Prof. Maryline Hélard | September 5, 2018 | 15:00 | L4.1.01

Abstract

To meet growing demand for higher throughputs, advanced digital communication techniques based on multicarrier modulations, multiple antenna systems (MIMO) and their extension to massive MIMO (M-MIMO), powerful coding schemes or interference coordination are always under study and could be combined with solutions based on network densification and deployment of heterogeneous infrastructures.

For a number of years now,  IETR (Institut of Electronics and Telecommunications of Rennes) Labs has being developed strong expertise in communications systems with several experiments associating OFDM and MIMO in some proofs of concept.

In the first part of this talk, one of the classical precoding technique, known as Time Reversal applied to digital communications will be presented as well as its easy combination with OFDM. A second part will be dedicated to spatial modulation applied either at transmit or receive side and its possible application to IoT. For both systems, the benefit of using a high number of transmit antenna will be highlighted and a description of the proofs of concept implementation carried out in our labs provided.

The talk will then concentrate on the researches carried out with some of our PhD students on hybrid beamforming techniques, mmWave communications and VLC communications.

Bio

Professor Maryline Hélard received the M.Sc and PhD degrees from INSA Rennes and the Habilitation degree from Rennes 1 University in 1981, 1884 and 2004 respectively. In 1985, she joined France Telecom as a research engineer and since 1991 she has been studying physical layer in the field of digital television and wireless communications. In 2007, she joined the National Institute of Applied Science (INSA) as a professor and she is now the co-director of the Signal and 2018 Communications department of IETR (Electronics and Telecommunications Institute of Rennes). She is co-author of more than 140 technical papers including 37 journal papers and of 30 patents. Her current research interests are in the areas of digital communications such as MIMO techniques, large MIMO, OFDM, MC-CDMA, channel estimation, equalization, spatial modulations and iterative processing applied to wireless communications and more recently to wire communications (ADSL, optical). She was involved in several collaborative research projects including digital television, MC-CDMA techniques, time reversal and spatial modulation.

Prof. Jean-François Hélard | September 5, 2018 | 14:00 | L4.1.01

Abstract

OFDM multicarrier techniques are widely deployed in most wireless communication systems, in particular in cellular networks (LTE, LTE Advanced, 5G…), broadcast networks (DVB-T, DVB-T2, ATSC3.0…) and WiFi networks. However, multi-carrier modulations are characterized by a very large dynamic amplitude measured by the Peak to Average Power Ratio (PAPR). This large amplitude prevents radio frequency designers to feed the signal at the optimal point of the non linear High Power Amplifier (HPA) which reduces their energy efficiency and then increases dramatically the overall base station consumption. In literature, the PAPR reduction and the  linearization techniques are the main approaches to solve this HPA efficiency problem in cellular and broadcast networks

In recent years, tone reservation (TR) PAPR reduction techniques have been deeply studied and included for example in the DVB-T2 and the new American digital video broadcasting (ATSC 3.0) standards. It is based on a gradient iterative approach where, at each iteration, a predefined kernel is used to reduce one peak in the time domain. During this talk, we will present novel TR PAPR reduction techniques, namely grouped individual carrier allocation for multiple peaks (GICMP) based on a new kernel signal and fully compatible with the new broadcasting DVB-T2 and ATSC3.0 standards. An in-depth performance analysis based on simulation and experimental results demonstrated that the novel proposed PAPR reduction algorithm offers very good performance/complexity/latency trade-off.

In the second part of this talk, we will present intelligent solution for future implementations to control the reduction of PAPR and the linearization steps in a flexible way according to some predefined parameters so that they become adaptive and self-configurable. More specifically, our work focused on the analytical analysis of in-band measured by the Error Vector Magnitude (EVM) and out-of-band distortions measured by the Adjacent Chanel Power Ratio (ACPR) for multicarrier signals taking into account the PAPR reduction, the impact of non-linear amplification, the memory effects and the predistortion. Combining those complementary approaches, the power efficiency of the transmitters with OFDM multicarrier waveforms can be highly upgraded and/or the energy consumption reduced by 10 to 15 %

