The Experts below are selected from a list of 16932 Experts worldwide ranked by ideXlab platform

Scott T Acton - One of the best experts on this subject based on the ideXlab platform.

  • precog an efficient unitary split preconditioner for the transform domain lms filter via graph laplacian regularization
    arXiv: Signal Processing, 2018
    Co-Authors: Tamal Batabyal, Daniel S Weller, Scott T Acton
    Abstract:

    Transform-domain least mean squares (LMS) adaptive filters encompass the class of algorithms in which the input data are subjected to a unitary transform followed by a Power Normalization stage and an LMS adaptive filter. Because of the data-independent nature of conventional transformations, such a transformation improves the convergence of the LMS filter only for certain classes of input data. However, for input data from unknown classes or a specific set of classes, it is difficult to decide which transformation to use. This decision necessitates a learning framework that obtains such a transformation using input data, which improves the condition number after transformation with minor additional computation. It is hypothesized that the underlying data topology affects the selection of the transformation. With the data modeled as a weighted graph and the input autocorrelation matrix known or computed beforehand, we propose a method, PrecoG, that obtains the desired transform by recursively estimating the graph Laplacian matrix. Additionally, we show the efficacy of the transformation as a generalized split preconditioner on a linear system of equations with an ill-conditioned real positive definite matrix. PrecoG shows significantly improved post-transformation condition number as compared to the existing state-of-the-art techniques that involve unitary and non-unitary transforms.

  • precog an efficient unitary split preconditioner for the transform domain lms filter via graph laplacian regularization
    arXiv: Signal Processing, 2018
    Co-Authors: Tamal Batabyal, Daniel S Weller, Jaideep Kapur, Scott T Acton
    Abstract:

    Transform-domain least mean squares (LMS) adaptive filters encompass the class of algorithms where the input data are subjected to a data-independent unitary transform followed by a Power Normalization stage as preprocessing steps. Because conventional transformations are not data-dependent, this preconditioning procedure was shown theoretically to improve the convergence of the LMS filter only for certain classes of input data. However, in reality if the class of input data is not known beforehand, it is difficult to decide which transformation to use. Thus, there is a need to devise a learning framework to obtain such a preconditioning transformation using input data prior to applying on the input data. It is hypothesized that the underlying topology of the data affects the selection of the transformation. With the input modeled as a weighted graph that mimics neuronal interactions, PrecoG obtains the desired transform by recursive estimation of the graph Laplacian matrix. Additionally, we show the efficacy of the transform as a generalized split preconditioner on a linear system of equations and in Hebb-LMS settings. In terms of the improvement of the condition number after applying the transformation, PrecoG performs significantly better than the existing state-of-the-art techniques that involve unitary and non-unitary transforms.

Akbar M. Sayeed - One of the best experts on this subject based on the ideXlab platform.

  • Ergodic Capacity Upper Bound for Dual MIMO Ricean Systems: Simplified Derivation and Asymptotic Tightness
    2016
    Co-Authors: Michail Matthaiou, David I. Laurenson, Yannis Kopsinis, Akbar M. Sayeed, Senior Member
    Abstract:

    Abstract—An analytical upper bound on the ergodic capacity of Multiple-Input Multiple-Output (MIMO) systems is deduced with the aid of a simplified approach that relies on a fundamental Power Normalization. Given their high practical usability, we are particularly interested in dual configurations where both ends deploy two antenna elements. Contrary to the majority of related studies, where only the common case of Rayleigh fading is considered, our analysis is extended to account for Ricean fading where a deterministic Line-of-Sight (LoS) component exists in the communication link and both ends are affected by spatial correlation. In the following, it is shown that the proposed bound is applicable for any arbitrary Signal-to-Noise Ratio (SNR) and rank of the mean channel matrix. In fact, we consider both conventional and optimized MIMO configurations with equal LoS eigenvalues. Moreover, the tightness of the bound is explored where it is demonstrated that as the SNR tends to zero the bound becomes asymptotically tight; at high SNRs, the offset between empirical capacity and the bound is analytically computed which implies that an explicit asymptotic capacity expression can ultimately be obtained. Index Terms—MIMO systems, ergodic capacity, Ricean fading, spatial fading correlation

  • Ergodic capacity upper bound for dual MIMO ricean systems: Simplified derivation and asymptotic tightness
    IEEE Transactions on Communications, 2009
    Co-Authors: Michail Matthaiou, David I. Laurenson, Yannis Kopsinis, Akbar M. Sayeed
    Abstract:

    An analytical upper bound on the ergodic capacity of Multiple-Input Multiple-Output (MIMO) systems is deduced with the aid of a simplified approach that relies on a fundamental Power Normalization. Given their high practical usability, we are particularly interested in dual configurations where both ends deploy two antenna elements. Contrary to the majority of related studies, where only the common case of Rayleigh fading is considered, our analysis is extended to account for Ricean fading where a deterministic Line-of-Sight (LoS) component exists in the communication link and both ends are affected by spatial correlation. In the following, it is shown that the proposed bound is applicable for any arbitrary Signal-to-Noise Ratio (SNR) and rank of the mean channel matrix. In fact, we consider both conventional and optimized MIMO configurations with equal LoS eigenvalues. Moreover, the tightness of the bound is explored where it is demonstrated that as the SNR tends to zero the bound becomes asymptotically tight; at high SNRs, the offset between empirical capacity and the bound is analytically computed which implies that an explicit asymptotic capacity expression can ultimately be obtained.

