The Experts below are selected from a list of 5799 Experts worldwide ranked by ideXlab platform
E Eweda - One of the best experts on this subject based on the ideXlab platform.
-
analysis of a normalized lms adaptive filter with a singular Input Covariance Matrix
Information Sciences Signal Processing and their Applications, 2007Co-Authors: E EwedaAbstract:The paper analyzes the signal behavior of an adaptive filter whose adaptation is governed by the normalized least mean square (NLMS) algorithm when the Covariance Matrix of the filter Input is singular. The signal behavior is evaluated in terms of the mean square of the excess output error of the filter. The analysis is done in the context of adaptive identification of a time-invariant plant. The plant Input and plant noise are assumed stationary and mutually independent. Under these assumptions, it is found that the long-term average of the mean square excess error of the NLMS algorithm is proportional to the algorithm step size. This implies that in spite of the singularity of the Input Covariance Matrix, the steady state signal behavior of the algorithm can be made arbitrarily fine by using a sufficiently small step size. The analytical results of the paper are supported by simulations.
-
ISSPA - Analysis of a normalized LMS adaptive filter with a singular Input Covariance Matrix
2007 9th International Symposium on Signal Processing and Its Applications, 2007Co-Authors: E EwedaAbstract:The paper analyzes the signal behavior of an adaptive filter whose adaptation is governed by the normalized least mean square (NLMS) algorithm when the Covariance Matrix of the filter Input is singular. The signal behavior is evaluated in terms of the mean square of the excess output error of the filter. The analysis is done in the context of adaptive identification of a time-invariant plant. The plant Input and plant noise are assumed stationary and mutually independent. Under these assumptions, it is found that the long-term average of the mean square excess error of the NLMS algorithm is proportional to the algorithm step size. This implies that in spite of the singularity of the Input Covariance Matrix, the steady state signal behavior of the algorithm can be made arbitrarily fine by using a sufficiently small step size. The analytical results of the paper are supported by simulations.
-
signal behavior of adaptive filtering algorithms in a nonstationary environment with singular data Covariance Matrix
Signal Processing, 2005Co-Authors: E EwedaAbstract:The paper analyzes the signal behavior of adaptive filtering algorithms when the target weights of the adaptive filter are time varying and the Covariance Matrix of the filter Input is singular. The signal behavior is evaluated in terms of moments of the excess output error of the filter. Two algorithms are considered: the LMS algorithm and the sign algorithm. The analysis is done in the context of adaptive plant identification. The plant parameters vary according to a random walk model. The plant Input, plant noise, and plant parameters are assumed mutually independent. Under these assumptions, it is found that the signal behavior of the algorithms is the same as the signal behavior in the case with positive definite Input Covariance Matrix.
-
Analysis and design of a signed regressor LMS algorithm for stationary and nonstationary adaptive filtering with correlated Gaussian data
IEEE Transactions on Circuits and Systems, 1990Co-Authors: E EwedaAbstract:A least mean square (LMS) algorithm with clipped data is studied for use when updating the weights of an adaptive filter with correlated Gaussian Input. Both stationary and nonstationary environments are considered. Three main contributions are presented. The first, corresponding to the stationary case, is a proof of the convergence of the algorithm in the case of a M-dependent sequence of correlated observation vectors. It is proven that the steady state mean square misalignment of the adaptive filter weights has an upper bound proportional to the algorithm step size mu . The second contribution, also belonging to the stationary case, is the derivation of the expressions of convergence time N/sub c/ and steady state mean square excess estimation error epsilon . It is shown that N/sub c/ is proportional to 1/( mu lambda ), with lambda being the minimum eigenvalue of the Input Covariance Matrix. It is also shown that the product N/sub c/ epsilon is independent of mu . For a given epsilon , the convergence time increases with the eigenvalue spread of the Input Covariance Matrix and the filter length, as well as its Input noise power. The range of mu that achieves tolerable values of N/sub c/ and epsilon is determined. The third contribution is concerned with the nonstationary case. It is shown that the mean square excess estimation error is the sum of the two terms with opposite dependencies on mu . An optimum value of mu is derived. >
Philippe Loubaton - One of the best experts on this subject based on the ideXlab platform.
