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
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
The preconditioned methods for solving linear system are discussed. The convergence rate of accelerated overrelaxation (AOR) method can be enlarged by using the preconditioned method when the classical AOR method conv...The preconditioned methods for solving linear system are discussed. The convergence rate of accelerated overrelaxation (AOR) method can be enlarged by using the preconditioned method when the classical AOR method converges, and the preconditioned method is invalid when the classical iterative method does not converge. The results in corresponding references are improved and perfected.展开更多
A unified convergence theory is derived for a class of stationary iterative methods for solving linear equality constrained quadratic programs or saddle point problems.This class is constructed from essentially all po...A unified convergence theory is derived for a class of stationary iterative methods for solving linear equality constrained quadratic programs or saddle point problems.This class is constructed from essentially all possible splittings of the submatrix residing in the(1,1)-block of the augmented saddle point matrix that would produce non-expansive iterations.The classic augmented Lagrangian method and alternating direction method of multipliers are two special members of this class.展开更多
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=（?）
文摘The preconditioned methods for solving linear system are discussed. The convergence rate of accelerated overrelaxation (AOR) method can be enlarged by using the preconditioned method when the classical AOR method converges, and the preconditioned method is invalid when the classical iterative method does not converge. The results in corresponding references are improved and perfected.
基金This paper is a polished version of the Rice University technical report CAAMTR10-24which was a work supported in part by the National Natural Science Foundation(No.DMS-0811188)Office of Navy Research(No.N00014-08-1-1101).
文摘A unified convergence theory is derived for a class of stationary iterative methods for solving linear equality constrained quadratic programs or saddle point problems.This class is constructed from essentially all possible splittings of the submatrix residing in the(1,1)-block of the augmented saddle point matrix that would produce non-expansive iterations.The classic augmented Lagrangian method and alternating direction method of multipliers are two special members of this class.
基金This work is supported by the Science Foundations of the Education Department ofYunnan Province (03Z169A) and the Science Foundations of Yunnan University (2003Z013B).
