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Andreas Burg
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 331
Citations - 8626
Andreas Burg is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Decoding methods & MIMO. The author has an hindex of 42, co-authored 317 publications receiving 7524 citations. Previous affiliations of Andreas Burg include École Normale Supérieure & Vienna University of Technology.
Papers
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Journal ArticleDOI
VLSI implementation of MIMO detection using the sphere decoding algorithm
TL;DR: Two ASIC implementations of MIMO sphere decoders with efficient implementation of the enumeration approach recently proposed in .
Journal ArticleDOI
LLR-Based Successive Cancellation List Decoding of Polar Codes
TL;DR: The LLR-based formulation of the successive cancellation list (SCL) decoder is presented, which leads to a more efficient hardware implementation of the decoder compared to the known log-likelihood based implementation.
Journal ArticleDOI
Soft-output sphere decoding: algorithms and VLSI implementation
TL;DR: VLSI implementation results are provided which demonstrate that single tree-search, sorted QR-decomposition, channel matrix regularization, log-likelihood ratio clipping, and imposing runtime constraints are the key ingredients for realizing soft-output MIMO detectors with near max-log performance at a chip area that is only 58% higher than that of the best-known hard-output sphere decoder VLSI Implementation.
Proceedings ArticleDOI
MIMO transmission with residual transmit-RF impairments
TL;DR: It is demonstrated that residual distortions severely degrade the performance of (near-)optimum MIMO detection algorithms, and an efficient algorithm is proposed, which is based on an i.i.d. Gaussian model for the distortion caused by these impairments.
Posted Content
MIMO Transmission with Residual Transmit-RF Impairments
TL;DR: In this article, the effect on channel capacity and error-rate performance of residual Tx-RF impairments that defy proper compensation was studied. And the authors proposed an efficient algorithm, which is based on an i.i.d. Gaussian model for the distortion caused by these impairments.