The Experts below are selected from a list of 377925 Experts worldwide ranked by ideXlab platform

Zhaocheng Wang - One of the best experts on this subject based on the ideXlab platform.

  • low complexity near optimal Signal Detection for uplink large scale mimo systems
    arXiv: Information Theory, 2015
    Co-Authors: Xinyu Gao, Linglong Dai, Zhaocheng Wang
    Abstract:

    Minimum mean square error (MMSE) Signal Detection algorithm is near- optimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for large- scale MIMO is symmetric positive definite, based on which we propose a low-complexity near-optimal Signal Detection algorithm by exploiting the Richardson method to avoid the matrix inversion. The complexity can be reduced from O(K3) to O(K2), where K is the number of users. We also provide the convergence proof of the proposed algorithm. Simulation results show that the proposed Signal Detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.

  • low complexity near optimal Signal Detection for uplink large scale mimo systems
    Electronics Letters, 2014
    Co-Authors: Xinyu Gao, Linglong Dai, Zhaocheng Wang
    Abstract:

    The minimum mean square error (MMSE) Signal Detection algorithm is near-optimal for uplink multi-user large-scale multiple-input-multiple-output (MIMO) systems, but involves matrix inversion with high complexity. It is firstly proved that the MMSE filtering matrix for large-scale MIMO is symmetric positive definite, based on which a low-complexity near-optimal Signal Detection algorithm by exploiting the Richardson method to avoid the matrix inversion is proposed. The complexity can be reduced from O(K 3 ) to O(K 2 ), where K is the number of users. The convergence proof of the proposed algorithm is also provided. Simulation results show that the proposed Signal Detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.

  • Matrix inversion-less Signal Detection using SOR method for uplink large-scale MIMO systems
    2014 IEEE Global Communications Conference GLOBECOM 2014, 2014
    Co-Authors: Xinyu Gao, Linglong Dai, Zhongxu Wang, Yuting Hu, Zhaocheng Wang
    Abstract:

    For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) Signal Detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity Signal Detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative Signal Detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K3) to O(K2), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.

Bart Kosko - One of the best experts on this subject based on the ideXlab platform.

  • 2009 special issue error probability noise benefits in threshold neural Signal Detection
    Neural Networks, 2009
    Co-Authors: Ashok Patel, Bart Kosko
    Abstract:

    Five new theorems and a stochastic learning algorithm show that noise can benefit threshold neural Signal Detection by reducing the probability of Detection error. The first theorem gives a necessary and sufficient condition for such a noise benefit when a threshold neuron performs discrete binary Signal Detection in the presence of additive scale-family noise. The theorem allows the user to find the optimal noise probability density for several closed-form noise types that include generalized Gaussian noise. The second theorem gives a noise-benefit condition for more general threshold Signal Detection when the Signals have continuous probability densities. The third and fourth theorems reduce this noise benefit to a weighted-derivative comparison of Signal probability densities at the Detection threshold when the Signal densities are continuously differentiable and when the noise is symmetric and comes from a scale family. The fifth theorem shows how collective noise benefits can occur in a parallel array of threshold neurons even when an individual threshold neuron does not itself produce a noise benefit. The stochastic gradient-ascent learning algorithm can find the optimal noise value for noise probability densities that do not have a closed form.

  • optimal noise benefits in neyman pearson and inequality constrained statistical Signal Detection
    IEEE Transactions on Signal Processing, 2009
    Co-Authors: Ashok Patel, Bart Kosko
    Abstract:

    We present theorems and an algorithm to find optimal or near-optimal ldquostochastic resonancerdquo (SR) noise benefits for Neyman-Pearson hypothesis testing and for more general inequality-constrained Signal Detection problems. The optimal SR noise distribution is just the randomization of two noise realizations when the optimal noise exists for a single inequality constraint on the average cost. The theorems give necessary and sufficient conditions for the existence of such optimal SR noise in inequality-constrained Signal detectors. There exists a sequence of noise variables whose Detection performance limit is optimal when such noise does not exist. Another theorem gives sufficient conditions for SR noise benefits in Neyman-Pearson and other Signal Detection problems with inequality cost constraints. An upper bound limits the number of iterations that the algorithm requires to find near-optimal noise. The appendix presents the proofs of the main results.

