The Experts below are selected from a list of 222 Experts worldwide ranked by ideXlab platform

B. Farhang-boroujeny - One of the best experts on this subject based on the ideXlab platform.

  • Convergence analysis of chip- and fractionally spaced LMS adaptive multiuser CDMA detectors
    IEEE Transactions on Signal Processing, 2000
    Co-Authors: Yu Gong, B. Farhang-boroujeny
    Abstract:

    This paper analyzes the convergence behavior of the least mean square (LMS) filter when used in an adaptive code division multiple access (CDMA) detector consisting of a tapped delay line with adjustable tap weights. The sampling rate may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally spaced (FS) detection, respectively. It is shown that CS and FS detectors with the same time-span exhibit identical convergence behavior if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). This phenomenon is carefully explained with reference to the eigenvalues of the Correlation Matrix when the Input signal is not perfectly bandlimited. The inadequacy of the eigenvalue spread of the tap-Input Correlation Matrix as an indicator of the transient behavior and the influence of the initial tap weight vector on convergence speed are highlighted. Specifically, a initialization within the signal subspace or to the origin leads to very much faster convergence compared with initialization in the a noise subspace.

  • WCNC - Convergence analysis of LMS multiuser CDMA detectors
    WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466), 1999
    Co-Authors: Yu Gong, B. Farhang-boroujeny
    Abstract:

    The convergence behaviour of the LMS CDMA multiuser detector is of great interest both in practice and in theory. The sampling rate for the tap-Input of a LMS detector may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally-spaced (FS) detection, respectively. It is shown in this paper that CS and FS detectors with the same time-span exhibit identical convergence behaviour, if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). The inadequacy of the eigenvalue spread of the tap-Input Correlation Matrix as an indicator of transient behaviour, and the influence of the initial tap weight vector on convergence speed, are highlighted. Specifically, initialization within the signal subspace or to the origin leads to very much faster convergence compared to initialization in the noise subspace.

  • Convergence analysis of LMS multiuser CDMA detectors
    WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466), 1999
    Co-Authors: Yu Gong, B. Farhang-boroujeny
    Abstract:

    The convergence behaviour of the LMS CDMA multiuser detector is of great interest both in practice and in theory. The sampling rate for the tap-Input of a LMS detector may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally-spaced (FS) detection, respectively. It is shown in this paper that CS and FS detectors with the same time-span exhibit identical convergence behaviour, if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). The inadequacy of the eigenvalue spread of the tap-Input Correlation Matrix as an indicator of transient behaviour, and the influence of the initial tap weight vector on convergence speed, are highlighted. Specifically, initialization within the signal subspace or to the origin leads to very much faster convergence compared to initialization in the noise subspace.

Andy W. H. Khong - One of the best experts on this subject based on the ideXlab platform.

  • APCCAS - An efficient quasi LMS/Newton adaptive algorithm for stereophonic acoustic echo cancellation
    2010 IEEE Asia Pacific Conference on Circuits and Systems, 2010
    Co-Authors: Mehdi Bekrani, Mojtaba Lotfizad, Andy W. H. Khong
    Abstract:

    In this paper an efficient quasi LMS/Newton adaptive algorithm is proposed for stereophonic acoustic echo cancellation. This method employs an efficient pseudo-diagonalization approach to the estimated joint-Input Correlation Matrix with an emphasis on reducing its high cross-Correlation components. We derive an estimate of the inverse joint-Input Correlation Matrix by means of this pseudo-diagonalization method. Simulation results show the improvement in convergence performance of this algorithm compared to the nonlinear RLS and nonlinear NLMS adaptive algorithms.

  • Neural network based adaptive echo cancellation for stereophonic teleconferencing application
    2010 IEEE International Conference on Multimedia and Expo, 2010
    Co-Authors: Mehdi Bekrani, Andy W. H. Khong, Mojtaba Lotfizad
    Abstract:

    Acoustic transmission for conferencing systems have progressed from the use of single channel to one that employs stereophonic channels. One of the most important challenges for such stereophonic system is the problem of stereophonic acoustic echo cancellation (SAEC) where a pair of echo cancellers are deployed to estimate the acoustic impulse responses of the receiving room. We propose, in this paper, a neural network based adaptive filtering approach for SAEC. The neural network is employed to decorrelate the Input vectors for efficient filter updating, resulting in a high convergence rate of the adaptive filters for this multi-channel acoustic application. To further enhance the efficiency of the proposed algorithm, we then utilize the joint-Input Correlation Matrix of the stereophonic signals so as to simplify the proposed neural network. Simulation results show the improvement in performance of the proposed adaptive SAEC approach over the state-of-the-art algorithms.

