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检索条件"主题词=Semidefinite Programming"

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Energy Efficient Joint Precoding for Multibeam Satellite Systems

Energy Efficient Joint Precoding for Multibeam Satellite Sys...

引用

IEEE Vehicular Technology Conference

作者： Hongjun Li Feihong Dong Yuanzhi He Bo Jing Jingchao Wang Fuchun Han Jing Wang College of Communications Engineering PLA University of Science and Technology Institute of China Electronic System Engineering Corporation Shenyang Military Region Equipment Department

ISBN: (纸本)9781509016990

Power and spectrum are considered as the significantly resource in the satellite communication systems, recently multiple-input multiple-output (MIMO) precoding has been identified as an excellent choice for the multibeam satellite communication systems (MSCSs). In order to improve power and spectrum utilization while mitigate the intra-beam interference and inter-beam interference, the crucial challenge is joint precoding design for multiple terminals. This paper proposes a hybrid full frequency (HFF) joint precoding scheme and chooses transmit power minimization as the energy efficient optimization problem which can be formulated as an indefinite quadratic optimization program. An optimization algorithm based on semidefinite programming (SDP) is designed to obtain the optimal objective. Simulation results demonstrate the performance improvement of proposed HFF joint precoding outperforms conventional frequency reuse and existing algorithms with respect to per feed power on-board constrained.

关键词： Energy efficient Multibeam satellite communication systems Hybrid full frequency Precoding semidefinite programming

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Distributed optimization with unconstrained polynomial objective function

Distributed optimization with unconstrained polynomial objec...

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第35届中国控制与决策会议

作者： Haozheng Meng Tai-Fang Li College of Control Science and Engineering Bohai University

Distributed optimization has been proposed for parallel computing for many *** present,many distributed algorithms are developed for solving the cooperative optimization problem of multi-agent systems with objective function in summation form of local *** order to solve more general optimization problems in a distributed way,in this paper,we study the distributed optimization problem with unconstrained polynomial objective function and use the Lasserre's relaxation method to convert the studied optimization problem to an equivalent semidefinite *** the knowledge of graph theory,we disassemble the matrix variables and propose a distributed alternating directions method of multipliers to solve the semidefinite ***,a numerical example is used to illustrate the feasibility of our proposed algorithm.

关键词： distributed optimization Lasserre's relaxation polynomial objective function semidefinite programming

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Cooperative Beamforming in Cognitive Radio Network with Two-Way Relay

Cooperative Beamforming in Cognitive Radio Network with Two-...

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IEEE Vehicular Technology Conference

作者： Jianwei Zhang Li Guo Tianyu Kang Peng Zhang Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications

ISBN: (纸本)9781479944811

This paper investigates the cooperative beamforming in the amplify-and-forward-based cognitive relay network. Beamforming coefficients are designed to maximize the sum rate of the secondary users with a total transmit power constraint of the relays and an interference power constraint at the primary receiver. The sum rate maximization problem is firstly solved by characterizing the achievable rate region. This method is optimal but of great complexity. Then, an iteration algorithm based on semidefinite programming (SDP) and the first order Taylor polynomial which has lower complexity but comparable performance is introduced. Simulation results confirm the feasibility of these two methods for the sum rate maximization problem in the cognitive relay network and demonstrate that the lower complexity method finds the global optimum of the problem just as the rate region method does.

关键词： amplify and forward communication array signal processing cognitive radio cooperative communication iterative methods mathematical programming relay networks (telecommunication) SDP Taylor polynomial amplify-and-forward-based cognitive relay network beamforming coefficients cognitive radio network cooperative beamforming iteration algorithm semidefinite programming sum rate maximization problem two-way relay Array signal processing Cognitive radio Complexity theory Interference Relay networks (telecommunications) Simulation Cognitive radio array signal processing Iterative algorithms Service Discovery Protocol cooperative communication Complexity theory Taylor Polynomial iterative methods Bidirectional Beamforming Relay networks relay

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A decomposition method for nonconvex quadratically constrained quadratic programs

A decomposition method for nonconvex quadratically constrain...

