Practical sufficient conditions for the convergence of the AOR method and a practical sufficient condition for H-matrices are studied. The obtained convergence conditions suited to matrices, which need not to be diago...Practical sufficient conditions for the convergence of the AOR method and a practical sufficient condition for H-matrices are studied. The obtained convergence conditions suited to matrices, which need not to be diagonally dominant.展开更多
Let the linear system Ax=b where the coefficient matrix A=(a<sub>ij</sub>)∈R<sup>m,n</sup> is an L-ma-trix(that is,a<sub>ij</sub>】0 （?） i and a<sub>ij</sub>≤0 （?...Let the linear system Ax=b where the coefficient matrix A=(a<sub>ij</sub>)∈R<sup>m,n</sup> is an L-ma-trix(that is,a<sub>ij</sub>】0 （?） i and a<sub>ij</sub>≤0 （?） i≠j),A=I-L-U,I is the identity matrix,-L and-U are,respectively,strictly lower and strictly upper triangular parts of A.In[1]theauthors considered two preconditioned linear systems?x=（?） and ?x=（?）展开更多
Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at...Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.展开更多
In this paper, the preconditioned accelerated overrelaxation (AOR) method for solving a class of two-by-two linear systems is presented. A new preconditioner is proposed according to the idea of [1] by Wu and Huang. T...In this paper, the preconditioned accelerated overrelaxation (AOR) method for solving a class of two-by-two linear systems is presented. A new preconditioner is proposed according to the idea of [1] by Wu and Huang. The spectral radii of the iteration matrix of the preconditioned and the original methods are compared. The comparison results show that the convergence rate of the preconditioned AOR methods is indeed better than that of the original AOR methods, whenever the original AOR methods are convergent under certain conditions. Finally, a numerical example is presented to confirm our results.展开更多
文摘Practical sufficient conditions for the convergence of the AOR method and a practical sufficient condition for H-matrices are studied. The obtained convergence conditions suited to matrices, which need not to be diagonally dominant.
文摘Let the linear system Ax=b where the coefficient matrix A=(a<sub>ij</sub>)∈R<sup>m,n</sup> is an L-ma-trix(that is,a<sub>ij</sub>】0 （?） i and a<sub>ij</sub>≤0 （?） i≠j),A=I-L-U,I is the identity matrix,-L and-U are,respectively,strictly lower and strictly upper triangular parts of A.In[1]theauthors considered two preconditioned linear systems?x=（?） and ?x=（?）
基金supported by the key project of the National Natural Science Foundation of China (No. 61431001)Huawei Innovation Research Program, the 5G research program of China Mobile Research Institute (Grant No. [2015] 0615)+2 种基金the open research fund of National Mobile Communications Research Laboratory Southeast University (No.2017D02)Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology)the Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, and Keysight
文摘Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.
文摘In this paper, the preconditioned accelerated overrelaxation (AOR) method for solving a class of two-by-two linear systems is presented. A new preconditioner is proposed according to the idea of [1] by Wu and Huang. The spectral radii of the iteration matrix of the preconditioned and the original methods are compared. The comparison results show that the convergence rate of the preconditioned AOR methods is indeed better than that of the original AOR methods, whenever the original AOR methods are convergent under certain conditions. Finally, a numerical example is presented to confirm our results.
