Results 11  20
of
1,525
A unified framework for optimizing linear nonregenerative multicarrier MIMO relay communication systems
 IEEE TRANS. SIGNAL PROCESS
, 2009
"... In this paper, we develop a unified framework for linear nonregenerative multicarrier multipleinput multipleoutput (MIMO) relay communications in the absence of the direct source–destination link. This unified framework classifies most commonly used design objectives such as the minimal meansqu ..."
Abstract

Cited by 93 (50 self)
 Add to MetaCart
(Show Context)
In this paper, we develop a unified framework for linear nonregenerative multicarrier multipleinput multipleoutput (MIMO) relay communications in the absence of the direct source–destination link. This unified framework classifies most commonly used design objectives such as the minimal meansquare error and the maximal mutual information into two categories: Schurconcave and Schurconvex functions. We prove that for Schurconcave objective functions, the optimal source precoding matrix and relay amplifying matrix jointly diagonalize the source–relay–destination channel matrix and convert the multicarrier MIMO relay channel into parallel singleinput singleoutput (SISO) relay channels. While for Schurconvex objectives, such joint diagonalization occurs after a specific rotation of the source precoding matrix. After the optimal structure of the source and relay matrices is determined, the linear nonregenerative relay design problem boils down to the issue of power loading among the resulting SISO relay channels. We show that this power loading problem can be efficiently solved by an alternating technique. Numerical examples demonstrate the effectiveness of the proposed framework.
Gradient of mutual information in linear vector Gaussian channels
 IEEE Trans. Inf. Theory
, 2006
"... Abstract — This paper considers a general linear vector Gaussian channel with arbitrary signaling and pursues two closely related goals: i) closedform expressions for the gradient of the mutual information with respect to arbitrary parameters of the system, and ii) fundamental connections between i ..."
Abstract

Cited by 93 (13 self)
 Add to MetaCart
(Show Context)
Abstract — This paper considers a general linear vector Gaussian channel with arbitrary signaling and pursues two closely related goals: i) closedform expressions for the gradient of the mutual information with respect to arbitrary parameters of the system, and ii) fundamental connections between information theory and estimation theory. Generalizing the fundamental relationship recently unveiled by Guo, Shamai, and Verdú [1], we show that the gradient of the mutual information with respect to the channel matrix is equal to the product of the channel matrix and the error covariance matrix of the estimate of the input given the output. I.
An empirical bayesian strategy for solving the simultaneous sparse approximation problem
 IEEE Trans. Sig. Proc
, 2007
"... Abstract—Given a large overcomplete dictionary of basis vectors, the goal is to simultaneously represent 1 signal vectors using coefficient expansions marked by a common sparsity profile. This generalizes the standard sparse representation problem to the case where multiple responses exist that were ..."
Abstract

Cited by 87 (14 self)
 Add to MetaCart
(Show Context)
Abstract—Given a large overcomplete dictionary of basis vectors, the goal is to simultaneously represent 1 signal vectors using coefficient expansions marked by a common sparsity profile. This generalizes the standard sparse representation problem to the case where multiple responses exist that were putatively generated by the same small subset of features. Ideally, the associated sparse generating weights should be recovered, which can have physical significance in many applications (e.g., source localization). The generic solution to this problem is intractable and, therefore, approximate procedures are sought. Based on the concept of automatic relevance determination, this paper uses an empirical Bayesian prior to estimate a convenient posterior distribution over candidate basis vectors. This particular approximation enforces a common sparsity profile and consistently places its prominent posterior mass on the appropriate region of weightspace necessary for simultaneous sparse recovery. The resultant algorithm is then compared with multiple response extensions of matching pursuit, basis pursuit, FOCUSS, and Jeffreys priorbased Bayesian methods, finding that it often outperforms the others. Additional motivation for this particular choice of cost function is also provided, including the analysis of global and local minima and a variational derivation that highlights the similarities and differences between the proposed algorithm and previous approaches. Index Terms—Automatic relevance determination, empirical Bayes, multiple response models, simultaneous sparse approximation, sparse Bayesian learning, variable selection. I.
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
"... Highrate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Trainingbased methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most co ..."
Abstract