Xin Liao - One of the best experts on this subject based on the ideXlab platform.

Michail Matthaiou - One of the best experts on this subject based on the ideXlab platform.

  • Ergodic Capacity Upper Bound for Dual MIMO Ricean Systems: Simplified Derivation and Asymptotic Tightness
    2016
    Co-Authors: Michail Matthaiou, David I. Laurenson, Yannis Kopsinis, Akbar M. Sayeed, Senior Member
    Abstract:

    Abstract—An analytical upper bound on the ergodic capacity of Multiple-Input Multiple-Output (MIMO) systems is deduced with the aid of a simplified approach that relies on a fundamental Power Normalization. Given their high practical usability, we are particularly interested in dual configurations where both ends deploy two antenna elements. Contrary to the majority of related studies, where only the common case of Rayleigh fading is considered, our analysis is extended to account for Ricean fading where a deterministic Line-of-Sight (LoS) component exists in the communication link and both ends are affected by spatial correlation. In the following, it is shown that the proposed bound is applicable for any arbitrary Signal-to-Noise Ratio (SNR) and rank of the mean channel matrix. In fact, we consider both conventional and optimized MIMO configurations with equal LoS eigenvalues. Moreover, the tightness of the bound is explored where it is demonstrated that as the SNR tends to zero the bound becomes asymptotically tight; at high SNRs, the offset between empirical capacity and the bound is analytically computed which implies that an explicit asymptotic capacity expression can ultimately be obtained. Index Terms—MIMO systems, ergodic capacity, Ricean fading, spatial fading correlation

  • Ergodic capacity upper bound for dual MIMO ricean systems: Simplified derivation and asymptotic tightness
    IEEE Transactions on Communications, 2009
    Co-Authors: Michail Matthaiou, David I. Laurenson, Yannis Kopsinis, Akbar M. Sayeed
    Abstract:

    An analytical upper bound on the ergodic capacity of Multiple-Input Multiple-Output (MIMO) systems is deduced with the aid of a simplified approach that relies on a fundamental Power Normalization. Given their high practical usability, we are particularly interested in dual configurations where both ends deploy two antenna elements. Contrary to the majority of related studies, where only the common case of Rayleigh fading is considered, our analysis is extended to account for Ricean fading where a deterministic Line-of-Sight (LoS) component exists in the communication link and both ends are affected by spatial correlation. In the following, it is shown that the proposed bound is applicable for any arbitrary Signal-to-Noise Ratio (SNR) and rank of the mean channel matrix. In fact, we consider both conventional and optimized MIMO configurations with equal LoS eigenvalues. Moreover, the tightness of the bound is explored where it is demonstrated that as the SNR tends to zero the bound becomes asymptotically tight; at high SNRs, the offset between empirical capacity and the bound is analytically computed which implies that an explicit asymptotic capacity expression can ultimately be obtained.

Tamal Batabyal - One of the best experts on this subject based on the ideXlab platform.

  • precog an efficient unitary split preconditioner for the transform domain lms filter via graph laplacian regularization
    arXiv: Signal Processing, 2018
    Co-Authors: Tamal Batabyal, Daniel S Weller, Scott T Acton
    Abstract:

    Transform-domain least mean squares (LMS) adaptive filters encompass the class of algorithms in which the input data are subjected to a unitary transform followed by a Power Normalization stage and an LMS adaptive filter. Because of the data-independent nature of conventional transformations, such a transformation improves the convergence of the LMS filter only for certain classes of input data. However, for input data from unknown classes or a specific set of classes, it is difficult to decide which transformation to use. This decision necessitates a learning framework that obtains such a transformation using input data, which improves the condition number after transformation with minor additional computation. It is hypothesized that the underlying data topology affects the selection of the transformation. With the data modeled as a weighted graph and the input autocorrelation matrix known or computed beforehand, we propose a method, PrecoG, that obtains the desired transform by recursively estimating the graph Laplacian matrix. Additionally, we show the efficacy of the transformation as a generalized split preconditioner on a linear system of equations with an ill-conditioned real positive definite matrix. PrecoG shows significantly improved post-transformation condition number as compared to the existing state-of-the-art techniques that involve unitary and non-unitary transforms.

  • precog an efficient unitary split preconditioner for the transform domain lms filter via graph laplacian regularization
    arXiv: Signal Processing, 2018
    Co-Authors: Tamal Batabyal, Daniel S Weller, Jaideep Kapur, Scott T Acton
    Abstract:

    Transform-domain least mean squares (LMS) adaptive filters encompass the class of algorithms where the input data are subjected to a data-independent unitary transform followed by a Power Normalization stage as preprocessing steps. Because conventional transformations are not data-dependent, this preconditioning procedure was shown theoretically to improve the convergence of the LMS filter only for certain classes of input data. However, in reality if the class of input data is not known beforehand, it is difficult to decide which transformation to use. Thus, there is a need to devise a learning framework to obtain such a preconditioning transformation using input data prior to applying on the input data. It is hypothesized that the underlying topology of the data affects the selection of the transformation. With the input modeled as a weighted graph that mimics neuronal interactions, PrecoG obtains the desired transform by recursive estimation of the graph Laplacian matrix. Additionally, we show the efficacy of the transform as a generalized split preconditioner on a linear system of equations and in Hebb-LMS settings. In terms of the improvement of the condition number after applying the transformation, PrecoG performs significantly better than the existing state-of-the-art techniques that involve unitary and non-unitary transforms.