Massive multiple-input multiple-output(MIMO)is a cornerstone technology in beyond 5G(B5G)communication systems due to its ability to achieve exceptional power and spectral efficiency.The development of low-complexity ...Massive multiple-input multiple-output(MIMO)is a cornerstone technology in beyond 5G(B5G)communication systems due to its ability to achieve exceptional power and spectral efficiency.The development of low-complexity detectors for massive MIMO remains a key area of research,driven by the need to strike a balance between performance and computational complexity,especially as the number of antennas increases at both the transmitter and receiver.In this paper,we propose efficient initialization methods to address these challenges.Instead of the conventional diagonal matrix,we employ the stair matrix and the band matrix in the initialization of the proposed detector based on accelerated overrelaxation.We also employ successive overrelaxation,Gauss-Seidel,and Jacobi methods to improve the performance of the proposed detector.The initialization scaling factors are based on the spectral radius of the iteration matrix.The proposed detectors are evaluated using diverse massive MIMO configurations and multiple modulation schemes and under both perfect and imperfect channel state information(CSI).Extensive simulations show that the proposed detectors achieve significant performance enhancements accompanied by a remarkable reduction in computational complexity,making them highly suitable for practical large-scale systems.展开更多
We present a class of asymptotically optimal successive overrelaxation methods for solving the large sparse system of linear equations. Numerical computations show that these new methods are more efficient and robust ...We present a class of asymptotically optimal successive overrelaxation methods for solving the large sparse system of linear equations. Numerical computations show that these new methods are more efficient and robust than the classical successive overrelaxation method.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
A class of preconditioned iterative methods, i.e., preconditioned generalized accelerated overrelaxation （GAOR） methods, is proposed to solve linear systems based on a class of weighted linear least squares problems...A class of preconditioned iterative methods, i.e., preconditioned generalized accelerated overrelaxation （GAOR） methods, is proposed to solve linear systems based on a class of weighted linear least squares problems. The convergence and comparison results are obtained. The comparison results show that the convergence rate of the preconditioned iterative methods is better than that of the original methods. Furthermore, the effectiveness of the proposed methods is shown in the numerical experiment.展开更多
In order to solve the linear algebraic system AX=b in complex domain, where A is a weakly cyclic of index p=3 matrix (p cyclic matrix), the convergence properties of SOR are studied in the paper. In section 1, we give...In order to solve the linear algebraic system AX=b in complex domain, where A is a weakly cyclic of index p=3 matrix (p cyclic matrix), the convergence properties of SOR are studied in the paper. In section 1, we give some definitions. In section 2, the necessary conditions for convergent complex SOR are given moreover the necessary and sufficient conditions in some special situations are also presented. In section 3, we expand the techniques applied by R.S. Varga et al., and it is established that the results of R.S. Varga et. al. are special cases of our work.展开更多
Recently,the projected Jacobi(PJ)and projected Gauss-Seidel(PGS)iteration methods have been studied for solving the horizontal linear complementarity problems(HLCPs).To further improve the convergence rates of the PJ ...Recently,the projected Jacobi(PJ)and projected Gauss-Seidel(PGS)iteration methods have been studied for solving the horizontal linear complementarity problems(HLCPs).To further improve the convergence rates of the PJ and PGS iteration methods,by using the successive overrelaxation(SOR)matrix splitting technique,a projected SOR iteration method is introduced in this paper to solve the HLCP.Convergence analyses are carefully studied when the system matrices are strictly diagonally dominant and irreducibly diagonally dominant.The newly obtained convergence results greatly extend the current convergence theory.Finally,two numerical examples are given to show the effectiveness of the proposed PSOR iteration method and its advantages over the recently proposed PJ and PGS iteration methods.展开更多
We propose,in this paper,the preconditioned accelerated generalized successive overrelaxation(PAGSOR)iteration method for efficiently solving the large complex symmetric linear systems.To solve the nonlinear systems w...We propose,in this paper,the preconditioned accelerated generalized successive overrelaxation(PAGSOR)iteration method for efficiently solving the large complex symmetric linear systems.To solve the nonlinear systems whose Jacobian matrices are complex and symmetric,treating the PAGSOR method as internal iteration,we construct a modified Newton-PAGSOR(MN-PAGSOR)method to provide an effective approach for solving a wide range of problems in various scientific and engineering fields.Based on the Hölder continuous condition we present the theoretical framework of the modified method,demonstrate its local convergence properties,and provide numerical experiments to validate its effectiveness in solving a class of nonlinear systems.展开更多
基金supported in part by the Seed Research Project under Grant 22020403209in part by the Competitive Research Project under Grant 23020403252in part by the Distributed and Networked Systems Research Group,associated with the Smart Automation and Communication Technologies(SACT)Research Center,Operating Grant 150410,University of Sharjah,Sharjah,UAE.The associate editor coordinating the review of this paper and approving it for publication was F.F.Gao.