Bio

Professor Hélard received his Dipl.-Ing. and his Ph.D in electronics and signal processing from the National Institute of Applied Sciences (INSA) in Rennes in 1981 and 1992 respectively. From 1982 to 1997, he was research engineer and then head of channel coding for the digital broadcasting research group at France Telecom Research Center (Orange Labs) in Rennes. In 1997, he joined INSA Rennes, which is one of the « Grandes Ecoles » in France, where he is today Full Professor, Classe Exceptionelle, which is the highest rank. He was Director of Research of INSA Rennes during 3 years  from december 2010 to december 2013. He was also during 8 years Deputy Director of the Rennes Institute for Electronics and Telecommunications (IETR, UMR CNRS 6164), which is an academic research laboratory of 400 people, created in 2002 in association with the CNRS. His research interests lie in signal processing techniques for digital communications, such as space-time and channel coding, multi-carrier modulation, as well as multi-user communications and cross-layer techniques, power efficient and PAPR reduction techniques.  He is involved in several European and national research projects in the fields of digital video terrestrial broadcasting, mobile radio communications and cellular networks, power-line and ultra-wide-band communications, cooperative communications and relaying techniques. Prof. J-F. Hélard is a senior member of IEEE, author and co-author of more than 270 technical papers in international scientific journals and conferences, and holds 15 European patents.

The review of the TEWI colloquium of Prof. Dr.-Ing. Jörn Ostermann from July 12, 2018 comprises the slides (below) and video here.

Abstract: We present a comparison between AV1, HEVC and JEM. It reveals that professionally optimized AV1 encoding software is about 32 times as complex as the HEVC encoder software HM. Given the wide attention that AV1 receives and the plans of Google and Facebook to actually use AV1 in commercial environments, the complexity constraints endured during the HEVC development seem to disappear. We present recent results on contour-based prediction for intra coding, a KLT depending on scene and intra prediction direction for coding the prediction error and texture synthesis to replace conventional texture coding for uniform parts of images. Especially contour-based prediction and texture synthesis depend heavily on computer vision algorithms. While typically failures of efficient prediction can be compensated by coding the prediction error and hence a higher data rate, texture synthesis in video coding does not have this safety leash.

Bio: Jörn Ostermann studied Electrical Engineering and Communications Engineering at the University of Hannover and Imperial College London, respectively. He received Dipl.-Ing. and Dr.-Ing. from the University of Hannover in 1988 and 1994, respectively. From 1988 till 1994, he worked as a Research Assistant at the Institut für Theoretische Nachrichtentechnik conducting research in low bit-rate and object-based analysis-synthesis video coding. In 1994 and 1995 he worked in the Visual Communications Research Department at AT&T Bell Labs on video coding. He was a member of Image Processing and Technology Research within AT&T Labs – Research from 1996 to 2003. Since 2003 he is Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover, Germany. Since 2008, he is the Chair of the Requirements Group of MPEG (ISO/IEC JTC1 SC29 WG11). Jörn was a scholar of the German National Foundation. In 1998, he received the AT&T Standards Recognition Award and the ISO award. He is a Fellow of the IEEE (class of 2005). Joern served as a Distinguished Lecturer of the IEEE CAS Society (2002/2003). He published more than 100 research papers and book chapters. He is coauthor of a graduate level text book on video communications. He holds more than 30 patents.

Alois Reitbauer |  August 23, 2018 | 15:00 | E.2.42

Abstract

You may have heard of autonomous self-driving cars, but what is autonomous self-driving software. Dynatrace has been running their software systems following a NoOps approach for several years now. Based on their experience they have developed a new approach to manage software applications using concepts like unbreakable delivery pipelines and self-healing deployments. Learn what is behind the idea of NoOps and how to build applications that run and manage themselves, what can be built today and what the future will bring.

CV

Alois Reitbauer is the Chief Technology Strategist of Dynatrace and leads the Dynatrace innovation lab. Alois has successfully developed several solutions in the application performance management space and brought them to market. Currently he is working with his team on building autonomous software systems that manage themselves. Alois is further interested in the impact of new collaboration technologies like voice or Augmented Reality. In both areas he currently works with customers to develop market-ready products. Alois is also a frequent speaker at technology conferences and the chair of the W3C Distributed Tracing working group.