-
Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
Entropy, 2012Co-Authors: Florian Dupuy, Philippe LoubatonAbstract:This paper provides a comprehensive introduction of large random Matrix methods for Input Covariance Matrix optimization of mutual information of MIMO systems. It is first recalled informally how large system approximations of mutual information can be derived. Then, the optimization of the approximations is discussed, and important methodological points that are not necessarily covered by the existing literature are addressed, including the strict concavity of the approximation, the structure of the argument of its maximum, the accuracy of the large system approach with regard to the number of antennas, or the justification of iterative water-filling optimization algorithms. While the existing papers have developed methods adapted to a specific model, this contribution tries to provide a unified view of the large system approximation approach.
-
Optimization of MIMO Systems Capacity Using Large Random
2012Co-Authors: Florian Dupuy, Philippe LoubatonAbstract:Universite Paris-Est/Marne la Vallee, LIGM, UMR CNRS 8049, 5 Bd. Descartes, Champs/Marne,77454 Marne la Vallee Cedex 2, France* Author to whom correspondence should be addressed; E-Mails: florian.dupuy@thalesgroup.com(F.D.); loubaton@univ-mlv.fr (P.L.), Tel.: +33-160-95-7293, Fax: +33-160-95-7755.Received: 12 September 2012; in revised form: 19 October 2012 / Accepted: 24 October 2012 /Published: 1 November 2012Abstract: This paper provides a comprehensive introduction of large random Matrixmethods for Input Covariance Matrix optimization of mutual information of MIMO systems.It is first recalled informally how large system approximations of mutual information canbe derived. Then, the optimization of the approximations is discussed, and importantmethodological points that are not necessarily covered by the existing literature areaddressed, including the strict concavity of the approximation, the structure of the argumentof its maximum, the accuracy of the large system approach with regard to the number ofantennas, or the justification of iterative water-filling optimization algorithms. While theexisting papers have developed methods adapted to a specific model, this contribution triesto provide a unified view of the large system approximation approach.Keywords: large random matrices; MIMO systems; average mutual information;optimization of the Input Covariance Matrix; iterative water-filling1. IntroductionIt is now recognized that multiple-Input multiple-output (MIMO) systems have the potential toincrease the capacity of wireless digital communication systems. If tand rdenote the number of transmitand receive antennas, the channel capacity gain can be in fact of the order of min(t;r) under certain(often rather unrealistic) assumptions [1]. Although the use of MIMO systems begins to be normalized,
-
On the Capacity Achieving Covariance Matrix for Frequency Selective MIMO Channels Using the Asymptotic Approach
IEEE Transactions on Information Theory, 2011Co-Authors: Florian Dupuy, Philippe LoubatonAbstract:In this contribution, an algorithm for evaluating the capacity-achieving Input Covariance matrices for frequency selective Rayleigh MIMO channels is proposed. In contrast with the flat fading Rayleigh case, no closed-form expressions for the eigenvectors of the optimum Input Covariance Matrix are available. Classically, both the eigenvectors and eigenvalues are computed numerically and the corresponding optimization algorithms remain computationally very demanding. In this paper, it is proposed to optimize (w.r.t. the Input Covariance Matrix) a large system approximation of the average mutual information derived by Moustakas and Simon. The validity of this asymptotic approximation is clarified thanks to Gaussian large random matrices methods. It is shown that the approximation is a strictly concave function of the Input Covariance Matrix and that the average mutual information evaluated at the argmax of the approximation is equal to the capacity of the channel up to a O(1/t) term, where t is the number of transmit antennas. An algorithm based on an iterative waterfilling scheme is proposed to maximize the average mutual information approximation, and its convergence studied. Numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information.
-
On the capacity achieving Covariance Matrix for frequency selective MIMO channels using the asymptotic approach
2010Co-Authors: Florian Dupuy, Philippe LoubatonAbstract:In this contribution, an algorithm for evaluating the capacity achieving Input Covariance matrices for frequency selective Rayleigh MIMO channels is proposed. In contrast with the flat fading Rayleigh cases, no closed-form expressions for the eigenvectors of the optimum Input Covariance Matrix are available. Classically, both the eigenvectors and eigenvalues are computed numerically and the corresponding optimization algorithms remain computationally very demanding. In this paper, it is proposed to optimize (w.r.t. the Input Covariance Matrix) a large system approximation of the average mutual information derived by Moustakas and Simon. An algorithm based on an iterative water filling scheme is proposed, and its convergence is studied. Numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information.