Cited by 84 (9 self)
 Add to MetaCart
(Show Context)
Highrate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Trainingbased methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most commonly used to accomplish this task. Traditional trainingbased channel estimation methods, typically comprising of linear reconstruction techniques, are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that many wireless channels encountered in practice tend to exhibit a sparse multipath structure that gets pronounced as the signal space dimension gets large (e.g., due to large bandwidth or large number of antennas). In this paper, we formalize the notion of multipath sparsity and present a new approach to estimating sparse (or effectively sparse) multipath channels that is based on some of the recent advances in the theory of compressed sensing. In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing, can potentially achieve a target reconstruction error using far less energy and, in many instances, latency and bandwidth than that dictated by the traditional leastsquaresbased training methods.
C.: Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation
 IEEE Trans. Audio, Speech, Language Process
, 2010
"... We consider inference in a general datadriven objectbased model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the ItakuraSaito divergence, wh ..."
Abstract

Cited by 78 (17 self)
 Add to MetaCart
(Show Context)
We consider inference in a general datadriven objectbased model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the ItakuraSaito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectationmaximization algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance. Index Terms — Multichannel audio, nonnegative matrix factorization, nonnegative tensor factorization, underdetermined convolutive blind source separation. 1.
Distributed compressionestimation using wireless sensor networks  The design goals of performance, bandwidth efficiency, scalability, and robustness
 IEEE SIGNAL PROCESSING MAG
, 2006
"... A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advance ..."
Abstract

Cited by 78 (1 self)
 Add to MetaCart
(Show Context)
A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advanced signal processing tasks with unprecedented robustness and versatility, thus making WSN an attractive lowcost technology for a wide range of remote sensing and environmental monitoring applications [1], [32].
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery
"... Abstract—This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown e ..."
Abstract

Cited by 76 (36 self)
 Add to MetaCart
Abstract—This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for nonnegativity and fulladditivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images. Index Terms—Bayesian inference, endmember extraction, hyperspectral imagery, linear spectral unmixing, MCMC methods. I.
The FiniteLength MultiInput MultiOutput MMSE–DFE
 IEEE Tr. SigProc
, 2000
"... Abstract—A new theoretical framework is introduced for analyzing the performance of a finitelength minimummeansquareerror decision feedback equalizer (MMSEDFE) in a multiinput multioutput (MIMO) environment. The framework includes transmit and receive diversity systems as special cases and q ..."
Abstract

Cited by 72 (3 self)
 Add to MetaCart
(Show Context)
Abstract—A new theoretical framework is introduced for analyzing the performance of a finitelength minimummeansquareerror decision feedback equalizer (MMSEDFE) in a multiinput multioutput (MIMO) environment. The framework includes transmit and receive diversity systems as special cases and quantifies the diversity performance improvement as a function of the number of transmit/receive antennas and equalizer taps. Fast and parallelizable algorithms for computing the finitelength MIMO MMSEDFE are presented for three common multiuser detection scenarios. Index Terms—Equalizers, FIR digital filters, mean square error methods, MIMO systems. I.
A Kalman Filterbased Algorithm for IMUCamera Calibration
, 2007
"... Visionaided Inertial Navigation Systems (VINS) can provide precise state estimates for the 3D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an IMU with visual observations from a camera under the assumption that ..."
Abstract

Cited by 72 (14 self)
 Add to MetaCart
Visionaided Inertial Navigation Systems (VINS) can provide precise state estimates for the 3D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an IMU with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMUcamera calibration process causes biases that reduce the accuracy of the estimation process and can even lead to divergence. In this paper, we present a Kalman filterbased algorithm for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlations of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3D laser scanner) except a calibration target. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.
A closedform location estimator for use with room environment microphone arrays. Speech and Audio Processing
 IEEE Transactions on
, 1997
"... ..."