MIMO-OFDM系统中基于QR分解的新型迭代检测算法

结合QR分解的迭代检测算法与连续干扰消除思想提出了一种新型的QR迭代检测算法。该算法充分利用最后检测层分集增益最高、性能最优的特点,在每一次QR分解之后,仅保留最后检测层的判决,在接收信号中消除已判决信号的干扰,并将信道矩阵中已判决信号的列删除,降低信道矩阵列的维数后,进行下一次QR分解,直到所有层的信号都检测出来。分析表明,新型QR迭代检测算法复杂度大约为连续干扰消除算法的1/8,约为传统迭代检测算法的1/2。仿真试验表明,对称系统中新型QR迭代检测算法性能与传统迭代检测算法基本保持一致,都要优于连续干扰消除算法。     

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。     

一种改进的排序QR分解MIMO检测算法

提出了改进的排序QR分解MIMO检测算法,并对其性能进行了分析．该算法针对系统采用排序QR分解检测算法时误码率较高的不足,对信道矩阵按列进行正交变换,避免了求信道矩阵的上三角矩阵,并且仅对信道矩阵按列2—范数模值由小到大进行1次排序．在检测过程中,采用了并行处理的思想,将部分判决信号进行反馈,同时消除接收信号中的干扰,使系统检测性能得到了明显改善．在多散射物的无线通信环境下进行了仿真实验,结果表明,与传统的SQRD算法相比,所提算法在计算复杂度略微下降的情况下,检测性能得到提升．     

基于Householder变换的改进并行MIMO检测算法

针对按序QR分解(SQRD)检测算法在多径瑞利慢衰落信道中检测误码率较高的不足,提出了基于Householder变换的改进并行MIM0检测算法(HIP).该算法对信道矩阵按列进行Householder正交变换,避免了求上三角矩阵的运算并且仅对信道矩阵进行1次排序.在判决信号过程中,采用部分判决信号反馈和接收信号干扰消除并行处理的检测算法,使系统检测性能得到了明显改善.在多散射物的无线通信环境下进行仿真实验,结果表明与传统的SQRD算法相比,所提算法在计算复杂度下降的情况下误码率显著下降.     

大规模天线系统中基于软判决的MIMO信号检测算法

在大规模多输入多输出(MIMO)系统下,提出了一种基于软判决的改进MMSE(IMMSE)信号检测算法。在IMMSE算法中,把MMSE算法检测值作为算法的初始值并采用迭代干扰消除技术。进一步使用对数最大似然比(LLR)将检测序列进行排序,提出一种有序的IMMSE(OIMMSE),并使用软判决技术来提高算法的检测性能。在不同天线数的MIMO系统下,对IMMSE算法和OIMMSE算法进行误码率性能仿真。仿真结果表明,OIMMSE算法和IMMSE算法性能明显优于MMSE。而且提出的新算法随着天线数的增加,越来越接近单输入单输出(SISO)在加性高斯白噪声下的性能。由此可见,新算法对大规模MIMO系统是有效的。     

MC-CDMA系统中基于QR分解的多址干扰消除算法

多址干扰是导致MC-CDMA系统误码性能下降的重要因素。为了消除多址干扰,提出基于接收信号功率排序和QR分解的多址干扰消除算法（Power-based MMSE Sorted QR Decomposition,P-based MMSE-SQRD）对MC-CDMA系统的上行链路进行检测。仿真结果显示,与传统的串行干扰消除算法相比,P-based MMSE-SQRD的误码性能有明显的提高,而且其复杂度也相对较低。     

VBLAST-OFDM系统中的联合检测算法

基于对双向迫零算法(BID-ZF)和循环迭代QR分解算法(IQRD)的研究,提出了VBLAST-OFDM系统中一种双向迫零循环迭代QR分解联合检测算法,称之为BID-IQRD算法.该算法主要克服循环迭代QR分解算法最先检测层性能差和存在误码传播的缺点,把双向迫零算法的检测结果作为循环迭代QR分解算法的输入,进一步遏制了误码传播,有效提高了系统的检测性能.仿真结果表明了联合算法的有效性,可以获得比单纯使用循环迭代QR分解算法或者BID-ZF算法更好的误码性能.     

MIMO-MC-CDMA系统分层空时非线性预编码

针对垂直分层空时方案（VBLAST）传统检测存在误层传输效应及复杂度高的问题,提出了一种多用户MIMO-MC-CDMA下行链路系统中基于QR分解的VBLAST非线性模代数预编码算法,该算法首先采用QR分解获得预编码矩阵,然后在发射端MC-CDMA子载波信道间进行非线性模代数THP预编码,可以有效地消除分层空时码的误层传输效应。在接收端采用迫零与最小均方误差准则,降低了下行接收机的复杂度。仿真结果表明,提出的算法比传统检测算法有效改善了系统的误码性能。     

