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检索条件"主题词=Semidefinite Programming"

共 2467 条记录，以下是2441-2450 订阅

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A CONVEX RELAXATION FOR APPROXIMATE MAXIMUM-LIKELIHOOD 2D SOURCE LOCALIZATION FROM RANGE MEASUREMENTS

A CONVEX RELAXATION FOR APPROXIMATE MAXIMUM-LIKELIHOOD 2D SO...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Pinar Oguz-Ekim Joao Gomes Joao Xavier Paulo Oliveira Institute for Systems and Robotics-Instituto Superior Tecnico Department of Electrical and Computer Engineering Lisboa Portugal

ISBN: (纸本)9781424442959

This paper addresses the problem of locating a single source from noisy range measurements in wireless sensor networks. An approximate solution to the maximum likelihood location estimation problem is proposed, by redefining the problem in the complex plane and relaxing the minimization problem into semidefinite programming form. Existing methods solve the source localization problem either by minimizing the maximum likelihood function iteratively or exploiting other semidefinite programming relaxations. In addition, using squared range measurements, exact and approximate least squares solutions can be calculated. Our relaxation for source localization in the complex plane (SLCP) is motivated by the near-convexity of the objective function and constraints in the complex formulation of the original (non-relaxed) problem. Simulation results indicate that the SLCP algorithm outperforms existing methods in terms of accuracy, particularly in the presence of outliers and when the number of anchors is larger than three.

关键词： Single source localization maximum likelihood estimation nonconvex and nonsmooth minimization semidefinite programming maximum likelihood estimation Position measurement Ranging measurements Complex Plane maximum likelihood relaxation Boat anchors outliers Objective function Wireless sensor networks

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Sum Secrecy Rate in MISO Full-Duplex Wiretap Channel with Imperfect CSI

Sum Secrecy Rate in MISO Full-Duplex Wiretap Channel with Im...

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IEEE Globecom Workshops

作者： Sanjay Vishwakarma A. Chockalingam Department of ECE Indian Institute of Science Bangalore 560012 India

ISBN: (纸本)9781467395274

In this paper, we consider the achievable sum secrecy rate in MISO (multiple-input-single-output) full-duplex wiretap channel in the presence of a passive eavesdropper and imperfect channel state information (CSI). We assume that the users participating in full-duplex communication have multiple transmit antennas, and that the users and the eavesdropper have single receive antenna each. The users have individual transmit power constraints. They also transmit jamming signals to improve the secrecy rates. We obtain the achievable perfect secrecy rate region by maximizing the worst case sum secrecy rate. We also obtain the corresponding transmit covariance matrices associated with the message signals and the jamming signals. Numerical results that show the impact of imperfect CSI on the achievable secrecy rate region are presented.

关键词： MISO full-duplex physical layer security secrecy rate semidefinite programming

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Robust Interference Management via Outage-Constrained Downlink Beamforming in Multicell Networks

Robust Interference Management via Outage-Constrained Downli...

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IEEE Global Communications Conference

作者： Saba Nasseri Mohammad Reza Nakhai Centre for Telecommunications Research King's College London Strand UK WC2R 2LS

ISBN: (纸本)9781479913510

We introduce a downlink robust optimization problem to minimize an objective function which is a combination of a cost characterizing the total transmit power at each multiple antenna base station (BS) and a penalty term due to the induced aggregate intercell interference power across the multicell network. This optimization is constrained to assure that a set of target signal-to-interference-plus-noise ratio (SINR) levels are maintained at the intracell user terminals with adjustable outage probabilities. The involved uncertainties are due to the errors incurred in estimating both direct downlink channels within each cell and the interfering channels between a BS and the remote user terminals in other cells. By introducing a slack variable, we reformulate the problem so that the probability of imperfection in the penalty term is confined within an adjustable outage. To maintain the tractability of the robust solutions, we derive an equivalent semidefinite programming (SDP) formulation that is convex under the standard rank relaxation. Our simulation results show that at fixed outage probabilities, the proposed scheme is considerably more sensitive to imperfection in direct downlink channels than the imperfection in the interfering channels at a given BS. This observation is due to the presence of the interfering channels within the penalty term that is minimized as a part of the proposed objective function.

