The Experts below are selected from a list of 30 Experts worldwide ranked by ideXlab platform

Marc Moeneclaey - One of the best experts on this subject based on the ideXlab platform.

  • A Novel MIMO Detection Scheme with Linear Complexity
    2008
    Co-Authors: Frederik Simoens, Henk Wymeersch, Marc Moeneclaey
    Abstract:

    Abstract — In this contribution, we present a novel multipleinput multiple-output (MIMO) detection scheme for Quadrature Amplitude Modulation (QAM). The proposed method is based on the Slowest Descent (SD) method and is suitable for both iterative and non-iterative detectors. Optimal MIMO detection entails maximizing or marginalizing a likelihood function over a very large set of vectors. Similar to other low-complexity detection schemes such as sphere decoding, the SD approach restricts the maximization/marginalization to a small set of candidate vectors. As the size of this set scales linearly with the number of transmit antennas, the SD method bears an exceptionally low complexity. Furthermore, simulation results indicate that the method achieves a close-to-optimal performance, provided that the diversity order is sufficiently high. I

  • Low Complexity MIMO Detection Based on the Slowest Descent Method
    2008
    Co-Authors: Frederik Simoens, D. Van Welden, Henk Wymeersch, Student Member, Marc Moeneclaey
    Abstract:

    Abstract — This contribution presents a novel multiple-input multiple-output (MIMO) detection scheme for Quadrature Amplitude Modulation (QAM). The proposed method is based on the Slowest Descent (SD) method and is suitable for both iterative and non-iterative detectors. Similar to other low-complexity detection schemes, such as sphere decoding, the new SD approach restricts the maximization/marginalization to a small set of candidate vectors. Simulation results indicate that the method achieves a close-to-optimal performance, with a complexity that scales only linearly with the number of transmit antennas. Index Terms — MIMO detection, maximum-likelihood decoding, iterative processing. I

  • Low Complexity MIMO Detection Based on the Slowest Descent Method
    IEEE Communications Letters, 2007
    Co-Authors: Frederik Simoens, D. Van Welden, Henk Wymeersch, Marc Moeneclaey
    Abstract:

    This contribution presents a novel multiple-input multiple-output (MIMO) detection scheme for quadrature amplitude modulation (QAM). The proposed method is based on the Slowest Descent (SD) method and is suitable for both iterative and non-iterative detectors. Similar to other low-complexity detection schemes, such as sphere decoding;, the new SD approach restricts the maximization/marginalization to a small set of candidate vectors. Simulation results indicate that the method achieves a close-to-optimal performance, with a complexity that scales only linearly with the number of transmit antennas

Wen Chen - One of the best experts on this subject based on the ideXlab platform.

  • integer forcing linear receiver design with Slowest Descent method
    arXiv: Signal Processing, 2020
    Co-Authors: Lili Wei, Wen Chen
    Abstract:

    Compute-and-forward (CPF) strategy is one category of network coding in which a relay will compute and forward a linear combination of source messages according to the observed channel coefficients, based on the algebraic structure of lattice codes. Recently, based on the idea of CPF, integer forcing (IF) linear receiver architecture for MIMO system has been proposed to recover different integer combinations of lattice codewords for further original message detection. In this paper, we consider the problem of IF linear receiver design with respect to the channel conditions. Instead of exhaustive search, we present practical and efficient suboptimal algorithms to design the IF coefficient matrix with full rank such that the total achievable rate is maximized, based on the Slowest Descent method. Numerical results demonstrate the effectiveness of our proposed algorithms.

Frederik Simoens - One of the best experts on this subject based on the ideXlab platform.

  • A Novel MIMO Detection Scheme with Linear Complexity
    2008
    Co-Authors: Frederik Simoens, Henk Wymeersch, Marc Moeneclaey
    Abstract:

    Abstract — In this contribution, we present a novel multipleinput multiple-output (MIMO) detection scheme for Quadrature Amplitude Modulation (QAM). The proposed method is based on the Slowest Descent (SD) method and is suitable for both iterative and non-iterative detectors. Optimal MIMO detection entails maximizing or marginalizing a likelihood function over a very large set of vectors. Similar to other low-complexity detection schemes such as sphere decoding, the SD approach restricts the maximization/marginalization to a small set of candidate vectors. As the size of this set scales linearly with the number of transmit antennas, the SD method bears an exceptionally low complexity. Furthermore, simulation results indicate that the method achieves a close-to-optimal performance, provided that the diversity order is sufficiently high. I

  • Low Complexity MIMO Detection Based on the Slowest Descent Method
    2008
    Co-Authors: Frederik Simoens, D. Van Welden, Henk Wymeersch, Student Member, Marc Moeneclaey
    Abstract:

    Abstract — This contribution presents a novel multiple-input multiple-output (MIMO) detection scheme for Quadrature Amplitude Modulation (QAM). The proposed method is based on the Slowest Descent (SD) method and is suitable for both iterative and non-iterative detectors. Similar to other low-complexity detection schemes, such as sphere decoding, the new SD approach restricts the maximization/marginalization to a small set of candidate vectors. Simulation results indicate that the method achieves a close-to-optimal performance, with a complexity that scales only linearly with the number of transmit antennas. Index Terms — MIMO detection, maximum-likelihood decoding, iterative processing. I

