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389
Unitary SpaceTime Modulation for MultipleAntenna Communications in Rayleigh Flat Fading
 IEEE Trans. Inform. Theory
, 1998
"... Motivated by informationtheoretic considerations, we propose a signalling scheme, unitary spacetime modulation, for multipleantenna communication links. This modulation is ideally suited for Rayleigh fastfading environments, since it does not require the receiver to know or learn the propagation ..."
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Cited by 307 (19 self)
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Motivated by informationtheoretic considerations, we propose a signalling scheme, unitary spacetime modulation, for multipleantenna communication links. This modulation is ideally suited for Rayleigh fastfading environments, since it does not require the receiver to know or learn the propagation coefficients. Unitary spacetime modulation uses constellations of T \cross M spacetime signals {\Phi_l, l= 1,...L},where T represents the coherence interval during which the fading is approximately constant, and M > M . We design some multipleantenna signal constellations and simulate their effectiveness as measured by bit error probability with maximum likelihood decoding. We demonstrate that two antennas have a 6dB diversity gain over one antenna at 15db SNR.
A Sparse Signal Reconstruction Perspective for Source Localization With Sensor Arrays
, 2005
"... We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the 1norm. A number of recent theoretical results on sparsifying properties of ..."
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Cited by 222 (6 self)
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We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the 1norm. A number of recent theoretical results on sparsifying properties of 1 penalties justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum that exhibits superresolution. We propose to use the singular value decomposition (SVD) of the data matrix to summarize multiple time or frequency samples. Our formulation leads to an optimization problem, which we solve efficiently in a secondorder cone (SOC) programming framework by an interior point implementation. We propose a grid refinement method to mitigate the effects of limiting estimates to a grid of spatial locations and introduce an automatic selection criterion for the regularization parameter involved in our approach. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Cramér–Rao bound (CRB). We observe that our approach has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources, as well as not requiring an accurate initialization.
Systematic design of unitary spacetime constellations
 IEEE TRANS. INFORM. THEORY
, 2000
"... We propose a systematic method for creating constellations of unitary space–time signals for multipleantenna communication links. Unitary space–time signals, which are orthonormal in time across the antennas, have been shown to be welltailored to a Rayleigh fading channel where neither the transm ..."
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Cited by 198 (11 self)
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We propose a systematic method for creating constellations of unitary space–time signals for multipleantenna communication links. Unitary space–time signals, which are orthonormal in time across the antennas, have been shown to be welltailored to a Rayleigh fading channel where neither the transmitter nor the receiver knows the fading coefficients. The signals can achieve low probability of error by exploiting multipleantenna diversity. Because the fading coefficients are not known, the criterion for creating and evaluating the constellation is nonstandard and differs markedly from the familiar maximumEuclideandistance norm. Our construction begins with the first signal in the constellation—an oblong complexvalued matrix whose columns are orthonormal—and systematically produces the remaining signals by successively rotating this signal in a highdimensional complex space. This construction easily produces large constellations of highdimensional signals. We demonstrate its efficacy through examples involving one, two, and three transmitter antennas.
Persurvivor processing: A general approach to MLSE in uncertain environments
 IEEE Trans. Communications
, 1995
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A SelfLocalization Method for Wireless Sensor Networks
 EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
, 2002
"... We consider the problem of locating and orienting a network of unattended sensor nodes that have been deployed in a scene at unknown locations and orientation angles. This selfcalibration problem is solved by placing a number of source signals, also with unknown locations, in the scene. Each sou ..."
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Cited by 115 (5 self)
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We consider the problem of locating and orienting a network of unattended sensor nodes that have been deployed in a scene at unknown locations and orientation angles. This selfcalibration problem is solved by placing a number of source signals, also with unknown locations, in the scene. Each source in turn emits a calibration signal, and a subset of sensor nodes in the network measures the timeofarrival and directionofarrival (with respect to the sensor node's local orientation coordinates) of the signal emitted from that source. From these measurements we compute the sensor node locations and orientations, along with any unknown source locations and emission times. We develop necessary conditions for solving the selfcalibration problem and provide a maximum likelihood solution and corresponding location error estimate. We also compute the CramerRao Bound of the sensor node location and orientation estimates, which provides a lower bound on calibration accuracy. Results using both synthetic data and field measurements are presented.