关键词： Robust CSI uncertainty intercell interference outage probability semidefinite programming

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Discrete-Time Maximum Likelihood Neural Distribution Steering

Discrete-Time Maximum Likelihood Neural Distribution Steerin...

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IEEE Conference on Decision and Control

作者： George Rapakoulias Panagiotis Tsiotras School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA School of Aerospace Engineering Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies the problem of steering the distribution of a discrete-time dynamical system from an initial distribution to a target distribution in finite time. The formulation is fully nonlinear, allowing the use of general control policies, parametrized by neural networks. Although similar solutions have been explored in the continuous-time context, extending these techniques to systems with discrete dynamics is not trivial. The proposed algorithm results in a regularized maximum likelihood optimization problem, which is solved using machine learning techniques. After presenting the algorithm, we provide several numerical examples that illustrate the capabilities of the proposed method. We start from a simple problem that admits a solution through semidefinite programming, serving as a benchmark for the proposed approach. Then, we employ the framework in more general problems that cannot be solved using existing techniques, such as problems with non-Gaussian boundary distributions and non-linear dynamics.

关键词： Machine learning algorithms semidefinite programming Costs Heuristic algorithms Neural networks Machine learning Gaussian distribution Benchmark testing Nonlinear dynamical systems Numerical models

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Symbolic-numeric algorithms for computing validated results 14

Symbolic-numeric algorithms for computing validated results

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Proceedings of the 39th International Symposium on Symbolic and Algebraic Computation

作者： Lihong Zhi Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Beijing China

ISBN: (纸本)9781450325011

In the tutorial, we will introduce two kinds of problems for which validated results are computed via hybrid symbolic-numeric algorithms. These hybrid algorithms follow the basic principle pointed out by Siegfried M. Rump in [1] for computing validated results: First, a pure floating point algorithm is used to compute an approximate solution of good quality for a given problem. Second, a verification step using exact rational arithmetic or interval arithmetic is appended. If this step is successful, then certified lower bounds or verified error bounds are computed for the previously computed approximation.

关键词： singular solutions sum-of-squares certificates semidefinite programming symbolic/numeric hybrid methods verified error bounds

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Two Stage Distributionally Robust Optimization for Post-Disaster Material Supply Network

Two Stage Distributionally Robust Optimization for Post-Disa...

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Chinese Control and Decision Conference, CCDC

作者： Dongdong Zhang Qinghe Hu Yiming Wang Hanxing Shang Xilong Hua College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Material supply network for emergency rescue of natural disasters is of vital importance in reducing casualties and protecting people's lives and property. This paper introduces a post-disaster rescue material supply network. Due to the strong uncertainty of material demand in disaster-stricken areas and the difficulty in obtaining probability distributions with sufficient accuracy, we propose a two-stage distribution robust optimization method. We construct the ambiguity set of material demand in the supply network based on statistics such as the first and second moments from historical data. Then, the optimization problem is formulated as a two-stage stochastic robust optimization model. Linear decision-making rules and duality theory are jointly applied to facilitate solving process of the proposed optimization model. Via a numerical experiment, the effectiveness of the proposed approach is validated.

关键词： Uncertainty semidefinite programming Disasters Optimization models Supply chains Transportation Stochastic processes

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Direction-of-Arrival Estimation for Constant Modulus Signals via Convex Optimization

Direction-of-Arrival Estimation for Constant Modulus Signals...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Xunmeng Wu Zai Yang Zongben Xu The School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Several man-made signals in communications and array processing, e.g., phase-modulated and frequency-modulated signals, exhibit the constant modulus (CM) property. This paper is concerned about the problem of direction-of-arrival (DOA) estimation for CM source signals using linear arrays. Existing methods either rely on nonconvex optimization suffering from convergence or optimality issues or cannot fully use the CM property of signals or the array manifold due to great challenges brought by the highly nonconvex CM constraints. In this paper, we propose a convex optimization approach for CM DOA estimation based on atomic norm minimization. Our main contributions are summarized below. 1) To use the CM property, we define a CM atomic norm and formulate the CM DOA estimation problem as an ANM problem. 2) We propose a semidefinite programming, by introducing a series of positive-semidefinite structured matrices, to characterize the CM atomic norm and enable computations of the CM ANM problem. 3) Simulations are carried out that validate the advantageous performance of the proposed approach.