-
ISIT - On the capacity achieving Covariance Matrix for frequency selective MIMO channels using the asymptotic approach
2010 IEEE International Symposium on Information Theory, 2010Co-Authors: Florian Dupuy, Philippe LoubatonAbstract:In this contribution, an algorithm for evaluating the capacity-achieving Input Covariance matrices for frequency selective Rayleigh MIMO channels is proposed. In contrast with the flat fading Rayleigh cases, no closed-form expressions for the eigenvectors of the optimum Input Covariance Matrix are available. Classically, both the eigenvectors and eigenvalues are computed numerically and the corresponding optimization algorithms remain computationally very demanding. In this paper, it is proposed to optimize (w.r.t. the Input Covariance Matrix) a large system approximation of the average mutual information derived by Moustakas and Simon. An algorithm based on an iterative water filling scheme is proposed, and its convergence is studied. Numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information.
Athina P. Petropulu - One of the best experts on this subject based on the ideXlab platform.
-
WPMC - Optimality of beamforming and closed form secrecy capacity of MIMO wiretap channels with two transmit antennas
2013Co-Authors: Athina P. PetropuluAbstract:A Gaussian multiple-Input multiple-output (MIMO) wiretap channel model is considered. The Input is a two-antenna transmitter, while the outputs are the legitimate receiver and an eavesdropper, both equipped with multiple antennas. All channels are assumed to be known. The problem of obtaining the optimal Input Covariance Matrix that achieves secrecy capacity subject to a sum power constraint is addressed, and a closed-form expression for the secrecy capacity is obtained. The sufficient and necessary condition for beamforming to be optimal is also given.
-
GLOBECOM - Outage secrecy rate in wireless relay channels using cooperative jamming
2012 IEEE Global Communications Conference (GLOBECOM), 2012Co-Authors: Shuangyu Luo, Athina P. PetropuluAbstract:A wireless relay channel is considered, consisting of a multi-antenna source, a single-antenna destination, a single-antenna eavesdropper and a set of multi-antenna relays (helpers) that act as jammers to the eavesdropper. Each helper knows only its own link to the receiver and independently transmits jamming noise, lying in the null space of its own link to the destination, thus causes no interference to the destination. The source, knowing the main channel explicitly and having statistical information on the eavesdropper channel, designs the Input Covariance Matrix so that the secrecy rate is maximized subject to an outage probability constraint and a sum power constraint. We show that the optimal Input Covariance Matrix has rank one. Assuming that the eavesdropper channels follow a zero-mean Gaussian distribution with known Covariance, the outage probability and outage secrecy rate are derived in closed form. Simulation results in support of the analysis are provided.
-
Closed Form Secrecy Capacity of MIMO Wiretap Channels with Two Transmit Antennas
arXiv: Information Theory, 2011Co-Authors: Athina P. PetropuluAbstract:A Gaussian multiple-Input multiple-output (MIMO) wiretap channel model is considered. The Input is a two-antenna transmitter, while the outputs are the legitimate receiver and an eavesdropper, both equipped with multiple antennas. All channels are assumed to be known. The problem of obtaining the optimal Input Covariance Matrix that achieves secrecy capacity subject to a power constraint is addressed, and a closed-form expression for the secrecy capacity is obtained.
-
GLOBECOM Workshops - On beamforming solution for secrecy capacity of MIMO wiretap channels
2011 IEEE GLOBECOM Workshops (GC Wkshps), 2011Co-Authors: Athina P. PetropuluAbstract:A Gaussian multiple-Input multiple-output (MIMO) wiretap channel model is considered, where there exists a transmitter, a legitimate receiver and an eavesdropper each equipped with multiple antennas. The problem of finding the optimal Input Covariance Matrix that achieves secrecy capacity subject to a power constraint is studied. In particular, it is shown that when the difference between the Grams of legitimate and eavesdropper channel matrices has all negative eigenvalues except one positive eigenvalue, beamforming is optimal. For that case, the secrecy capacity is obtained.