  • ICME - Neural network based adaptive echo cancellation for stereophonic teleconferencing application
    2010 IEEE International Conference on Multimedia and Expo, 2010
    Co-Authors: Mehdi Bekrani, Andy W. H. Khong, Mojtaba Lotfizad
    Abstract:

    Acoustic transmission for conferencing systems have progressed from the use of single channel to one that employs stereophonic channels. One of the most important challenges for such stereophonic system is the problem of stereophonic acoustic echo cancellation (SAEC) where a pair of echo cancellers are deployed to estimate the acoustic impulse responses of the receiving room. We propose, in this paper, a neural network based adaptive filtering approach for SAEC. The neural network is employed to decorrelate the Input vectors for efficient filter updating, resulting in a high convergence rate of the adaptive filters for this multi-channel acoustic application. To further enhance the efficiency of the proposed algorithm, we then utilize the joint-Input Correlation Matrix of the stereophonic signals so as to simplify the proposed neural network. Simulation results show the improvement in performance of the proposed adaptive SAEC approach over the state-of-the-art algorithms.

  • An efficient quasi LMS/Newton adaptive algorithm for stereophonic acoustic echo cancellation
    2010 IEEE Asia Pacific Conference on Circuits and Systems, 2010
    Co-Authors: Mehdi Bekrani, Mojtaba Lotfizad, Andy W. H. Khong
    Abstract:

    In this paper an efficient quasi LMS/Newton adaptive algorithm is proposed for stereophonic acoustic echo cancellation. This method employs an efficient pseudo-diagonalization approach to the estimated joint-Input Correlation Matrix with an emphasis on reducing its high cross-Correlation components. We derive an estimate of the inverse joint-Input Correlation Matrix by means of this pseudo-diagonalization method. Simulation results show the improvement in convergence performance of this algorithm compared to the nonlinear RLS and nonlinear NLMS adaptive algorithms.

Mehdi Bekrani - One of the best experts on this subject based on the ideXlab platform.

  • APCCAS - An efficient quasi LMS/Newton adaptive algorithm for stereophonic acoustic echo cancellation
    2010 IEEE Asia Pacific Conference on Circuits and Systems, 2010
    Co-Authors: Mehdi Bekrani, Mojtaba Lotfizad, Andy W. H. Khong
    Abstract:

    In this paper an efficient quasi LMS/Newton adaptive algorithm is proposed for stereophonic acoustic echo cancellation. This method employs an efficient pseudo-diagonalization approach to the estimated joint-Input Correlation Matrix with an emphasis on reducing its high cross-Correlation components. We derive an estimate of the inverse joint-Input Correlation Matrix by means of this pseudo-diagonalization method. Simulation results show the improvement in convergence performance of this algorithm compared to the nonlinear RLS and nonlinear NLMS adaptive algorithms.

  • Neural network based adaptive echo cancellation for stereophonic teleconferencing application
    2010 IEEE International Conference on Multimedia and Expo, 2010
    Co-Authors: Mehdi Bekrani, Andy W. H. Khong, Mojtaba Lotfizad
    Abstract:

    Acoustic transmission for conferencing systems have progressed from the use of single channel to one that employs stereophonic channels. One of the most important challenges for such stereophonic system is the problem of stereophonic acoustic echo cancellation (SAEC) where a pair of echo cancellers are deployed to estimate the acoustic impulse responses of the receiving room. We propose, in this paper, a neural network based adaptive filtering approach for SAEC. The neural network is employed to decorrelate the Input vectors for efficient filter updating, resulting in a high convergence rate of the adaptive filters for this multi-channel acoustic application. To further enhance the efficiency of the proposed algorithm, we then utilize the joint-Input Correlation Matrix of the stereophonic signals so as to simplify the proposed neural network. Simulation results show the improvement in performance of the proposed adaptive SAEC approach over the state-of-the-art algorithms.