Video Orbits of the Projective Group: A Simple Approach to Featureless Estimation of Parameters
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 1997
"... We present direct featureless methods for estimating the eight parameters of an "exact" projective (homographic) coordinate transformation to register pairs of images, together with the application of seamlessly combining a plurality of images of the same scene, resulting in a single image ..."
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Cited by 102 (9 self)
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We present direct featureless methods for estimating the eight parameters of an "exact" projective (homographic) coordinate transformation to register pairs of images, together with the application of seamlessly combining a plurality of images of the same scene, resulting in a single image (or new image sequence) of greater resolution or spatial extent. The approach is "exact" for two cases of static scenes: 1) images taken from the same location of an arbitrary threedimensional (3D) scene, with a camera that is free to pan, tilt, rotate about its optical axis, and zoom, or 2) images of a flat scene taken from arbitrary locations. The featureless projective approach generalizes interframe camera motion estimation methods that have previously used an affine model (which lacks the degrees of freedom to "exactly" characterize such phenomena as camera pan and tilt) and/or which have relied upon finding points of correspondence between the image frames. The featureless projective approach...
Sequential karhunenloeve basis extraction and its application to images
 IEEE Trans. On Image Processing
, 2000
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Wireless systems and interference avoidance
 IEEE Trans. Wireless Commun
, 2002
"... Abstract—Motivated by the emergence of programmable radios, we seek to understand a new class of communication system where pairs of transmitters and receivers can adapt their modulation/demodulation method in the presence of interference to achieve better performance. Using signal to interference r ..."
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Cited by 74 (12 self)
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Abstract—Motivated by the emergence of programmable radios, we seek to understand a new class of communication system where pairs of transmitters and receivers can adapt their modulation/demodulation method in the presence of interference to achieve better performance. Using signal to interference ratio as a metric and a general signal space approach, we present a class of iterative distributed algorithms for synchronous systems which results in an ensemble of optimal waveforms for multiple users connected to a common receiver (or colocated independent receivers). That is, the waveform ensemble meets the Welch Bound with equality and, therefore, achieves minimum average interference over the ensemble of signature waveforms. We derive fixed points for a number of scenarios, provide examples, look briefly at ensemble stability under user addition and deletion as well as provide a simplistic comparison to synchronous codedivision multipleaccess. We close with suggestions for future work. Index Terms—Adaptive modulation, codedivision multipleaccess systems, codeword optimization, interference avoidance, multiuser
On the bit error rate of lightwave systems with optical amplifiers
 J. Lightwave Technol
, 1991
"... We revisit the problem of evaluating the performances of communication systems with optical amplifiers and a wideband optical filter. We compute the exact probability of error and the optimal threshold and compare them with those predicted by Gaussian approximations for ASK, FSK or DPSK modulations, ..."
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Cited by 67 (1 self)
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We revisit the problem of evaluating the performances of communication systems with optical amplifiers and a wideband optical filter. We compute the exact probability of error and the optimal threshold and compare them with those predicted by Gaussian approximations for ASK, FSK or DPSK modulations, both for ideal photodetectors and for the case where shot noise is significant. 1
A variational formulation for framebased inverse problems
 Inverse Problems
, 2007
"... A convex variational framework is proposed for solving inverse problems in Hilbert spaces with a priori information on the representation of the target solution in a frame. The objective function to be minimized consists of a separable term penalizing each frame coefficient individually and of a smo ..."
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Cited by 62 (24 self)
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A convex variational framework is proposed for solving inverse problems in Hilbert spaces with a priori information on the representation of the target solution in a frame. The objective function to be minimized consists of a separable term penalizing each frame coefficient individually and of a smooth term modeling the data formation model as well as other constraints. Sparsityconstrained and Bayesian formulations are examined as special cases. A splitting algorithm is presented to solve this problem and its convergence is established in infinitedimensional spaces under mild conditions on the penalization functions, which need not be differentiable. Numerical simulations demonstrate applications to framebased image restoration. 1