-
GLOBECOM - Ergodic Secrecy Rate for Gaussian MISO Wiretap Channels with Non-Trivial Covariance
2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010Co-Authors: Jiangyuan Li, Athina P. PetropuluAbstract:A Gaussian multiple-Input single-output (MISO) wiretap channel model is considered, where there exists a transmitter equipped with multiple antennas, a legitimate receiver and an eavesdropper each equipped with a single antenna. We study the problem of finding the optimal Input Covariance that achieves ergodic secrecy rate subject to a power constraint where the full information on the legitimate channel is known to the transmitter, but only statistical information on the eavesdropper channel is available at the transmitter. Existing results address the case in which the eavesdropper channel has independent and identically distributed Gaussian entries with zero-mean, i.e., the channel has trivial Covariance. This paper addresses the general case where eavesdropper channel has nontrivial Covariance. A set of equations describing the optimal Input Covariance Matrix are proposed. Based on this framework, we show that the optimal Input Covariance has always rank one. Numerical results are presented to illustrate the algorithm.
Young-cheol Park - One of the best experts on this subject based on the ideXlab platform.
-
Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field
EURASIP Journal on Audio Speech and Music Processing, 2017Co-Authors: Yonghyun Baek, Young-cheol ParkAbstract:In speech enhancement, noise power spectral density (PSD) estimation plays a key role in determining appropriate de-nosing gains. In this paper, we propose a robust noise PSD estimator for binaural speech enhancement in time-varying noise environments. First, it is shown that the noise PSD can be numerically obtained using an eigenvalue of the Input Covariance Matrix. A simplified estimator is then derived through an approximation process, so that the noise PSD is expressed as a combination of the second eigenvalue of the Input Covariance Matrix, the noise coherence, and the interaural phase difference (IPD) of the Input signal. Later, to enhance the accuracy of the noise PSD estimate in time-varying noise environments, an eigenvalue compensation scheme is presented, in which two eigenvalues obtained in noise-dominant regions are combined using a weighting parameter based on the speech presence probability (SPP). Compared with the previous prediction filter-based approach, the proposed method requires neither causality delays nor explicit estimation of the prediction errors. Finally, the proposed noise PSD estimator is applied to a binaural speech enhancement system, and its performance is evaluated through computer simulations. The simulation results show that the proposed noise PSD estimator yields accurate noise PSD regardless of the direction of the target speech signal. Therefore, slightly better performance in quality and intelligibility can be obtained than that with conventional algorithms.
-
ICASSP - Robust noise PSD estimation for binaural hearing aids in time-varying diffuse noise field
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Young-cheol Park, Dong-wook Kim, Jun-il SohnAbstract:In this paper, we present an unsupervised noise PSD estimation algorithm for binaural hearing aids in a time-varying diffuse noise field. It is shown that the noise PSD can be obtained from the eigenvalues of the Input Covariance Matrix together with the noise coherence function effective at low frequencies. To reduce the estimation bias due to fast smoothing, pre- and post-compensation methods are proposed. The proposed algorithm is able to track non-stationary noise PSD without tracking delay or underestimation problems. Its performance is independent of the target speech direction and Input SNR. Results of the objective parameter evaluation demonstrate the superiority of the proposed algorithm over conventional techniques.
-
ICCE - Real-time binaural noise reduction in diffuse noise field
2012 IEEE International Conference on Consumer Electronics (ICCE), 2012Co-Authors: Yonghyun Baek, Young-cheol Park, Dong-wook Kim, Jun-il ShonAbstract:In this paper, we present a real-time noise reduction algorithm for binaural sound devices operating in diffuse noise fields. The diffuse noise PSD is estimated in a form of the minimum eigenvalue of the (2×2) Input Covariance Matrix. To solve the under-estimation problem at low-frequencies, a compensation scheme is used. Also, to alleviate spectral bias due to smoothing, the estimated noise PSD is scaled by a smoothing factor. The proposed algorithm was implemented in real-time using a 16-bit programmable DSP core. Real-time implementation indicates that the algorithm requires less than 4MHz clock, so that it is suitable for wearable sound devices such as binaural hearing aids and headphones with noise reduction features.