  • ICME - Neural network based adaptive echo cancellation for stereophonic teleconferencing application
    2010 IEEE International Conference on Multimedia and Expo, 2010
    Co-Authors: Mehdi Bekrani, Andy W. H. Khong, Mojtaba Lotfizad
    Abstract:

    Acoustic transmission for conferencing systems have progressed from the use of single channel to one that employs stereophonic channels. One of the most important challenges for such stereophonic system is the problem of stereophonic acoustic echo cancellation (SAEC) where a pair of echo cancellers are deployed to estimate the acoustic impulse responses of the receiving room. We propose, in this paper, a neural network based adaptive filtering approach for SAEC. The neural network is employed to decorrelate the Input vectors for efficient filter updating, resulting in a high convergence rate of the adaptive filters for this multi-channel acoustic application. To further enhance the efficiency of the proposed algorithm, we then utilize the joint-Input Correlation Matrix of the stereophonic signals so as to simplify the proposed neural network. Simulation results show the improvement in performance of the proposed adaptive SAEC approach over the state-of-the-art algorithms.

  • An efficient quasi LMS/Newton adaptive algorithm for stereophonic acoustic echo cancellation
    2010 IEEE Asia Pacific Conference on Circuits and Systems, 2010
    Co-Authors: Mehdi Bekrani, Mojtaba Lotfizad, Andy W. H. Khong
    Abstract:

    In this paper an efficient quasi LMS/Newton adaptive algorithm is proposed for stereophonic acoustic echo cancellation. This method employs an efficient pseudo-diagonalization approach to the estimated joint-Input Correlation Matrix with an emphasis on reducing its high cross-Correlation components. We derive an estimate of the inverse joint-Input Correlation Matrix by means of this pseudo-diagonalization method. Simulation results show the improvement in convergence performance of this algorithm compared to the nonlinear RLS and nonlinear NLMS adaptive algorithms.

Mojtaba Lotfizad - One of the best experts on this subject based on the ideXlab platform.

  • APCCAS - An efficient quasi LMS/Newton adaptive algorithm for stereophonic acoustic echo cancellation
    2010 IEEE Asia Pacific Conference on Circuits and Systems, 2010
    Co-Authors: Mehdi Bekrani, Mojtaba Lotfizad, Andy W. H. Khong
    Abstract:

    In this paper an efficient quasi LMS/Newton adaptive algorithm is proposed for stereophonic acoustic echo cancellation. This method employs an efficient pseudo-diagonalization approach to the estimated joint-Input Correlation Matrix with an emphasis on reducing its high cross-Correlation components. We derive an estimate of the inverse joint-Input Correlation Matrix by means of this pseudo-diagonalization method. Simulation results show the improvement in convergence performance of this algorithm compared to the nonlinear RLS and nonlinear NLMS adaptive algorithms.

  • Neural network based adaptive echo cancellation for stereophonic teleconferencing application
    2010 IEEE International Conference on Multimedia and Expo, 2010
    Co-Authors: Mehdi Bekrani, Andy W. H. Khong, Mojtaba Lotfizad
    Abstract:

    Acoustic transmission for conferencing systems have progressed from the use of single channel to one that employs stereophonic channels. One of the most important challenges for such stereophonic system is the problem of stereophonic acoustic echo cancellation (SAEC) where a pair of echo cancellers are deployed to estimate the acoustic impulse responses of the receiving room. We propose, in this paper, a neural network based adaptive filtering approach for SAEC. The neural network is employed to decorrelate the Input vectors for efficient filter updating, resulting in a high convergence rate of the adaptive filters for this multi-channel acoustic application. To further enhance the efficiency of the proposed algorithm, we then utilize the joint-Input Correlation Matrix of the stereophonic signals so as to simplify the proposed neural network. Simulation results show the improvement in performance of the proposed adaptive SAEC approach over the state-of-the-art algorithms.

