphere Decoding,SD）、K-Best或QRM（QR Decomposition and M algorithm）等,提出了一种具有最优检测顺序的QR快速分解方法,作为检测前的预处理操作。该算法首先对信道矩阵进行第一次QR分解,根据所得上三角矩阵R可确定最优的检测顺序,并按该顺序对R进行列重排。然后对R进行第二次QR分解,即得具有最优检测顺序的QR分解结果。与现有的基于R对角元素的模值排序的QR分解算法相比,本算法可保证检测顺序最优从而性能最优。仿真结果表明天线配置为4*4和6*6时,在误码率10^-3处可节约信噪比分别为:1dB和2dB;与现有的基于信干噪比排序的QR分解算法相比,本算法与其性能一致的基础上可节约25%的复乘法次数和33%的复加法次数。      

Blind channel identification: subspace tracking method without rankestimation

Subspace (SS) methods are an effective approach for blind channel identification. However, these methods also have two major disadvantages: 1) They require accurate channel length estimation and/or rank estimation of the correlation matrix, which is difficult with noisy channels, and 2) they require a large amount of computation for the singular value decomposition (SVD), which makes it inconvenient for adaptive implementation. Although many adaptive subspace tracking algorithms can be applied, the computational complexity is still O(m³), where m is the data vector length. In this paper, we introduce new recursive subspace algorithms using ULV updating and successive cancellation techniques. The new algorithms do not need to estimate the rank of the correlation matrix. Furthermore, the channel length can be overestimated initially and be recovered at the end by a successive cancellation procedure, which leads to more convenient implementations. The adaptive algorithm has computations of O(m² ) in each recursion. The new methods can be applied to either the single user or the multiuser cases. Simulations demonstrate their good performance     

Blind adaptive MIMO receivers for space-time block-coded DS-CDMA systems in multipath channels using the constant modulus criterion

We propose blind adaptive multi-input multi-output (MIMO) linear receivers for DS-CDMA systems using multiple transmit antennas and space-time block codes (STBC) in multipath channels. A space-time code-constrained constant modulus (CCM) design criterion based on constrained optimization techniques is considered and recursive least squares (RLS) adaptive algorithms are developed for estimating the parameters of the linear receivers. A blind space-time channel estimation method for MIMO DS-CDMA systems with STBC based on a subspace approach is also proposed along with an efficient RLS algorithm. Simulations for a downlink scenario assess the proposed algorithms in several situations against existing methods.