文摘Massive multiple-input multiple-output(MIMO)is a cornerstone technology in beyond 5G(B5G)communication systems due to its ability to achieve exceptional power and spectral efficiency.The development of low-complexity detectors for massive MIMO remains a key area of research,driven by the need to strike a balance between performance and computational complexity,especially as the number of antennas increases at both the transmitter and receiver.In this paper,we propose efficient initialization methods to address these challenges.Instead of the conventional diagonal matrix,we employ the stair matrix and the band matrix in the initialization of the proposed detector based on accelerated overrelaxation.We also employ successive overrelaxation,Gauss-Seidel,and Jacobi methods to improve the performance of the proposed detector.The initialization scaling factors are based on the spectral radius of the iteration matrix.The proposed detectors are evaluated using diverse massive MIMO configurations and multiple modulation schemes and under both perfect and imperfect channel state information(CSI).Extensive simulations show that the proposed detectors achieve significant performance enhancements accompanied by a remarkable reduction in computational complexity,making them highly suitable for practical large-scale systems.
基金Subsidized by the Special Funds For Major State Basic Research Project G1999032803.
文摘We present a class of asymptotically optimal successive overrelaxation methods for solving the large sparse system of linear equations. Numerical computations show that these new methods are more efficient and robust than the classical successive overrelaxation method.
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.
基金supported by the National Natural Science Foundation of China (No. 11071033)the Fundamental Research Funds for the Central Universities (No. 090405013)
文摘A class of preconditioned iterative methods, i.e., preconditioned generalized accelerated overrelaxation （GAOR） methods, is proposed to solve linear systems based on a class of weighted linear least squares problems. The convergence and comparison results are obtained. The comparison results show that the convergence rate of the preconditioned iterative methods is better than that of the original methods. Furthermore, the effectiveness of the proposed methods is shown in the numerical experiment.
文摘In order to solve the linear algebraic system AX=b in complex domain, where A is a weakly cyclic of index p=3 matrix (p cyclic matrix), the convergence properties of SOR are studied in the paper. In section 1, we give some definitions. In section 2, the necessary conditions for convergent complex SOR are given moreover the necessary and sufficient conditions in some special situations are also presented. In section 3, we expand the techniques applied by R.S. Varga et al., and it is established that the results of R.S. Varga et. al. are special cases of our work.
基金National Natural Science Foundation of China(No.11771225)Qinglan Project of Jiangsu Province of China.
文摘Recently,the projected Jacobi(PJ)and projected Gauss-Seidel(PGS)iteration methods have been studied for solving the horizontal linear complementarity problems(HLCPs).To further improve the convergence rates of the PJ and PGS iteration methods,by using the successive overrelaxation(SOR)matrix splitting technique,a projected SOR iteration method is introduced in this paper to solve the HLCP.Convergence analyses are carefully studied when the system matrices are strictly diagonally dominant and irreducibly diagonally dominant.The newly obtained convergence results greatly extend the current convergence theory.Finally,two numerical examples are given to show the effectiveness of the proposed PSOR iteration method and its advantages over the recently proposed PJ and PGS iteration methods.
基金National Natural Science Foundation of China with Grant Nos.12161030,12171216.
文摘We propose,in this paper,the preconditioned accelerated generalized successive overrelaxation(PAGSOR)iteration method for efficiently solving the large complex symmetric linear systems.To solve the nonlinear systems whose Jacobian matrices are complex and symmetric,treating the PAGSOR method as internal iteration,we construct a modified Newton-PAGSOR(MN-PAGSOR)method to provide an effective approach for solving a wide range of problems in various scientific and engineering fields.Based on the Hölder continuous condition we present the theoretical framework of the modified method,demonstrate its local convergence properties,and provide numerical experiments to validate its effectiveness in solving a class of nonlinear systems.
