Results 21  30
of
48
Bandwidth Extension in CMOS with Optimized OnChip Inductors
 IEEE Journal of SolidState Circuits
, 2000
"... We present a technique for enhancing the bandwidth of gigahertz broadband circuitry by using optimized onchip spiral inductors as shuntpeaking elements. The series resistance of the onchip inductor is incorporated as part of the load resistance to permit a large inductance to be realized with mi ..."
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Cited by 12 (3 self)
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We present a technique for enhancing the bandwidth of gigahertz broadband circuitry by using optimized onchip spiral inductors as shuntpeaking elements. The series resistance of the onchip inductor is incorporated as part of the load resistance to permit a large inductance to be realized with minimum area and capacitance. Simple, accurate inductance expressions are used in a lumped circuit inductor model to allow the passive and active components in the circuit to be simultaneously optimized. A quick and efficient global optimization method, based on geometric programming, is discussed. The bandwidth extension technique is applied in the implementation of a 2.125Gbaud preamplifier that employs a commongate input stage followed by a cascoded commonsource stage. Onchip shunt peaking is introduced at the dominant pole to improve the overall system performance, including a 40% increase in the transimpedance. This implementation achieves a 1.6k\Omega transimpedance and a 0.6 A i...
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 9 (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.
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 8 (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 8 (5 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.
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 7 (4 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
Power minimization under realtime source distortion constraints in wireless networks
 in IEEE Wireless Communications and Networking Conference (WCNC
, 2003
"... Abstract—In multiple access wireless networks with cochannel interference, allocating resources such as transmitted powers and source rates is a task critical to improve performance. In this paper, we introduce a new technique aimed at minimizing the overall transmitted power subject to constraints ..."
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Cited by 7 (6 self)
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Abstract—In multiple access wireless networks with cochannel interference, allocating resources such as transmitted powers and source rates is a task critical to improve performance. In this paper, we introduce a new technique aimed at minimizing the overall transmitted power subject to constraints on the incurred source distortion. The technique is based on the use of realtime source codecs with externally adaptable output rate and Rate Compatible Punctured Convolutional (RCPC) channel encoders. We develop an adaptive algorithm to find the optimal power and source rate allocation to the different users according to their channel conditions and under distortion constraints. We present simulation results to show that it is possible to reduce the overall transmitted power significantly with a relatively small distortion increases for some users, and while keeping the network average distortion unchanged. I.
Antenna Array Signal Processing for High Rank Data Models
, 1999
"... This thesis deals with spatial signal processing using an antenna array, for a class of data models with a high rank signal contribution, corresponding to a channel with multipath propagation. ..."
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Cited by 5 (2 self)
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This thesis deals with spatial signal processing using an antenna array, for a class of data models with a high rank signal contribution, corresponding to a channel with multipath propagation.
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 4 (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.
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 4 (3 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
Multiuser Precoders for Fixed Receivers
 IN PROC. OF IEEE INT. ZURICH SEMINAR (IZS2004
, 2004
"... We consider the problem of designing precoders for multiuser systems using fixed receivers. We first derive a precoder that minimizes the transmitted power subject to signal to interference plus noise ratio (SINR) constraints, and then derive a precoder that maximizes the worst case SINR subject to ..."
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Cited by 3 (1 self)
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We consider the problem of designing precoders for multiuser systems using fixed receivers. We first derive a precoder that minimizes the transmitted power subject to signal to interference plus noise ratio (SINR) constraints, and then derive a precoder that maximizes the worst case SINR subject to a power constraint. We show that both problems can be solved using standard optimization packages. In contrast to most of the existing precoders, our precoder design is not limited to full rank systems, and is applicable to systems in which the number of users is smaller, equal or larger than the processing gain. Simulation results in a multiuser system show that the proposed precoders can significantly outperform existing linear precoders.