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ICASSP
2011
IEEE
12 years 9 months ago
Worst-case based robust adaptive beamforming for general-rank signal models using positive semi-definite covariance constraint
In this paper, we develop a new approach to the robust beamforming for general-rank signal models. Our method is based on the worst-case performance optimization using a semi-de n...
Haihua Chen, Alex B. Gershman
SIAMJO
2010
110views more  SIAMJO 2010»
13 years 11 days ago
Estimating Bounds for Quadratic Assignment Problems Associated with Hamming and Manhattan Distance Matrices Based on Semidefinit
Quadratic assignment problems (QAPs) with a Hamming distance matrix for a hypercube or a Manhattan distance matrix for a rectangular grid arise frequently from communications and ...
Hans D. Mittelmann, Jiming Peng
ICASSP
2008
IEEE
14 years 2 days ago
Robust adaptive beamforming for general-rank signal models using positive semi-definite covariance constraint
In this paper, we develop an improved approach to the worstcase robust adaptive beamforming for general-rank signal models by means of taking into account the positive semi-defin...
Haihua Chen, Alex B. Gershman
SIAMMAX
2010
224views more  SIAMMAX 2010»
13 years 11 days ago
Robust Approximate Cholesky Factorization of Rank-Structured Symmetric Positive Definite Matrices
Abstract. Given a symmetric positive definite matrix A, we compute a structured approximate Cholesky factorization A RT R up to any desired accuracy, where R is an upper triangula...
Jianlin Xia, Ming Gu
TSP
2010
13 years 9 days ago
A dual perspective on separable semidefinite programming with applications to optimal downlink beamforming
This paper considers the downlink beamforming optimization problem that minimizes the total transmission power subject to global shaping constraints and individual shaping constrai...
Yongwei Huang, Daniel Pérez Palomar