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American Control Conference

作者： Chuangchuang Sun Ran Dai Aerospace Engineering Department Iowa State University Ames United States of America

ISBN: (纸本)9781509045839

This paper examines the nonconvex quadratically constrained quadratic programming (QCQP) problems using a decomposition method. It is well known that a QCQP can be transformed into a rank-one constrained optimization problem. Finding a rank-one matrix is computationally complicated, especially for large scale QCQPs. A decomposition method is applied to decompose the single rank-one constraint on original unknown matrix into multiple rank-one constraints on small scale submatrices. An iterative rank minimization (IRM) algorithm is then proposed to gradually approach all of the rank-one constraints. To satisfy each rank-one constraint in the decomposed formulation, linear matrix inequalities (LMIs) are introduced in IRM with local convergence analysis. The decomposition method reduces the overall computational cost by decreasing size of LMIs, especially when the problem is sparse. Simulation examples with comparative results obtained from an alternative method are presented to demonstrate advantages of the proposed method.

关键词： Quadratically Constrained Quadratic programming Rank Constraint Optimization semidefinite programming Matrix Decomposition Sparse Matrix

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Stability and Stabilization of Aperiodic Sampled-Data Control Systems:An Approach Using Robust Linear Matrix Inequalities

Stability and Stabilization of Aperiodic Sampled-Data Contro...

引用

Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Yasuaki Oishi Hisaya Fujioka Department of Systems Design and Engineering Nanzan University

Stability analysis of an aperiodic sampled-data control system is considered for application to network and embedded control. The stability condition is described in a linear matrix inequality to be satisfied for all possible sampling intervals. Although this condition is numerically intractable,a tractable sufficient condition can be constructed with the mean value theorem. Special attention is paid to tightness of the sufficient condition for less conservative stability analysis.A region-dividing technique for reduction of conservatism and generalization to stabilization are also discussed. Examples show the efficacy of the approach.

关键词： sampled-data control robust linear matrix inequality conservatism asymptotic exactness semidefinite programming adaptive division

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Solution of the Propeller Conjecture in R~3 12

Solution of the Propeller Conjecture in R~3

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ACM Symposium on Theory of Computing

作者： Steven Heilman Aukosh Jagannath Assaf Naor Courant Institute NYU New York NY USA

ISBN: (纸本)9781450312455

It is shown that every measurable partition {A_1,...,A_k} of R~3 satisfies ∑_(i=1)~k ‖∫_(A_i) xe~(-1/2‖x‖_2~2)dx‖_2~2 ≤ 9π~2. (1) Let {P_1, P_2, P_3} be the partition of R~2 into 120° sectors centered at the origin. The bound (1) is sharp, with equality holding if A_i = P_i × R for i ∈ {1, 2, 3} and A_i = 0 for i ∈ {4,...,k}. This settles positively the 3-dimensional Propeller Conjecture of Khot and Naor (FOCS 2008). The proof of (1) reduces the problem to a finite set of numerical inequalities which are then verified with full rigor in a computer-assisted fashion. The main consequence (and motivation) of (1) is complexity-theoretic: the Unique Games hardness threshold of the Kernel Clustering problem with 4×4 centered and spherical hypothesis matrix equals 2π/3.

关键词： Theory Kernel clustering Unique games hardness semidefinite programming Grothendieck inequalities

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Sum of Squares Approaches for Control of Continuous-Time Recurrent Fuzzy Systems

Sum of Squares Approaches for Control of Continuous-Time Rec...

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Mediterranean Conference on Control & Automation

作者： Stefan Gering Andreas Schwung Thomas Gussner Jurgen Adamy Technische Universitat Darmstadt Control Theory & Robotics Lab

ISBN: (纸本)9781479909964

In recent years, a concise theory of recurrent fuzzy systems has emerged and methods for utilizing these fuzzy systems with dynamics for modeling and fault detection were developed. At the same time, sum of squares decompositions in conjunction with semidefinite programming were successfully applied for the synthesis of controllers for polynomial systems. In this paper, we combine both approaches and present sum of squares based control strategies for continuous-time recurrent fuzzy systems. The system dynamics under consideration is defined by gradients at discrete points in the input-state space. An alternative description as piecewise polynomial system is possible. This motivates to utilize a controller switching between local polynomial control laws. We propose three different approaches for controller synthesis based on this idea and demonstrate this new synthesis method by means of an example. In addition, advantages and drawbacks of the approaches are discussed.

