STBC系统在非同分布Nakagami信道下的一种快速性能评估算法

针对非同分布的Nakagami信道,基于矩生成函数MGF(Moment Generation Function)的分析方法,提出正交空时分组码系统STBC(Space-Time Block Coding)的一种快速性能评估算法,不需要涉及超几何函数积分运算,可在中高信噪比时,快速准确地估计STBC系统的符号错误概率性能。在平坦瑞利衰落信道下的计算机仿真表明,该算法与已有的STBC系统的近似估计算法相比,具有较优的性能。     

一种快衰落下正交空时分组码的译码

准静态衰落信道下正交空时分组码MIMO系统的一种简单最大似然检测算法性能接近于最大似然检测算法．但在实际时变信道干扰下，性能表现较差。本文分析提出了在多个发送和接收天线下能有效消除时变信道对简单最大似然检测算法产生干扰的算法改进。计算机仿真表明，针对时变信道下的正交空时分组码MIMO系统，该译码算法性能实现简单、计算复杂度低，性能接近最大似然检测算法。     

传输损耗条件下基于循环平稳检测的空时分组码盲识别方法

针对传输损耗条件下空间复用（SM）和Alamouti空时分组码的盲识别问题,提出一种单接收天线下的基于循环平稳检测的盲识别算法。首先通过理论推导,证明Alamouti空时分组码具有循环频率的特性;然后,求取接收信号的四阶循环累积量来观察循环频率;最后使用循环平稳检测的方法,构造检测不同空时分组码循环频率的假设检验,从而对2种码的接收信号进行区分。该方法可在单接收天线下进行,蒙特卡洛仿真结果表明,该算法不需要预先知道信道信息、调制方式、载波相位和时间偏移,不需要精确知道载波频偏,识别效果较好。     

一种新的准正交空时分组码设计方法*

自Jafarkhani提出非正交空时分组码以来,其构造方法一直是研究热点之一。构造了一种发射天线为4的准正交空时分组码,为了改善该分组码性能,对输入信号进行了旋转预处理,得到旋转后的准正交空时分组码;系统的接收端采用最大似然译码方法。数值仿真显示,没有预处理的准正交空时分组码的误码率与已提出的准正交空时分组码相当。然而,经旋转后的空时分组码的误比特率至少有2 dB改善。     

一种MIMO-OFDM系统空频分组码相位校正技术

对于MIMO-OFDM系统,本文指出如果直接将空频分组码SFBC与使用了跨符号插值信道估计算法和“增/扣脉冲”同步技术的OFDM方案相结合,那么由后者引入的时域循环移位操作会破坏SFBC算法对于信道假设的有效性,从而导致空频分组码译码性能下降。针对这一现象,本文提出了一种频域相位校正的方法,使得校正后的等效频域信道能重新满足空频码对于信道的要求,进而使这类MIMO-OFDM系统在工程中更具有实用价值。     

一种优化的准正交空时分组码预编码方法

基于准正交空时分组码的时域均衡技术提出一种新的块预编码方法。该方法将平坦衰落信道符号编码扩展到时域上数据块编码,构造了一种嵌入式准正交结构,从而简化了接收端信号处理,降低了复杂度。同时,对准正交空时分组码部分解耦和全解耦两种方式进行了比较,蒙特卡罗仿真结果证明了该方法的优势。     

空时码在Rician衰落信道下性能分析

基于Alamouti提出的BPSK调制下空时分组码在Rayleigh衰落信道中的简单分集方案。推导出多发射和多接收天线系统中正交空时分组码在Rician衰落信道的BPSK调制下的比特差错率的最小距离球界,并推广到在高阶调制下衰落信道中系统符号差错率的性能。仿真分析和比较了空时分组码的多天线系统中发射和接收天线分集增益,以及信道相关参数的变化对系统误比特性能的影响。     

一种采用坐标交织空时编码的空间调制方案

基于坐标交织正交设计(CIOD)的空时编码,提出了一种新型的4发射天线空间调制(SM)方案。与传统的空间调制技术相比,该方案不仅利用天线序号携带信息,提高了无线通信系统的频带利用率,而且通过空时编码可获得较大的分集增益。接收端采用条件最大似然(ML)算法,大大降低了译码复杂度。在独立和相关信道情况下对所提方案、传统空间调制及空时分组码(STBC)的误比特(BER)性能进行了仿真,结果表明,所提方案的性能优于传统的空时分组码和空间调制。     

分层块空时分组码的简化译码方法

为四发射天线的多输入多输出(Multi-input multi-output,MIMO)系统提出一种高速率全分集的分层块空时(Layered block space-time,LBST)分组码.该空时分组码能够提供2符号/信道利用的传输率,为了能够正确地译码,接收天线数必须不少于2个.本文分析了LBST分组码的编码结构与简化的译码算法,并对其性能进行了仿真分析.结果表明:本文方案与其他空时分组码相比较,具有一定的优越性,并且采用简化的译码算法可以很大程度上降低计算复杂度.     