-
EMBC - Noise reduction for binaural hearing aids using unsupervised diffuse noise estimator
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Young-cheol Park, Dong-wook Kim, Jun-il SohnAbstract:In this paper a new noise reduction algorithm for binaural hearing aids is proposed. This algorithm is capable of suppressing both nonstationary diffuse noise and unknown directional interferences without distorting the directional cues. For the estimation of the diffuse noise power spectral density (PSD), we utilize the eigenstructure of the 2×2 Input Covariance Matrix, together with a compensation process for preventing the underestimation at low frequencies. The interference PSD is estimated from the target cancelled Input signals through the signal prediction. Effectiveness of the proposed algorithm was confirmed according to the computer simulations in terms of noise reduction and binaural cue preservation.
Julien Dumont - One of the best experts on this subject based on the ideXlab platform.
-
On the Capacity Achieving Covariance Matrix for Rician MIMO Channels: An Asymptotic Approach
IEEE Transactions on Information Theory, 2010Co-Authors: Julien Dumont, Philippe Loubaton, Samson Lasaulce, Walid Hachem, Jamal NajimAbstract:In this paper, the capacity-achieving Input Covariance matrices for coherent block-fading correlated multiple Input multiple output (MIMO) Rician channels are determined. In contrast with the Rayleigh and uncorrelated Rician cases, no closed-form expressions for the eigenvectors of the optimum Input Covariance Matrix are available. Classically, both the eigenvectors and eigenvalues are computed numerically and the corresponding optimization algorithms remain computationally very demanding. In the asymptotic regime where the number of transmit and receive antennas converge to infinity at the same rate, new results related to the accuracy of the approximation of the average mutual information are provided. Based on the accuracy of this approximation, an attractive optimization algorithm is proposed and analyzed. This algorithm is shown to yield an effective way to compute the capacity achieving Matrix for the average mutual information and numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information.
-
on the capacity achieving Covariance Matrix for rician mimo channels an asymptotic approach
arXiv: Probability, 2007Co-Authors: Julien Dumont, Philippe Loubaton, Samson Lasaulce, Walid Hachem, Jamal NajimAbstract:The capacity-achieving Input Covariance matrices for coherent block-fading correlated MIMO Rician channels are determined. In this case, no closed-form expressions for the eigenvectors of the optimum Input Covariance Matrix are available. An approximation of the average mutual information is evaluated in this paper in the asymptotic regime where the number of transmit and receive antennas converge to $+\infty$. New results related to the accuracy of the corresponding large system approximation are provided. An attractive optimization algorithm of this approximation is proposed and we establish that it yields an effective way to compute the capacity achieving Covariance Matrix for the average mutual information. Finally, numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information, while being much more computationally attractive.
-
on the capacity achieving transmit Covariance matrices of mimo correlated rician channels a large system approach
arXiv: Information Theory, 2006Co-Authors: Julien Dumont, Philippe Loubaton, Samson LasaulceAbstract:We determine the capacity-achieving Input Covariance matrices for coherent block-fading correlated MIMO Rician channels. In contrast with the Rayleigh and uncorrelated Rician cases, no closed-form expressions for the eigenvectors of the optimum Input Covariance Matrix are available. Both the eigenvectors and eigenvalues have to be evaluated by using numerical techniques. As the corresponding optimization algorithms are not very attractive, we evaluate the limit of the average mutual information when the number of transmit and receive antennas converge to infinity at the same rate. If the channel is semi-correlated, we propose an attractive optimization algorithm of the large system approximant, and establish some convergence results. Simulation results show that our approach provide reliable results even for a quite moderate number of transmit and receive antennas.
-
GLOBECOM - CTH09-5: On the Capacity Achieving Transmit Covariance Matrices of Mimo Correlated Rician Channels: A Large System Approach
IEEE Globecom 2006, 2006Co-Authors: Julien Dumont, Philippe Loubaton, Samson LasaulceAbstract:We determine the capacity-achieving Input Covariance matrices for coherent block-fading correlated MIMO Rician channels. In contrast with the Rayleigh and uncorrelated Rician cases, no closed-form expressions for the eigenvectors of the optimum Input Covariance Matrix are available. Both the eigenvectors and eigenvalues have to be evaluated by using numerical techniques. As the corresponding optimization algorithms are not very attractive, we evaluate the limit of the average mutual information when the number of transmit and receive antennas converge to +infin at the same rate. We propose an attractive optimization algorithm of the large system approximant, and establish some convergence results. Numerical simulation results show that, even for a quite moderate number of transmit and receive antennas, the new approach provides the same results than direct maximization approaches of the average mutual information, while being much more computationally attractive.