  • ICME - Neural network based adaptive echo cancellation for stereophonic teleconferencing application
    2010 IEEE International Conference on Multimedia and Expo, 2010
    Co-Authors: Mehdi Bekrani, Andy W. H. Khong, Mojtaba Lotfizad
    Abstract:

    Acoustic transmission for conferencing systems have progressed from the use of single channel to one that employs stereophonic channels. One of the most important challenges for such stereophonic system is the problem of stereophonic acoustic echo cancellation (SAEC) where a pair of echo cancellers are deployed to estimate the acoustic impulse responses of the receiving room. We propose, in this paper, a neural network based adaptive filtering approach for SAEC. The neural network is employed to decorrelate the Input vectors for efficient filter updating, resulting in a high convergence rate of the adaptive filters for this multi-channel acoustic application. To further enhance the efficiency of the proposed algorithm, we then utilize the joint-Input Correlation Matrix of the stereophonic signals so as to simplify the proposed neural network. Simulation results show the improvement in performance of the proposed adaptive SAEC approach over the state-of-the-art algorithms.

  • An efficient quasi LMS/Newton adaptive algorithm for stereophonic acoustic echo cancellation
    2010 IEEE Asia Pacific Conference on Circuits and Systems, 2010
    Co-Authors: Mehdi Bekrani, Mojtaba Lotfizad, Andy W. H. Khong
    Abstract:

    In this paper an efficient quasi LMS/Newton adaptive algorithm is proposed for stereophonic acoustic echo cancellation. This method employs an efficient pseudo-diagonalization approach to the estimated joint-Input Correlation Matrix with an emphasis on reducing its high cross-Correlation components. We derive an estimate of the inverse joint-Input Correlation Matrix by means of this pseudo-diagonalization method. Simulation results show the improvement in convergence performance of this algorithm compared to the nonlinear RLS and nonlinear NLMS adaptive algorithms.

Yu Gong - One of the best experts on this subject based on the ideXlab platform.

  • Convergence analysis of chip- and fractionally spaced LMS adaptive multiuser CDMA detectors
    IEEE Transactions on Signal Processing, 2000
    Co-Authors: Yu Gong, B. Farhang-boroujeny
    Abstract:

    This paper analyzes the convergence behavior of the least mean square (LMS) filter when used in an adaptive code division multiple access (CDMA) detector consisting of a tapped delay line with adjustable tap weights. The sampling rate may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally spaced (FS) detection, respectively. It is shown that CS and FS detectors with the same time-span exhibit identical convergence behavior if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). This phenomenon is carefully explained with reference to the eigenvalues of the Correlation Matrix when the Input signal is not perfectly bandlimited. The inadequacy of the eigenvalue spread of the tap-Input Correlation Matrix as an indicator of the transient behavior and the influence of the initial tap weight vector on convergence speed are highlighted. Specifically, a initialization within the signal subspace or to the origin leads to very much faster convergence compared with initialization in the a noise subspace.

  • WCNC - Convergence analysis of LMS multiuser CDMA detectors
    WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466), 1999
    Co-Authors: Yu Gong, B. Farhang-boroujeny
    Abstract:

    The convergence behaviour of the LMS CDMA multiuser detector is of great interest both in practice and in theory. The sampling rate for the tap-Input of a LMS detector may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally-spaced (FS) detection, respectively. It is shown in this paper that CS and FS detectors with the same time-span exhibit identical convergence behaviour, if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). The inadequacy of the eigenvalue spread of the tap-Input Correlation Matrix as an indicator of transient behaviour, and the influence of the initial tap weight vector on convergence speed, are highlighted. Specifically, initialization within the signal subspace or to the origin leads to very much faster convergence compared to initialization in the noise subspace.

  • Convergence analysis of LMS multiuser CDMA detectors
    WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466), 1999
    Co-Authors: Yu Gong, B. Farhang-boroujeny
    Abstract:

    The convergence behaviour of the LMS CDMA multiuser detector is of great interest both in practice and in theory. The sampling rate for the tap-Input of a LMS detector may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally-spaced (FS) detection, respectively. It is shown in this paper that CS and FS detectors with the same time-span exhibit identical convergence behaviour, if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). The inadequacy of the eigenvalue spread of the tap-Input Correlation Matrix as an indicator of transient behaviour, and the influence of the initial tap weight vector on convergence speed, are highlighted. Specifically, initialization within the signal subspace or to the origin leads to very much faster convergence compared to initialization in the noise subspace.