关键词： Continuous-Time Recurrent Fuzzy Systems Sum of Squares Controller Synthesis Switched Controller Polynomial Systems semidefinite programming

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Robust Quadratic Optimal Control of Linear Systems with Ellipsoid-Set Learning

Robust Quadratic Optimal Control of Linear Systems with Elli...

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European Control Conference (ECC)

作者： Xuehui Mal Yutong Chen Shiliang Zhang Yushuai Li Fucai Qian Zhiyong Sun School of Automation and Information Engineering Xi'an University of Technology Xi'an China Department of Electronic Engineering Tsinghua University Beijing China Department of Informatics University of Oslo Oslo Norway Department of Computer Science Aalborg University Aalborg Denmark Department of Electrical Engineering Eind-hoven University of Technology Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Despite the celebrated success of linear quadratic Gaussian control (LQG) for stochastic systems, LQG approaches are inefficient in handling systems with non-Gaussian noises. This paper is concerned with linear quadratic control of discrete-time systems with bounded noises and unobservable system states. We describe such noises and system states by ellipsoidal sets, enabling the establishment of boundaries for those uncertainties in the control. Further, we learn and update the ellipsoidal sets for the system states by an ellipsoidal set-membership filter. With the learned ellipsoidal sets, we derive a robust state-feedback optimal control law by solving a rendered semidefinite programming problem. Simulation results demonstrate the enhanced control performance by the proposed method.

关键词： Robust control Linear systems Discrete-time systems semidefinite programming Uncertainty Stochastic systems Simulation

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Hybrid Robust Beamforming against DoA Estimation Mismatch and Moving Interference

Hybrid Robust Beamforming against DoA Estimation Mismatch an...

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IEEE International Conference on Communications in China (ICCC)

作者： Yu Du Minjian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

The direction of arrival (DoA) estimation mismatch and moving interference severely degrade the performance of beamforming. Null broadening techniques have been extensively studied to handle these issues. They block interference by generating broad nulls in the direction of interference. However, the conventional fully digital null broadening methods, which require one radio frequency (RF) chain per antenna, are not viable for large-scale antenna arrays due to the high cost and high power consumption. Moreover, they produce sharp main beams, which, despite concentrating energy, are more sensitive to DoA estimation mismatch. To overcome the challenges associated with hardware limitations and mismatches in DoA estimation, this paper investigates a partially connected hybrid beamforming (HBF) architecture. We propose an alternating fractional programming semidefinite relaxation (SDR) algorithm, which addresses the problem by dividing it into analog and digital beamforming matrices and transforming it into a semidefinite programming (SDP) problem. This approach achieves broad nulls in the interference region and flatten the main beam. Simulation results demonstrate that this method effectively combats DoA estimation mismatch and moving interference, outperforming traditional algorithms.

关键词： Radio frequency Direction-of-arrival estimation semidefinite programming Array signal processing Heuristic algorithms Simulation Estimation

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Robust Beamforming Design for MIMO-OFDM Joint Radar and Communication Systems

Robust Beamforming Design for MIMO-OFDM Joint Radar and Comm...

引用

IEEE International Conference on Communications (ICC)

作者： Chi-Han Chou Ming-Chun Lee Po-Chun Kang Ta-Sung Lee Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The investigation of the joint radar and communication (RadCom) systems has drawn attention in recent years. However, the relevant investigation of the multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) based joint RadCom system is incomplete, especially for the robust beamforming design. To fill this gap, by first formulating a robust design problem that jointly considers the estimation of ranges, Doppler velocities, and angles of targets under uncertainty along with the system capacity, this paper studies the robust beamforming design for the MIMO-OFDM joint RadCom system. Then, since the design problem can be reformulated as a semidefinite programming with rank-1 constraint, we propose using the semidefinite relaxation to solve the problem. Simulation results show that our proposed design can outperform the reference methods in the literature.

关键词： Uncertainty semidefinite programming Array signal processing Simulation OFDM Estimation Radar

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