Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These tech- niques can be characterized in t...Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These tech- niques can be characterized in terms of complexity and detection performance. The ＂efficient frontier＂ of known techniques include the decision-feedback, branch-and-bound and probabilistic data association detectors. The presented iterative multiuser detection technique is based on joint deregularized and box-constrained so- lution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm. The deregulari- zation maximizes the energy of the solution, this is opposite to the Tikhonov regularization where the energy is minimized. However, combined with box-constraints, the deregularization forces the solution to be close to the binary set. We further exploit the box- constrained dichotomous coordinate descent （DCD） algorithm and adapt it to the nonstationary iterative Tikhonov regularization to present an efficient detector. As a result, the worst-case and aver- age complexity are reduced down to K28 and K2~ floating point operation per second, respectively. The development improves the ＂efficient frontier＂ in multiuser detection, which is illustrated by simulation results. Finally, a field programmable gate array （FPGA） design of the detector is presented. The detection performance obtained from the fixed-point FPGA implementation shows a good match to the floating-point implementation.展开更多
In the bioluminescence tomography （BLT） problem, one constructs quantitatively the bioluminescence source distribution inside a small animal from optical signals detected on the animal＇s body surface. The BLT probl...In the bioluminescence tomography （BLT） problem, one constructs quantitatively the bioluminescence source distribution inside a small animal from optical signals detected on the animal＇s body surface. The BLT problem is ill-posed and often the Tikhonov regularization is used to obtain stable approximate solutions. In conventional Tikhonov regularization, it is crucial to choose a proper regularization parameter to balance the accuracy and stability of approximate solutions. In this paper, a parameter-dependent coupled complex boundary method （CCBM） based Tikhonov regularization is applied to the BLT problem governed by the radiative transfer equation （RTE）. By properly adjusting the parameter in the Robin boundary condition, we achieve one important property： the regularized solutions are uniformly stable with respect to the regularization parameter so that the regularization parameter can be chosen based solely on the consideration of the solution accuracy. The discrete-ordinate finite-element method is used to compute numerical solutions. Numerical results are provided to illustrate the performance of the proposed method.展开更多
基金supported by the National Council for Technological and Scientific Development of Brazil (RN82/2008)
文摘Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These tech- niques can be characterized in terms of complexity and detection performance. The ＂efficient frontier＂ of known techniques include the decision-feedback, branch-and-bound and probabilistic data association detectors. The presented iterative multiuser detection technique is based on joint deregularized and box-constrained so- lution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm. The deregulari- zation maximizes the energy of the solution, this is opposite to the Tikhonov regularization where the energy is minimized. However, combined with box-constraints, the deregularization forces the solution to be close to the binary set. We further exploit the box- constrained dichotomous coordinate descent （DCD） algorithm and adapt it to the nonstationary iterative Tikhonov regularization to present an efficient detector. As a result, the worst-case and aver- age complexity are reduced down to K28 and K2~ floating point operation per second, respectively. The development improves the ＂efficient frontier＂ in multiuser detection, which is illustrated by simulation results. Finally, a field programmable gate array （FPGA） design of the detector is presented. The detection performance obtained from the fixed-point FPGA implementation shows a good match to the floating-point implementation.
文摘In the bioluminescence tomography （BLT） problem, one constructs quantitatively the bioluminescence source distribution inside a small animal from optical signals detected on the animal＇s body surface. The BLT problem is ill-posed and often the Tikhonov regularization is used to obtain stable approximate solutions. In conventional Tikhonov regularization, it is crucial to choose a proper regularization parameter to balance the accuracy and stability of approximate solutions. In this paper, a parameter-dependent coupled complex boundary method （CCBM） based Tikhonov regularization is applied to the BLT problem governed by the radiative transfer equation （RTE）. By properly adjusting the parameter in the Robin boundary condition, we achieve one important property： the regularized solutions are uniformly stable with respect to the regularization parameter so that the regularization parameter can be chosen based solely on the consideration of the solution accuracy. The discrete-ordinate finite-element method is used to compute numerical solutions. Numerical results are provided to illustrate the performance of the proposed method.
