(1) We have a new paper accepted in IEEE Communications Letters:
Ce Zheng, Malcolm Egan, Laurent Clavier, Anders Kalør and Petar Popovski, “Stochastic Resource Optimization of Random Access for Transmitters with Correlated Activation,” IEEE Communications Letters, (2021).
(2) On 10th June, Prof. Dejan Vukobratovic (University of Novi Sad) will present in our seminar:
Speaker: Prof. Dejan Vukobratovic (University of Novi Sad)
Date: 2pm (CET), 10th June 2021
Title: Designing Unequal Error Protection Codes Using Deep Autoencoders
Abstract: In this talk, we will discuss an autoencoder-based approach for designing codes that provide unequal error protection (UEP) capabilities. The proposed design accommodates both message-wise and bit-wise UEP scenarios. For both scenarios, we present the design method for the proposed autoencoder-based UEP codes and compare them with classical UEP code designs available in the literature.
Bio: Dejan Vukobratovic received a PhD degree in electrical engineering from the University of Novi Sad, Serbia, in 2008, where he is now a full Professor from 2019. During 2009 and 2010, he was Marie Curie Intra-European Fellow at the University of Strathclyde, Glasgow, UK. He published about 40 journal and 80 conference papers mainly in top-tier IEEE journals and conference venues. He received the best paper award at IEEE MMSP 2010 and his PhD student received the best student paper award at IEEE SmartGridComm 2017. His research interests are in the broad area of information and coding theory, wireless communications, distributed signal and information processing in Smart Grids, and massive machine-type communications in mobile cellular networks.
(3) On 17th June, Dr Xuewen Qian (Centrale Supelec) will present in our seminar:
Speaker: Dr. Xuewen Qian (Centrale Supelec)
Date: 2pm (CET), 17th June 2021
Title: Advanced Detection Schemes for Molecular Communications based on K-Means Clustering Approach
Abstract: We consider non-coherent detection (without channel information) for molecular communication systems in the presence of inter-symbol-interference. In particular, we study non-coherent detectors based on memory-bits-based thresholds in order to achieve low bit-error-ratio (BER) transmission. The main challenge of realizing detectors based on memory-bits-based thresholds is to obtain the channel state information based only on the received signals. We tackle this issue by reformulating the thresholds through intermediate variables, which can be obtained by clustering multi-dimensional data from the received signals, and by using the K-means clustering algorithm. In addition to estimating the thresholds, we show that the transmitted bits can be retrieved from the clustered data. To reduce clustering errors, we propose iterative clustering methods from one-dimensional to multi-dimensional data, which are shown to reduce the BER. Simulation results are presented to verify the effectiveness of the proposed methods.
Bio: Xuewen Qian received the B.Sc. and M.S. degrees with distinction in Electronic Science and Technology from Central South University, Changsha, China in 2014 and 2017, respectively. He obtained the Ph.D. degree from Paris-Saclay University, Paris, France, in 2020. In 2021, he was awarded the NEC Student Research Fellowship Award. Currently, he is a Research Fellow at CentraleSupelec, Paris-Saclay University, Paris, France. His current research interests include wireless communications, molecular communications, machine learning, deep learning, and reconfigurable intelligent surfaces.
(4) On 24th June, Dr. Vyacheslav Kungurtsev (Czech Technical University in Prague) will present in our seminar:
Speaker: Dr. Vyacheslav Kungurtsev (Czech Technical University in Prague)
Date: 2pm (CET), 24th June 2021
Title: Levenberg Marquardt Algorithms for Nonlinear Inverse Least Squares
Abstract: Levenberg Marquardt (LM) algorithms are a class of methods that add a regularization term to a Gauss Newton method to promote better convergence properties. This talk presents three works on this class of methods. The first discusses a new method that simultaneously achieves all types of state of the art convergence guarantees for unconstrained problems. Stochastic LM is discussed next, which is an algorithm to handle noisy data. An example is presented on data assimilation. Finally, a LM method is presented to handle equality constraints, with examples from inverse problems in PDEs.
Bio: Dr. Vyacheslav Kungurtsev is a researcher in the Department of Computer Science, Czech Technical University in Prague, Department of Com- puter Science, Faculty of Electrical Engineering, Prague, Czech Republic.