Results 21  30
of
72
Optimal Power Control in Interference Limited Fading Wireless Channels with Outage Probability Specifications
, 2000
"... We propose a new method of power control for interference limited wireless networks with Rayleigh fading of both the desired and interference signals. Our method explictly takes into account the statistical variation of both the received signal and interference power, and optimally allocates powe ..."
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Cited by 26 (2 self)
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We propose a new method of power control for interference limited wireless networks with Rayleigh fading of both the desired and interference signals. Our method explictly takes into account the statistical variation of both the received signal and interference power, and optimally allocates power subject to constraints on the probability of fading induced outage for each transmitter/receiver pair. We establish several results for this type of problem. For the case
Design of robust global power and ground networks
 In ISPD
, 2001
"... We consider the problem of determining optimal wire widths for a power or ground network, subject to limits on wire widths, voltage drops, total wire area, current density, and power dissipation. To account for the variation of the current demand, we model it as a random vector with known statistics ..."
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Cited by 18 (4 self)
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We consider the problem of determining optimal wire widths for a power or ground network, subject to limits on wire widths, voltage drops, total wire area, current density, and power dissipation. To account for the variation of the current demand, we model it as a random vector with known statistics, possibly including correlation between subsystem currents. Other researchers have shown that when the variation in the current is not taken into account, the optimal network topology is a tree. A tree topology is, however, almost never used in practice, because it is not robust with respect to variations in the block currents. We show that when the current variation is taken into account, the optimal network is usually not a tree. We formulate a heuristic method based on minimizing a linear combination of total average power and total wire area. We show that this results in designs that obey the reliability constraints, occupy small area, and most importantly are robust against variations in block currents. The problem can be formulated as a nonlinear convexoptimization problem that can be globally solved very efficiently.
On Constant Power Waterfilling
 IEEE ICC
, 2001
"... This paper derives a rigorous performance bound for the constantpower waterfilling algorithm for ISI channels with multicarrier modulation and for i.i.d. fading channels with adaptive modulation. Based on the performance bound, a verylow complexity logarithmfree power allocation algorithm is pro ..."
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Cited by 17 (0 self)
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This paper derives a rigorous performance bound for the constantpower waterfilling algorithm for ISI channels with multicarrier modulation and for i.i.d. fading channels with adaptive modulation. Based on the performance bound, a verylow complexity logarithmfree power allocation algorithm is proposed. Theoretical worstcase analysis and simulation show that the approximate waterfilling scheme is close to optimal. I.
Symmetric Capacity of MIMO Downlink Channels
"... This paper studies the symmetric capacity of the MIMO downlink channel, which is defined to be the maximum rate that can be allocated to every receiver in the system. The symmetric capacity represents absolute fairness and is an important metric for slowly fading channels in which users have symme ..."
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Cited by 15 (0 self)
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This paper studies the symmetric capacity of the MIMO downlink channel, which is defined to be the maximum rate that can be allocated to every receiver in the system. The symmetric capacity represents absolute fairness and is an important metric for slowly fading channels in which users have symmetric rate demands. An efficient and provably convergent algorithm for computing the symmetric capacity is proposed, and it is shown that a simple modification of the algorithm can be used to compute the minimum power required to meet given downlink rate demands. In addition, the difference between the symmetric and sum capacity, termed the fairness penalty, is studied. Exact analytical results for the fairness penalty at high SNR are provided for the 2 user downlink channel, and numerical results are given for channels with more users.
likelihood estimation in linear models with Gaussian model matrix
 IEEE Signal Process. Lett
"... Abstract—We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a Gaussian model matrix. We derive the maximum likelihood (ML) estimator for this problem and show that it can be found using a simple linesearch over a unimodal function that can be effi ..."
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Cited by 12 (3 self)
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Abstract—We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a Gaussian model matrix. We derive the maximum likelihood (ML) estimator for this problem and show that it can be found using a simple linesearch over a unimodal function that can be efficiently evaluated. We then discuss the similarity between the ML, the total least squares (TLS), the regularized TLS, and the expected least squares estimators. Index Terms—Errors in variables (EIV), linear models, maximum likelihood (ML) estimation, random model matrix, total least squares (TLS). I.
Covariance estimation in decomposable Gaussian graphical models
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2010
"... Graphical models are a framework for representing and exploiting prior conditional independence structures within distributions using graphs. In the Gaussian case, these models are directly related to the sparsity of the inverse covariance (concentration) matrix and allow for improved covariance es ..."
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Cited by 12 (7 self)
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Graphical models are a framework for representing and exploiting prior conditional independence structures within distributions using graphs. In the Gaussian case, these models are directly related to the sparsity of the inverse covariance (concentration) matrix and allow for improved covariance estimation with lower computational complexity. We consider concentration estimation with the meansquared error (MSE) as the objective, in a special type of model known as decomposable. This model includes, for example, the well known banded structure and other cases encountered in practice. Our first contribution is the derivation and analysis of the minimum variance unbiased estimator (MVUE) in decomposable graphical models. We provide a simple closed form solution to the MVUE and compare it with the classical maximum likelihood estimator (MLE) in terms of performance and complexity. Next, we extend the celebrated Stein’s unbiased risk estimate (SURE) to graphical models. Using SURE, we prove that the MSE of the MVUE is always smaller or equal to that of the biased MLE, and that the MVUE itself is dominated by other approaches. In addition, we propose the use of SURE as a constructive mechanism for deriving new covariance estimators. Similarly to the classical MLE, all of our proposed estimators have simple closed form solutions but result in a significant reduction in MSE.
Decomposable Principal Component Analysis
"... Abstract—In this paper, we consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute PCA computation. For this purpose, we reformulate the PCA problem in the sparse inverse covariance (concentration) ..."
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Cited by 10 (6 self)
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Abstract—In this paper, we consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute PCA computation. For this purpose, we reformulate the PCA problem in the sparse inverse covariance (concentration) domain and address the global eigenvalue problem by solving a sequence of local eigenvalue problems in each of the cliques of the decomposable graph. We illustrate our methodology in the context of decentralized anomaly detection in the Abilene backbone network. Based on the topology of the network, we propose an approximate statistical graphical model and distribute the computation of PCA. Index Terms—Anomaly detection, graphical models, principal
Optimal allocation of local feedback in multistage amplifiers via geometric programming
 IEEE Transactions on Circuits and Systems I
, 2001
"... We consider the problem of optimally allocating local feedback to the stages of a multistage amplifier. The local feedback gains affect many performance indices for the overall amplifier, such as bandwidth, gain, risetime, delay, output signal swing, linearity, and noise performance, in a complicat ..."
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Cited by 10 (5 self)
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We consider the problem of optimally allocating local feedback to the stages of a multistage amplifier. The local feedback gains affect many performance indices for the overall amplifier, such as bandwidth, gain, risetime, delay, output signal swing, linearity, and noise performance, in a complicated and nonlinear fashion, making optimization of the feedback gains a challenging problem. In this paper we show that this problem, though complicated and nonlinear, can be formulated as a special type of optimization problem called geometric programming. Geometric programs can be solved globally and efficiently using recently developed interiorpoint methods. Our method therefore gives a complete solution to the problem of optimally allocating local feedback gains, taking into account a wide variety of constraints. 1 1