  • Low Complexity MIMO Detection Based on the Slowest Descent Method
    IEEE Communications Letters, 2007
    Co-Authors: Frederik Simoens, D. Van Welden, Henk Wymeersch, Marc Moeneclaey
    Abstract:

    This contribution presents a novel multiple-input multiple-output (MIMO) detection scheme for quadrature amplitude modulation (QAM). The proposed method is based on the Slowest Descent (SD) method and is suitable for both iterative and non-iterative detectors. Similar to other low-complexity detection schemes, such as sphere decoding;, the new SD approach restricts the maximization/marginalization to a small set of candidate vectors. Simulation results indicate that the method achieves a close-to-optimal performance, with a complexity that scales only linearly with the number of transmit antennas

Predrag Spasojevic - One of the best experts on this subject based on the ideXlab platform.

  • Improving Soft Interference Cancellation for CDMA Systems
    2013
    Co-Authors: Predrag Spasojevic, Aylin Yener
    Abstract:

    Abstract- The optimum receiver to detect the bits of multiple CDMA users has exponential coniplexity in the number of active users in the system. Previous work showed that the successive and parallel soft interference cancellers correspond to nonlinear programming relaxations of the optimum multiuser detection problem. In this paper, we use this approximation method combined with the Slowest Descent approach to improve the performance of soft interference cancellers. The aim is to achieve a performance closer to the performance of the optimum receiver without significantly compromising the low complexity of the resulting receiver. We derive the resulting detectors and evaluate their performance. Results show that they can achieve near-optimum performance and outperform several previously proposed multiuser detectors.

  • the Slowest Descent method and its application to sequence estimation
    IEEE Transactions on Communications, 2001
    Co-Authors: Predrag Spasojevic, C N Georghiades
    Abstract:

    A new approach to sequence estimation is proposed and its performance is analyzed for a number of channels of practical interest. The proposed approach, termed the Slowest Descent method, comprises as a special case the zero-forcing equalizer for intersymbol interference channels and the decorrelator for the multiuser detection problem. The latter two methods quantize the unconstrained sequence that maximizes the likelihood function. The proposed method can be viewed as a generalization of these two methods in two ways. First, the unconstrained maximization is extended to nonquadratic log-likelihood functions; second, the decorrelator estimate can be "refined" by comparing its likelihood to a set of discrete-valued sequences along mutually orthogonal lines of the least decrease in the likelihood function. The gradient Descent method for iterative computation of the line of least likelihood decrease (i.e., Slowest likelihood Descent) and its relationship to the expectation-maximization (EM) algorithm for unconstrained likelihood maximization is discussed. The Slowest Descent method is shown to provide a performance comparable to maximum-likelihood for a number of channels. These problems can be described by either quadratic or nonquadratic log-likelihood functions.

  • multiuser detection in impulsive noise via Slowest Descent search
    IEEE Workshop on Statistical Signal and Array Processing, 2000
    Co-Authors: Predrag Spasojevic, Xiaodong Wang
    Abstract:

    A new technique is proposed for robust multiuser detection in the presence of non-Gaussian ambient noise. This method is based on minimizing a certain cost function (e.g. the Huber penalty function) over a discrete set of candidate user bit vectors. The set of candidate points are chosen based on the so-called "Slowest-Descent search", starting from the estimate closest to the unconstrained minimizer of the cost function, and along mutually orthogonal directions where this cost function grows the Slowest. The extension of the proposed technique to multiuser detection in unknown multipath fading channels is also proposed. Simulation results show that this new technique offers substantial performance improvement over the recently proposed robust multiuser detectors, with little attendant increase in computational complexity.

C N Georghiades - One of the best experts on this subject based on the ideXlab platform.

  • the Slowest Descent method and its application to sequence estimation
    IEEE Transactions on Communications, 2001
    Co-Authors: Predrag Spasojevic, C N Georghiades
    Abstract:

    A new approach to sequence estimation is proposed and its performance is analyzed for a number of channels of practical interest. The proposed approach, termed the Slowest Descent method, comprises as a special case the zero-forcing equalizer for intersymbol interference channels and the decorrelator for the multiuser detection problem. The latter two methods quantize the unconstrained sequence that maximizes the likelihood function. The proposed method can be viewed as a generalization of these two methods in two ways. First, the unconstrained maximization is extended to nonquadratic log-likelihood functions; second, the decorrelator estimate can be "refined" by comparing its likelihood to a set of discrete-valued sequences along mutually orthogonal lines of the least decrease in the likelihood function. The gradient Descent method for iterative computation of the line of least likelihood decrease (i.e., Slowest likelihood Descent) and its relationship to the expectation-maximization (EM) algorithm for unconstrained likelihood maximization is discussed. The Slowest Descent method is shown to provide a performance comparable to maximum-likelihood for a number of channels. These problems can be described by either quadratic or nonquadratic log-likelihood functions.