关键词： Manifolds Direction-of-arrival estimation semidefinite programming Simulation Computational modeling Estimation Minimization

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Learning Sheaf Laplacian Optimizing Restriction Maps

Learning Sheaf Laplacian Optimizing Restriction Maps

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Asilomar Conference on Signals, Systems & Computers

作者： Leonardo Di Nino Sergio Barbarossa Paolo Di Lorenzo Department of Information Engineering Electronics and Telecommunications Sapienza University Rome Italy National Interuniversity Telecommunications Consortium (CNIT) Parma Italy

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

The aim of this paper is to propose a novel frame-work to infer the sheaf Laplacian, including the topology of a graph and the restriction maps, from a set of data observed over the nodes of a graph. The proposed method is based on sheaf theory, which represents an important generalization of graph signal processing. The learning problem aims to find the sheaf Laplacian that minimizes the total variation of the observed data, where the variation over each edge is also locally minimized by optimizing the associated restriction maps. Compared to alternative methods based on semidefinite programming, our solution is significantly more numerically efficient, as all its fundamental steps are resolved in closed form. The method is numerically tested on data consisting of vectors defined over subspaces of varying dimensions at each node. We demonstrate how the resulting graph is influenced by two key factors: the cross-correlation and the dimensionality difference of the data residing on the graph's nodes.

关键词： Computers Laplace equations semidefinite programming Network topology Signal processing Vectors Topology Signal denoising

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A Power-Efficient Coverage Scheme for Cell-Edge Users using Cognitive Beamforming

A Power-Efficient Coverage Scheme for Cell-Edge Users using ...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

作者： Azar Zarrebini-Esfahani Tuan Anh Le Mohammad Reza Nakhai Centre for Telecommunications Research King's College London Strand UK WC2R 2LS

ISBN: (纸本)9781467362337

This paper addresses the problem of strong intercell interference on cell-edge users in conventional cellular networks by deploying cognitive cells within the vicinity of primary cell borders. The cognitive base stations serve primary cell-edge users within the cognitive cells. In return, the cognitive base stations are rewarded by the same spectrum allocated to the primary base stations to serve secondary users. We propose a strategy that is formulated as an optimization problem for the cognitive cell to minimize the total transmit power of the cognitive base station. This optimization problem is subjected to maintain a controlled level of interference at the primary outer-cell users falling outside of the cognitive cell and to assure required levels of signal-to-noise-plus-interference-ratio (SINR) at all primary celledge and secondary users within the cognitive cell. Simulation results confirm that the beamforming scheme in conjunction with the proposed cognitive structure lead to a significant reduction in overall power transmitted in the network.

关键词： Cognitive radio beamforming intercell interference semidefinite programming

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Sampling and Reconstruction of Graph Signals via Weak Submodularity and semidefinite Relaxation

Sampling and Reconstruction of Graph Signals via Weak Submod...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Abolfazl Hashemi Rasoul Shafipour Haris Vikalo Gonzalo Mateos Dept. of Electrical and Computer Engineering University of Texas at Austin Austin TX USA Dept. of Electrical and Computer Engineering University of Rochester Rochester NY USA

ISBN: (纸本)9781538646595

We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the signal reconstruction mean squared error (MSE). To that end, we formulate the task at hand as the minimization of MSE subject to binary constraints, and approximate the resulting NP-hard problem via semidefinite programming (SDP) relaxation. Moreover, we provide an alternative formulation based on maximizing a monotone weak submodular function and propose a randomized-greedy algorithm to find a sub-optimal subset. We then derive a worst-case performance guarantee on the MSE returned by the randomized greedy algorithm for general non-stationary graph signals. The efficacy of the proposed methods is illustrated through numerical simulations on synthetic and realworld graphs. Notably, the randomized greedy algorithm yields an order-of-magnitude speedup over state-of-the-art greedy sampling schemes, while incurring only a marginal MSE performance loss.

关键词： graph signal processing sampling weak sub-modularity semidefinite programming randomized algorithms randomized algorithms Mean square error greedy algorithm Sampling signals Reconstructions

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