A
Andreas Wolfgang
Researcher at Chalmers University of Technology
Publications - 64
Citations - 885
Andreas Wolfgang is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Beamforming & MIMO. The author has an hindex of 15, co-authored 59 publications receiving 757 citations. Previous affiliations of Andreas Wolfgang include University of Southampton & Karlsruhe Institute of Technology.
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Journal ArticleDOI
Calculated and measured absorption cross sections of lossy objects in reverberation chamber
TL;DR: In this paper, the authors used the forward scattering theorem to calculate the mean absorption cross-section of the lossy objects in a reverberation chamber and compared the results with measured levels in the reverberation chambers.
Proceedings ArticleDOI
Integration of Communication and Sensing in 6G: a Joint Industrial and Academic Perspective
Henk Wymeersch,Deep Shrestha,Carlos H. M. de Lima,Vijaya Yajnanarayana,Bjorn Richerzhagen,Musa Furkan Keskin,Kim Schindhelm,Alejandro Ramirez,Andreas Wolfgang,Mar Francis de Guzman,Katsuyuki Haneda,Tommy Svensson,Robert Baldemair,Stefan Parkvall +13 more
TL;DR: The Hexa-X project as mentioned in this paper aims to support high-resolution localization and sensing in the next generation of mobile communication, which will enable novel use cases requiring extreme localization performance and provide a means to support and improve communication functionalities.
Journal ArticleDOI
Symmetric RBF Classifier for Nonlinear Detection in Multiple-Antenna-Aided Systems
TL;DR: A powerful symmetric radial basis function (RBF) classifier for nonlinear detection in the so-called "overloaded" multiple-antenna-aided communication systems is proposed by exploiting the inherent symmetry property of the optimal Bayesian detector.
Proceedings ArticleDOI
Joint Maximum Likelihood Channel Estimation and Data Detection for MIMO Systems
TL;DR: Blind and semiblind adaptive schemes are proposed for joint maximum likelihood channel estimation and data detection for multiple-input multiple-output (MIMO) systems and an efficient global optimisation search algorithm is employed at the upper level to identify the unknown MIMO channel model.