一种新的准正交空时分组码设计

根据正交设计理论,当发送天线数大于2时,不存在可以获得完全分集增益和全速率的复正交空时分组码。对空时分组码采用准正交设计,能够保证数据以全速率传输,但是会使其误码性能降低。本文根据矩阵正交理论提出了一种新的全速率准正交空时分组码并给出了一种基于QR分解的最大似然译码方法。仿真结果表明,本文方案与已有典型的准正交空时分组码相比,具有更好的误码性能。     

VBLAST-OFDM系统中改进的QR分解检测算法

基于VBLAST-OFDM系统中传统QR分解算法,将最大似然和并行干扰消除的思想引入QR分解算法中,提出了对QR分解算法的改进方法,克服了传统QR分解算法检测性能差的缺点,运用QR分解从最后两层信号开始,等效为2收2发的MIMO系统。用最优算法检测判决后,回代到原QR分解算法检测余下层信号;或者依次并行消除已判决信号的影响,再进行下个等效MIMO系统的判决,直至所有信号检测完毕。仿真结果表明,本文改进的QR分解检测算法比传统的QR算法和迫零算法在误码性能上得到改善。     

Adaptive block size for dense QR factorization in hybrid CPU–GPU systems via statistical modeling

QR factorization is a computational kernel of scientific computing. How can the latest computer be used to accelerate this task? We investigate this topic by proposing a dense QR factorization algorithm with adaptive block sizes on a hybrid system that contains a central processing unit (CPU) and a graphic processing unit (GPU). To maximize the use of CPU and GPU, we develop an adaptive scheme that chooses block size at each iteration. The decision is based on statistical surrogate models of performance and an online monitor, which avoids unexpected occasional performance drops. We modify the highly optimized CPU–GPU based QR factorization in MAGMA to implement the proposed schemes. Numerical results suggest that our approaches are efficient and can lead to near-optimal block sizes. The proposed algorithm can be extended to other one-sided factorizations, such as LU and Cholesky factorizations.     

Parallel solution of sparse linear least squares problems on distributed-memory multiprocessors

This paper studies the parallel solution of large-scale sparse linear least squares problems on distributed-memory multiprocessors. The key components required for solving a sparse linear least squares problem are sparse QR factorization and sparse triangular solution. A block-oriented parallel algorithm for sparse QR factorization has already been described in the literature. In this paper, new block-oriented parallel algorithms for sparse triangular solution are proposed. The arithmetic and communication complexities of the new algorithms applied to regular grid problems are analyzed. The proposed parallel sparse triangular solution algorithms together with the block-oriented parallel sparse QR factorization algorithm result in a highly efficient approach to the parallel solution of sparse linear least squares problems. Performance results obtained on an IBM Scalable POWERparallel system SP2 are presented. The largest least squares problem solved has over two million rows and more than a quarter million columns. The execution speed for the numerical factorization of this problem achieves over 3.7 gigaflops per second on an IBM SP2 machine with 128 processors.     

An IV-QR Algorithm for Neuro-Fuzzy Multivariable Online Identification

In this paper, a new algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamic systems, more specifically applied to consequent parameters estimation of the neuro-fuzzy inference system, is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) recurrent neuro-fuzzy structure from input-output samples of a multivariable nonlinear dynamic system in a noisy environment. The adaptive weighted instrumental variable (WIV) algorithm by QR factorization based on the numerically robust orthogonal Householder transformation is developed to modify the consequent parameters of the TS multivariable neuro-fuzzy network     

一种新的平行干扰消除QR分解检测算法

提出了一种VBLAST-OFDM系统中的平行干扰消除QR分解检测算法,称为P-ICQR算法。该算法首先对最先检测层信号做出假设,分成多个平行分支,在每个分支上依次干扰消除已检测信号的影响,运用QR分解判决余下层信号而只保留分集增益最高的最后检测层判决信号,最后用部分最大似然法对平行分支选取最优解作为最终检测结果,有效提高了系统的检测性能。仿真结果表明,提出的P-ICQR算法比传统的平行算法、循环迭代QR分解算法、QR算法、迫零算法的误码性能都要好。     

Solving linear least squares by orthogonal factorization and pseudoinverse computation via the modified Huang algorithm in the ABS class

We consider three algorithms for solving linear least squares problems based upon the modified Huang algorithm (MHA) in the ABS class for linear systems recently introduced by Abaffy, Broyden and Spedicato. The first algorithm uses an explicit QR factorization of the coefficient matrixA computed by applying MHA to the matrixA ^T . The second and the third algorithm is based upon two representations of the Moore-Penrose pseudoinverse constructed with the use of MHA. The three algorithms are tested on a large set of problems and compared with the NAG code using QR factorization with Householder rotations. The comparison shows that the algorithms based on MHA are generally more accurate.     

广义行（列）对称矩阵的QR分解及其算法

对广义行（列）对称矩阵的QR分解和性质进行了研究,给出了广义行（列）对称矩阵的QR分解的公式和快速算法,它们可有效减少广义行（列）对称矩阵的QR分解的计算量与存储量,并且不会丧失数值精度。同时讨论了系统参数估计,推广和丰富了两文（邹红星,王殿军,戴琼海,等.行(或列)对称矩阵的QR分解.中国科学：A辑,2002,32(9):842-849;蔺小林,蒋耀林.酉对称矩阵的QR分解及其算法.计算机学报,2005,28(5):817-822）的研究内容,拓宽了实际应用领域的范围, 并修正了后者的错误。     

iSAM: Incremental Smoothing and Mapping

In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.      

MIMO-CPSC系统中预编码盲信道估计算法研究*

在频域均衡系统中,基于预编码的子空间盲信道估计算法可以有效地对信道进行估计。针对多天线单载波循环前缀系统,提出了一种基于预编码的盲信道估计算法。该算法通过分离出连续两个接收符号块的互相关矩阵所包含的信道信息实现信道盲估计。仿真结果表明,该算法具有较低的计算复杂度以及对信道阶数过估计有很好的鲁棒性,并且该算法利用较少的数据块个数就得到了一个可靠的信道估计值。     

一种基于噪声功率的信道有效阶数盲估计算法

基于二阶统计量的盲均衡算法大多要求估计信道阶数。根据噪声功率随信道阶数的变化特点,提出一种新的阶数估计算法。实验表明,与传统算法相比,该算法不仅复杂度低,在低信噪比和小样本个数情况下估计性能良好,且不依赖于具体的盲信道估计算法,具有较强的适用性。