MIMO-OFDM系统中改进的QRD-M检测算法

在MIMO-OFDM系统的信号检测中传统的QRD-M算法以较低的复杂度逼近了ML检测的性能,具有很好的应用前景。但是该算法M值必须足够大且计算复杂度较高。针对此缺点,在QRD-M算法的基础上,首先对QR分解采用改进的修正GramSchmidt正交化算法,使得接收端能够最先检测信噪比较大的层,从而减少错过ML解的可能性;其次在树搜索过程中与DFE(Decision Feedback Equalization)算法相结合,引入新的参数T。改进算法的前T层用M分支搜索算法检测,剩余的其他层用DFE算法检测。这种方法降低了传统算法复杂度,同时增加了接收端检测的灵活性。仿真结果显示,改进的算法以更低的复杂度获得更接近最大似然检测的性能。     

MIMO-GFDM系统中低复杂度动态禁忌搜索检测算法的改进

针对多输入多输出的广义频分复用（MIMO-GFDM）系统的等效信道矩阵维度极大，传统的MIMO检测算法复杂度高且性能不佳的问题，将大规模MIMO系统中的动态禁忌搜索（RTS）检测算法运用到MIMO-GFDM系统中，并解决了RTS算法初始值的求解复杂度高的问题。首先利用最小均方误差（MMSE）检测算法所用到矩阵的正定对称性将矩阵Cholesky分解，并结合Sherman-Morrison公式迭代计算初始值，降低了初始值求逆的高复杂度；然后以改进的MMSE检测结果作为RTS算法的初始值，从初始值逐步全局搜索最优解；最后通过仿真，对不同算法的迭代次数和误码率（BER）性能进行了研究。理论分析与仿真结果表明：在MIMO-GFDM中，所提改进RTS信号检测算法误码率远低于传统信号检测算法。在4QAM时，RTS算法大约有低于MMSE检测6 dB的信噪比性能增益（误码率在10^-3时）；在16QAM时，RTS算法大约有低于MMSE检测4 dB的信噪比性能增益（误码率在10^-2时）。与传统RTS算法性相比，所提改进算法在不影响误码率性能的同时降低了算法复杂度。     

Tradeoffs in the design of efficient algorithm-based errordetection schemes for hypercube multiprocessors

The authors provide an in-depth study of the various issues and tradeoffs available in algorithm-based error detection, as well as a general methodology for evaluating the schemes. They illustrate the approach on an extremely useful computation in the field of numerical linear algebra: QR factorization. They have implemented and investigated numerous ways of applying algorithm-based error detection using different system-level encoding strategies for QR factorization. Specifically, schemes based on the checksum and sum-of-squares (SOS) encoding techniques have been developed. The results of studies performed on a 16-processor Intel iPSC-2/D4/MX hypercube multiprocessor are reported. It is shown that, in general, the SOS approach gives much better coverage (85-100%) for QR factorization while maintaining low overheads (below 10%)     

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.     

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.     

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.     

正交空时分组码系统的一种新的盲信道估计算法

基于QR分解的信道盲估计方法是一种性能优良的新算法。将该算法推广到正交空时分组码的信道估计中,结合正交空时分组码的特性提出了一种新的信道盲估计算法。与经典的信道盲估计算法相比,新算法的计算量大为降低。同时,Monte Carlo仿真表明,当信噪比较低时,该算法比子空间法具有更好的性能。     

Performance of a QR algorithm implementation on a multicluster of transputers

Some results of an implementation of the QR factorization by Householder reflectors, on a multicluster transputer system with distributed memory are presented, that show how important is the communication time between processor in the performance of the algorithm. The QR factorization was chosen as test method because it is required for many real life applications, for instance in least squares problems. We use a version of Householder transformation that is the basis for numerically stable QR factorization. The machine used was the MultiCluster 2 model of Parsytec which is distributed memory system with 16 Inmos T800 processors. The Helios operating system was chosen because it provides transparency in CPU management. However it limits the sets of connecting topologies to be used. The results are presented in terms of speedup and efficiency, showing the importance of the communication time on the total elapsed time.     

On robust nonlinear modeling of a complex process with large number of inputs using m-QRcp factorization and C(p) statistic

The problem of modeling complex processes with a large number of inputs is addressed. A new method is proposed for the optimization of the models in minimum C(p) statistic sense using QR with a modified scheme of column pivoting (m-QRcp) factorization. Two different classes of multilayer nonlinear modeling problems are explored: 1) in the first class of models, each layer comprises multiple linearly parameterized submodels or cells; the individual cells are optimally modeled using QR factorization, and m-QRcp factorization ensures optimal selection of variables across the layers. 2) The nonhomogeneous feed-forward neural network is chosen as the second class of models, where the network architecture and structure are optimized in terms of best set of hidden links (and nodes) using m-QPcp factorization. In both the cases, the optimization is shown to be direct and conclusive. The proposed is a generic approach to the optimal modeling of complex multilayered architectures, which leads to computationally fast and numerically robust parsimonious designs, free from collinearity problems. The method is largely free from heuristics and is amenable to automated modeling.     

Fast block QR update in digital signal processing

The Journal of Supercomputing - The processing of digital sound signals often requires the computation of the QR factorization of a rectangular system matrix. However, sometimes, only a given (and...