  • error probability noise benefits in threshold neural Signal Detection
    International Joint Conference on Neural Network, 2009
    Co-Authors: Ashok Patel, Bart Kosko
    Abstract:

    Five new theorems and a stochastic learning algorithm show that noise can benefit threshold neural Signal Detection by reducing the probability of Detection error. The first theorem gives a necessary and sufficient condition for such a noise benefit when a threshold neuron performs discrete binary Signal Detection in the presence of additive scale-family noise. The theorem allows the user to find the optimal noise probability density for several closed-form noise types that include generalized Gaussian noise. The second theorem gives a noise-benefit condition for more general threshold Signal Detection when the Signals have continuous probability densities. The third and fourth theorems reduce this noise benefit to a weighted-derivative comparison of Signal probability densities at the Detection threshold when the Signal densities are continuously differentiable and when the noise is symmetric and comes from a scale family. The fifth theorem shows how collective noise benefits can occur in a parallel array of threshold neurons even when an individual threshold neuron does not itself produce a noise benefit. The stochastic gradient-ascent learning algorithm can find the optimal noise value for noise probability densities that do not have a closed form.

Xinyu Gao - One of the best experts on this subject based on the ideXlab platform.

  • low complexity near optimal Signal Detection for uplink large scale mimo systems
    arXiv: Information Theory, 2015
    Co-Authors: Xinyu Gao, Linglong Dai, Zhaocheng Wang
    Abstract:

    Minimum mean square error (MMSE) Signal Detection algorithm is near- optimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for large- scale MIMO is symmetric positive definite, based on which we propose a low-complexity near-optimal Signal Detection algorithm by exploiting the Richardson method to avoid the matrix inversion. The complexity can be reduced from O(K3) to O(K2), where K is the number of users. We also provide the convergence proof of the proposed algorithm. Simulation results show that the proposed Signal Detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.

  • low complexity near optimal Signal Detection for uplink large scale mimo systems
    Electronics Letters, 2014
    Co-Authors: Xinyu Gao, Linglong Dai, Zhaocheng Wang
    Abstract:

    The minimum mean square error (MMSE) Signal Detection algorithm is near-optimal for uplink multi-user large-scale multiple-input-multiple-output (MIMO) systems, but involves matrix inversion with high complexity. It is firstly proved that the MMSE filtering matrix for large-scale MIMO is symmetric positive definite, based on which a low-complexity near-optimal Signal Detection algorithm by exploiting the Richardson method to avoid the matrix inversion is proposed. The complexity can be reduced from O(K 3 ) to O(K 2 ), where K is the number of users. The convergence proof of the proposed algorithm is also provided. Simulation results show that the proposed Signal Detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.

  • Matrix inversion-less Signal Detection using SOR method for uplink large-scale MIMO systems
    2014 IEEE Global Communications Conference GLOBECOM 2014, 2014
    Co-Authors: Xinyu Gao, Linglong Dai, Zhongxu Wang, Yuting Hu, Zhaocheng Wang
    Abstract:

    For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) Signal Detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity Signal Detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative Signal Detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K3) to O(K2), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.

Ashok Patel - One of the best experts on this subject based on the ideXlab platform.

  • 2009 special issue error probability noise benefits in threshold neural Signal Detection
    Neural Networks, 2009
    Co-Authors: Ashok Patel, Bart Kosko
    Abstract:

    Five new theorems and a stochastic learning algorithm show that noise can benefit threshold neural Signal Detection by reducing the probability of Detection error. The first theorem gives a necessary and sufficient condition for such a noise benefit when a threshold neuron performs discrete binary Signal Detection in the presence of additive scale-family noise. The theorem allows the user to find the optimal noise probability density for several closed-form noise types that include generalized Gaussian noise. The second theorem gives a noise-benefit condition for more general threshold Signal Detection when the Signals have continuous probability densities. The third and fourth theorems reduce this noise benefit to a weighted-derivative comparison of Signal probability densities at the Detection threshold when the Signal densities are continuously differentiable and when the noise is symmetric and comes from a scale family. The fifth theorem shows how collective noise benefits can occur in a parallel array of threshold neurons even when an individual threshold neuron does not itself produce a noise benefit. The stochastic gradient-ascent learning algorithm can find the optimal noise value for noise probability densities that do not have a closed form.

