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410
How Much Training is Needed in MultipleAntenna Wireless Links?
 IEEE Trans. Inform. Theory
, 2000
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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.
Issues in Evolutionary Robotics
, 1992
"... In this paper we propose and justify a methodology for the development of the control systems, or `cognitive architectures', of autonomous mobile robots. We argue that the design by hand of such control systems becomes prohibitively difficult as complexity increases. We discuss an alternative a ..."
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Cited by 268 (32 self)
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In this paper we propose and justify a methodology for the development of the control systems, or `cognitive architectures', of autonomous mobile robots. We argue that the design by hand of such control systems becomes prohibitively difficult as complexity increases. We discuss an alternative approach, involving artificial evolution, where the basic building blocks for cognitive architectures are adaptive noisetolerant dynamical neural networks, rather than programs. These networks may be recurrent, and should operate in real time. Evolution should be incremental, using an extended and modified version of genetic algorithms. We nally propose that, sooner rather than later, visual processing will be required in order for robots to engage in nontrivial navigation behaviours. Time constraints suggest that initial architecture evaluations should be largely done in simulation. The pitfalls of simulations compared with reality are discussed, together with the importance of incorporating noise. To support our claims and proposals, we present results from some preliminary experiments where robots which roam officelike environments are evolved.
Identification and deconvolution of multichannel linear nonGaussian processes using higher order statistics and inverse filter criteria
 IEEE Trans. Signal Process
, 1997
"... Abstract—This paper is concerned with the problem of estimation and deconvolution of the matrix impulse response function of a multipleinput multipleoutput (MIMO) system given only the measurements of the vector output of the system. The system is assumed to be driven by a temporally i.i.d. and s ..."
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Cited by 47 (1 self)
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Abstract—This paper is concerned with the problem of estimation and deconvolution of the matrix impulse response function of a multipleinput multipleoutput (MIMO) system given only the measurements of the vector output of the system. The system is assumed to be driven by a temporally i.i.d. and spatially independent nonGaussian vector sequence (which is not observed). An iterative, inverse filter criteriabased approach is developed using the thirdorder or the fourthorder normalized cumulants of the inverse filtered data at zero lag. Stationary points of the proposed cost functions are investigated. The approach is input iterative, i.e., the input sequences are extracted and removed one by one. The matrix impulse response is then obtained by cross correlating the extracted inputs with the observed outputs. Identifiability conditions are analyzed. Strong consistency of the proposed approach is also briefly discussed. Computer simulation examples are presented to illustrate the proposed approaches. I.
Maximum Likelihood Methods in Radar Array Signal Processing
, 1997
"... We consider robust and computationally efficient maximum likelihood algorithms for estimating the parameters of a radar target whose signal is observed by an array of sensors in interference with unknown second order spatial statistics. Two data models are described, one that uses the target directi ..."
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Cited by 40 (3 self)
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We consider robust and computationally efficient maximum likelihood algorithms for estimating the parameters of a radar target whose signal is observed by an array of sensors in interference with unknown second order spatial statistics. Two data models are described, one that uses the target direction of arrival (DOA) and signal amplitude as parameters, and the other a simpler unstructured model that uses a generic target "spatial signature." An extended invariance principle is invoked to show how the less accurate maximum likelihood estimates obtained from the simple model may be refined to asymptotically achieve the performance available using the structured model. The resulting algorithm requires two 1D searches rather than a 2D search, as with previous approaches for the structured case. If a uniform linear array is used, only a single 1D search is needed. A generalized likelihood ratio test for target detection is also derived under the unstructured model. The principal advanta...
An Algorithm For Joint Symbol Timing And Channel Estimation For OFDM Systems
"... In this paper, we consider the problem of joint syuchronization and channel estimation for orthogonal frequency division multiplexing (OFDM) systems. A new algorithm is proposed that estimates the channel and symbol timing simultaneously by using a technique based on maximumlikelihood (ML) theory a ..."
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Cited by 32 (1 self)
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In this paper, we consider the problem of joint syuchronization and channel estimation for orthogonal frequency division multiplexing (OFDM) systems. A new algorithm is proposed that estimates the channel and symbol timing simultaneously by using a technique based on maximumlikelihood (ML) theory and the generalized Akaike information criterion (GAIC). Finally, we demonstrate the performance of our algorithm by simulation results.
The Ricean K Factor: Estimation and Performance Analysis
 IEEE Transactions on Wireless Communications
, 2003
"... In wireless communications, the relative strength of the direct and scattered components of the received signal, as expressed by the Ricean factor, provides an indication of link quality. Accordingly, efficient and accurate methods for estimating are of considerable interest. In this paper, we propo ..."
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Cited by 29 (0 self)
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In wireless communications, the relative strength of the direct and scattered components of the received signal, as expressed by the Ricean factor, provides an indication of link quality. Accordingly, efficient and accurate methods for estimating are of considerable interest. In this paper, we propose a general class of momentbased estimators which use the signal envelope. This class of estimators unifies many of the previous estimators, and introduces new ones. We derive, for the first time, the asymptotic variance (AsV) of these estimators and compare them with the CramrRao bound (CRB). We then tackle the problem of estimating from the inphase and quadraturephase (I/Q) components of the received signal and illustrate the improvement in performance as compared with the envelopebased estimators. We derive the CRBs for the I/Q data model, which, unlike the envelope CRB, is tractable for correlated samples. Furthermore, we introduce a novel estimator that relies on the I/Q components, and derive its AsV even when the channel samples are correlated. We corroborate our analytical findings by simulations.
Software for weighted structured lowrank approximation
"... A software package is presented that computes locally optimal solutions to lowrank approximation problems with the following features: • mosaic Hankel structure constraint on the approximating matrix, • weighted 2norm approximation criterion, • fixed elements in the approximating matrix, • missing ..."
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Cited by 28 (17 self)
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A software package is presented that computes locally optimal solutions to lowrank approximation problems with the following features: • mosaic Hankel structure constraint on the approximating matrix, • weighted 2norm approximation criterion, • fixed elements in the approximating matrix, • missing elements in the data matrix, and • linear constraints on an approximating matrix’s left kernel basis. It implements a variable projection type algorithm and allows the user to choose standard local optimization methods for the solution of the parameter optimization problem. For an m×n data matrix, with n>m, the computational complexity of the cost function and derivative evaluation is O(m2n). The package is suitable for applications with n ≫ m. In statistical estimation and data modeling—the main application areas of the package—n ≫ m corresponds to modeling of large amount of data by a lowcomplexity model. Performance results on benchmark system identification problems from the database DAISY and approximate common divisor problems are presented.
Analysis of the asymptotic properties of the MOESP type of subspace algorithms
, 2000
"... The MOESP type of subspace algorithms are used for the identification of linear, discrete time, finite dimensional state space systems. They are based on the geometric structure of covariance matrices and exploit the properties of the state vector extensively. In this paper the asymptotic properties ..."
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Cited by 27 (2 self)
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The MOESP type of subspace algorithms are used for the identification of linear, discrete time, finite dimensional state space systems. They are based on the geometric structure of covariance matrices and exploit the properties of the state vector extensively. In this paper the asymptotic properties of the algorithms are examined. The main results include consistency and asymptotic normality for the estimates of the system matrices, under suitable assumptions on the noise sequence, the input process and the underlying true system.