Prof. Dr.-Ing. Jörn Ostermann | July 12, 2018 | 10:00am | E.2.42

Abstract: We present a comparison between AV1, HEVC and JEM. It reveals that professionally optimized AV1 encoding software is about 32 times as complex as the HEVC encoder software HM. Given the wide attention that AV1 receives and the plans of Google and Facebook to actually use AV1 in commercial environments, the complexity constraints endured during the HEVC development seem to disappear. We present recent results on contour-based prediction for intra coding, a KLT depending on scene and intra prediction direction for coding the prediction error and texture synthesis to replace conventional texture coding for uniform parts of images. Especially contour-based prediction and texture synthesis depend heavily on computer vision algorithms. While typically failures of efficient prediction can be compensated by coding the prediction error and hence a higher data rate, texture synthesis in video coding does not have this safety leash.

Bio: Jörn Ostermann studied Electrical Engineering and Communications Engineering at the University of Hannover and Imperial College London, respectively. He received Dipl.-Ing. and Dr.-Ing. from the University of Hannover in 1988 and 1994, respectively. From 1988 till 1994, he worked as a Research Assistant at the Institut für Theoretische Nachrichtentechnik conducting research in low bit-rate and object-based analysis-synthesis video coding. In 1994 and 1995 he worked in the Visual Communications Research Department at AT&T Bell Labs on video coding. He was a member of Image Processing and Technology Research within AT&T Labs – Research from 1996 to 2003. Since 2003 he is Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover, Germany. Since 2008, he is the Chair of the Requirements Group of MPEG (ISO/IEC JTC1 SC29 WG11). Jörn was a scholar of the German National Foundation. In 1998, he received the AT&T Standards Recognition Award and the ISO award. He is a Fellow of the IEEE (class of 2005). Joern served as a Distinguished Lecturer of the IEEE CAS Society (2002/2003). He published more than 100 research papers and book chapters. He is coauthor of a graduate level text book on video communications. He holds more than 30 patents.

The review of the TEWI colloquium of Prof. Konstantin Mischaikow from June 6, 2018 comprises the slides (below).

Abstract

With the advent of every improving information technologies, science and engineering is being being evermore guided by data-driven models and large-scale computations.  In this setting, one often is forced to work with models for which the nonlinearities are not derived from first principles and quantitative values for parameters are not known.

With this in mind, I will describe an alternative approach formulated in the language of combinatorics and algebraic topology that is inherently multiscale, amenable to mathematically rigorous results based on discrete descriptions of dynamics, computable, and capable of recovering robust dynamic structures.

To keep the talk grounded, I will discuss the ideas in the context of modeling of gene regulatory networks.

CV

Konstantin Mischaikow earned his Master and PhD degree at the University of Wisconsin–Madison in 1983 and 1985, respectively. Currently he is a Distinguished Professor at Rutgers University, New Jersey, USA. His main research interests are topological methods for the analysis of dynamical systems, computational topology and mathematical biology.  Professor Mischaikow has supervised 16 PhD theses and has been advisor of 21 postdocs. He has over 110 publications, including four books.

He is a leading expert of Conley theory, as well as of rigorous computer-assisted computations. One of his most celebrated results is the proof of chaos in the Lorenz attractor, which serves as a prominent example of the application of both techniques.  In 2014, in recognition of his contributions to dynamical systems as well as to applied and computational topology, Professor Mischaikow was elected to be a fellow of the American Mathematical Society.

The review of the TEWI colloquium of Dr. Hari Prabhat Gupta from June 22, 2018 comprises the slides (below).

Abstract

Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.

CV

Dr. Hari Prabhat Gupta received the B.E. degree in Computer Engineering from Government Engineering College Ajmer, Ajmer, India, the M.Tech. and Ph.D degrees in Computer Science and Engineering from the Indian Institute of Technology Guwahati (IITG), Guwahati, India. He worked with Samsung R&D Bangalore, India. He has received a research fellowship from TATA Consultancy Services, India. He is currently working as Assistant Professor in Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India. His research interests include wireless sensor networks, wireless ad hoc networks, and distributed algorithms. He has published various IEEE and ACM conference papers and Journals in the field of wireless sensor networks.