Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem

By Sergiy A. Vorobyov, Alex B. Gershman, Zhi-Quan Tom Luo. IEEE Transactions on Signal Processing, 2003

1,487 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/07ff0584542edf5cbc3489168f1c4b0c61aac81e

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Citations in the corpus

Robust Beamforming for Wireless Information and Power Transmission (2012, methodology): “For the objective function of P1 in (14), we simplify it using an approach similar to the one developed in [10] and [13].”
Doubly constrained robust Capon beamformer (2004, methodology): “However, for most of the diagonal loading methods, it is not clear how to choose the diagonal loading level based on information about the uncertainty of the array steering vector.”
Robust minimum variance beamforming (2005, background): “Simulations Numerical examples of RMVB with ellipsoid/spherical uncertainty can be found in [5]–[7].”
Robust adaptive beamforming for general-rank signal models (2003, methodology): “However, a serious drawback of this approach is that there is no reliable way to choose a proper value of the loading factor because the optimal choice ofessentially depends on the unknown signal and interference parameters [27].”
Convex Optimization-Based Beamforming (2010, neutral): “Note that all results in this section generalize straightforwardly to scenarios with multiple cells, see [17].”
Beampattern Synthesis for Linear and Planar Arrays With Antenna Selection by Convex Optimization (2010, positive): “To improve the robustness of the proposed methods (6) and (9) against various undesirable effects, the worst-case performance optimization technique [2], [17] is applied.”
A Robust and Efficient Algorithm for Coprime Array Adaptive Beamforming (2017, methodology): “The optimization problem in the worst-case performance optimization-based beamforming algorithm [37] also generates an O((M +N)3) computational complexity.”
A robust maximin approach for MIMO communications with imperfect channel state information based on convex optimization (2006, methodology): “This approach has been recently used in the classical problem of designing a receive beamformer under mismatches in the presumed model, as in [29], where the errors were assumed to be in the estimated steering vector and to belong to a spherical uncertainty region.”
Principles of minimum variance robust adaptive beamforming design (2013, methodology): “Thus, the worst-case optimization-based RAB uses the prior information about the presumed steering vector and the information that the mismatch vector is norm bounded [59].”
MIMO Transceiver Design via Majorization Theory (2007, neutral): “, [10, 101, 123, 159, 165].”
An introduction to convex optimization for communications and signal processing (2006, methodology): “Interestingly, by exploiting the phase ambiguity in w and using the same transformation outlined above, we can show [34] that (57) is equivalent to the following convex optimization problem (SOCP):”
Transmit beamforming for physical-layer multicasting (2006, background): “When the scaled channel vectors span a ball or ellipsoid about a " nominal " channel vector, 3 the problem can be transformed exactly into a second-order cone program, and hence can be efficiently solved [13].”
Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation (2012, methodology): “The proposed beamformers (8) and (13) are compared to the eigenspace-based beamformer [16], the worst-case-based beamformer [6], the sequential quadratic programming (SQP)-based beamformer [9], the shrinkage estimation-based beamformer [5], and the iterative adaptive approach (IAA) beamformer [17].”