In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve th...In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.展开更多
Highly precise acoustic impedance inversion is a key technology for pre-drilling prediction by VSP data. In this paper, based on the facts that VSP data has high resolution, high signal to noise ratio, and the downgoi...Highly precise acoustic impedance inversion is a key technology for pre-drilling prediction by VSP data. In this paper, based on the facts that VSP data has high resolution, high signal to noise ratio, and the downgoing and upgoing waves can be accurately separated, we propose a method of predicting the impedance below the borehole in front of the bit using VSP data. First, the method of nonlinear iterative inversion is adopted to invert for impedance using the VSP corridor stack. Then, by modifying the damping factor in the iteration and using the preconditioned conjugate gradient method to solve the equations, the stability and convergence of the inversion results can be enhanced. The results of theoretical models and actual data demonstrate that the method is effective for pre-drilling prediction using VSP data.展开更多
The precursors of dipole blocking are obtained by a numerical approach based upon a quasi-geostrophic barotropic planetary- to synoptic-scale interaction model without topography and with a localized synoptic-scale wa...The precursors of dipole blocking are obtained by a numerical approach based upon a quasi-geostrophic barotropic planetary- to synoptic-scale interaction model without topography and with a localized synoptic-scale wave-maker. The optimization problem related to the precursors of blocking is formulated and the nonlinear optimization method is used to examine the optimal synoptic-scale initial field successfully. The results show that the prominent characteristics of the optimal synoptic-scale initial field are that the synoptic-scale wave train structures exist upstream of the incipient blocking. In addition, the large-scale low/high eddy-forcing pattern upstream of the incipient blocking is an essential precondition for the onset of dipole blocking.展开更多
Can the semantics of a program be represented as a single formula? We show that one formula is insufficient to handle assertions, refinement or slicing, while two formulae are sufficient: A (S) , defining non-terminat...Can the semantics of a program be represented as a single formula? We show that one formula is insufficient to handle assertions, refinement or slicing, while two formulae are sufficient: A (S) , defining non-termination, and B (S), defining behaviour. Any two formulae A and B will define a corresponding program. Refinement is defined as implication between these formulae.展开更多
In this paper, a relaxed Hermitian and skew-Hermitian splitting （RHSS） preconditioner is proposed for saddle point problems from the element-free Galerkin （EFG） discretization method. The EFG method is one of the ...In this paper, a relaxed Hermitian and skew-Hermitian splitting （RHSS） preconditioner is proposed for saddle point problems from the element-free Galerkin （EFG） discretization method. The EFG method is one of the most widely used meshfree methods for solving partial differential equations. The RHSS preconditioner is constructed much closer to the coefficient matrix than the well-known HSS preconditioner, resulting in a RHSS fixed-point iteration. Convergence of the RHSS iteration is analyzed and an optimal parameter, which minimizes the spectral radius of the iteration matrix is described. Using the RHSS pre- conditioner to accelerate the convergence of some Krylov subspace methods （like GMRES） is also studied. Theoretical analyses show that the eigenvalues of the RHSS precondi- tioned matrix are real and located in a positive interval. Eigenvector distribution and an upper bound of the degree of the minimal polynomial of the preconditioned matrix are obtained. A practical parameter is suggested in implementing the RHSS preconditioner. Finally, some numerical experiments are illustrated to show the effectiveness of the new preconditioner.展开更多
基金supported by National Natural Science Foundation of China under Grant 61501072 and 61701062Chongqing Research Program of Basic Research and Frontier Technology under Grant cstc2019jcyj-msxmX0079Program for Changjiang Scholars and Innovative Research Team in University under Grant IRT16R72.
文摘In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.
文摘Highly precise acoustic impedance inversion is a key technology for pre-drilling prediction by VSP data. In this paper, based on the facts that VSP data has high resolution, high signal to noise ratio, and the downgoing and upgoing waves can be accurately separated, we propose a method of predicting the impedance below the borehole in front of the bit using VSP data. First, the method of nonlinear iterative inversion is adopted to invert for impedance using the VSP corridor stack. Then, by modifying the damping factor in the iteration and using the preconditioned conjugate gradient method to solve the equations, the stability and convergence of the inversion results can be enhanced. The results of theoretical models and actual data demonstrate that the method is effective for pre-drilling prediction using VSP data.
基金This paper was supported by the Outstanding Youth Natural Science Foundation of China(Grant No.40325016)the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE,PRC(TRAPOYT)the National Natural Science Foundation of China(Grant No.40175011).
文摘The precursors of dipole blocking are obtained by a numerical approach based upon a quasi-geostrophic barotropic planetary- to synoptic-scale interaction model without topography and with a localized synoptic-scale wave-maker. The optimization problem related to the precursors of blocking is formulated and the nonlinear optimization method is used to examine the optimal synoptic-scale initial field successfully. The results show that the prominent characteristics of the optimal synoptic-scale initial field are that the synoptic-scale wave train structures exist upstream of the incipient blocking. In addition, the large-scale low/high eddy-forcing pattern upstream of the incipient blocking is an essential precondition for the onset of dipole blocking.
文摘Can the semantics of a program be represented as a single formula? We show that one formula is insufficient to handle assertions, refinement or slicing, while two formulae are sufficient: A (S) , defining non-termination, and B (S), defining behaviour. Any two formulae A and B will define a corresponding program. Refinement is defined as implication between these formulae.
基金Acknowledgments. The authors express their thanks to the referees for the comments and constructive suggestions, which were valuable in improving the quality of the manuscript. This work is supported by the National Natural Science Foundation of China（11172192） and the National Natural Science Pre-Research Foundation of Soochow University （SDY2011B01）.
文摘In this paper, a relaxed Hermitian and skew-Hermitian splitting （RHSS） preconditioner is proposed for saddle point problems from the element-free Galerkin （EFG） discretization method. The EFG method is one of the most widely used meshfree methods for solving partial differential equations. The RHSS preconditioner is constructed much closer to the coefficient matrix than the well-known HSS preconditioner, resulting in a RHSS fixed-point iteration. Convergence of the RHSS iteration is analyzed and an optimal parameter, which minimizes the spectral radius of the iteration matrix is described. Using the RHSS pre- conditioner to accelerate the convergence of some Krylov subspace methods （like GMRES） is also studied. Theoretical analyses show that the eigenvalues of the RHSS precondi- tioned matrix are real and located in a positive interval. Eigenvector distribution and an upper bound of the degree of the minimal polynomial of the preconditioned matrix are obtained. A practical parameter is suggested in implementing the RHSS preconditioner. Finally, some numerical experiments are illustrated to show the effectiveness of the new preconditioner.
