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Alex B. Gershman
Researcher at Technische Universität Darmstadt
Publications - 314
Citations - 12810
Alex B. Gershman is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Robustness (computer science) & Estimator. The author has an hindex of 56, co-authored 314 publications receiving 12015 citations. Previous affiliations of Alex B. Gershman include Russian Academy of Sciences & University of Duisburg-Essen.
Papers
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Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem
TL;DR: A new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch is developed based on the optimization of worst-case performance.
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Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals
Mehrzad Biguesh,Alex B. Gershman +1 more
TL;DR: This paper considers the popular linear least squares and minimum mean-square-error approaches and proposes new scaled LS (SLS) and relaxed MMSE techniques which require less knowledge of the channel second-order statistics and/or have better performance than the conventional LS and MMSE channel estimators.
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Convex Optimization-Based Beamforming
Alex B. Gershman,Nicholas D. Sidiropoulos,Shahram Shahbazpanahi,Mats Bengtsson,Bjorn Ottersten +4 more
TL;DR: It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems.
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Robust adaptive beamforming for general-rank signal models
TL;DR: The proposed robust adaptive beamformers are based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix as well as worst-case performance optimization and offer a significantly improved robustness and faster convergence rates.
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Fast antenna subset selection in MIMO systems
TL;DR: This paper proposes a fast antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems that achieves almost the same outage capacity as the optimal selection technique while having lower computational complexity than the existing nearly optimal antenna selection methods.