  • optimal noise benefits in neyman pearson and inequality constrained statistical Signal Detection
    IEEE Transactions on Signal Processing, 2009
    Co-Authors: Ashok Patel, Bart Kosko
    Abstract:

    We present theorems and an algorithm to find optimal or near-optimal ldquostochastic resonancerdquo (SR) noise benefits for Neyman-Pearson hypothesis testing and for more general inequality-constrained Signal Detection problems. The optimal SR noise distribution is just the randomization of two noise realizations when the optimal noise exists for a single inequality constraint on the average cost. The theorems give necessary and sufficient conditions for the existence of such optimal SR noise in inequality-constrained Signal detectors. There exists a sequence of noise variables whose Detection performance limit is optimal when such noise does not exist. Another theorem gives sufficient conditions for SR noise benefits in Neyman-Pearson and other Signal Detection problems with inequality cost constraints. An upper bound limits the number of iterations that the algorithm requires to find near-optimal noise. The appendix presents the proofs of the main results.

  • error probability noise benefits in threshold neural Signal Detection
    International Joint Conference on Neural Network, 2009
    Co-Authors: Ashok Patel, Bart Kosko
    Abstract:

    Five new theorems and a stochastic learning algorithm show that noise can benefit threshold neural Signal Detection by reducing the probability of Detection error. The first theorem gives a necessary and sufficient condition for such a noise benefit when a threshold neuron performs discrete binary Signal Detection in the presence of additive scale-family noise. The theorem allows the user to find the optimal noise probability density for several closed-form noise types that include generalized Gaussian noise. The second theorem gives a noise-benefit condition for more general threshold Signal Detection when the Signals have continuous probability densities. The third and fourth theorems reduce this noise benefit to a weighted-derivative comparison of Signal probability densities at the Detection threshold when the Signal densities are continuously differentiable and when the noise is symmetric and comes from a scale family. The fifth theorem shows how collective noise benefits can occur in a parallel array of threshold neurons even when an individual threshold neuron does not itself produce a noise benefit. The stochastic gradient-ascent learning algorithm can find the optimal noise value for noise probability densities that do not have a closed form.

Mj Schuemie - One of the best experts on this subject based on the ideXlab platform.

  • a reference standard for evaluation of methods for drug safety Signal Detection using electronic healthcare record databases
    Drug Safety, 2013
    Co-Authors: P M Coloma, Mj Schuemie, Paul Avillach, Carmen Ferrajolo, Antoine Pariente, Francesco Salvo, Annie Fourrierreglat
    Abstract:

    Background The growing interest in using electronic healthcare record (EHR) databases for drug safety surveillance has spurred development of new methodologies for Signal Detection. Although several drugs have been withdrawn postmarketing by regulatory authorities after scientific evaluation of harms and benefits, there is no definitive list of confirmed Signals (i.e. list of all known adverse reactions and which drugs can cause them). As there is no true gold standard, prospective evaluation of Signal Detection methods remains a challenge.

  • methods for drug safety Signal Detection in longitudinal observational databases lgps and leopard
    Pharmacoepidemiology and Drug Safety, 2011
    Co-Authors: Mj Schuemie
    Abstract:

    Purpose There is a growing interest in using longitudinal observational databases for drug safety Signal Detection, but most of the existing statistical methods are tailored towards spontaneous reporting. Here a sequential set of methods for detecting and filtering drug safety Signals in longitudinal databases is presented. Method Longitudinal GPS (LGPS) is a modification of the Gamma Poisson Shrinker (GPS) that uses person time rather than case counts for the estimation of the expected number of events. Longitudinal Evaluation of Observational Profiles of Adverse events Related to Drugs (LEOPARD) is a method that can be used to automatically discard false drug-event associations caused by protopathic bias or misclassification of the dates of the adverse events by comparing prior event prescription rates to post event prescription rates. LEOPARD can generate a single test statistic, or a visualization that can be used for more qualitative information on the relationship between drug and event. Both methods were evaluated using data simulated using the Observational medical dataset SIMulator (OSIM), including the dataset used in the Observational Medical Outcomes Partnership (OMOP) cup, a recent public competition for Signal Detection methods. The Mean Average Precision (MAP) was used for performance measurement. Results On the OMOP cup data, LGPS achieved a MAP of 0.245, and the combination of LGPS and LEOPARD achieved a MAP of 0.260, the highest score in the competition. Conclusions The sequential use of LGPS and LEOPARD have proven to be a useful novel set of methods for drug safety Signal Detection on longitudinal health records. Copyright © 2010 John Wiley & Sons, Ltd.