T1 Ultra Reliable Low Latency Communications (URLLC): Resource Allocation, Scheduling & Multi-Connectivity
Speaker(s): Eduard Jorswieck TU-Dresden
Time and Venue: 27.8 9:00 – 12:30 IT112
Abstract: 5G is expected to support Ultra Reliable Low Latency Communications (URLLC) based services, such as industrial control, remote surgery, tactile internet, etc. These are also the most challenging services to implement because they require a new network design and control methodology, in order to satisfy their requirements and enable their co-existence with other types of services that 5G and beyond systems need to deliver. The tutorial addresses the signal processing and optimization aspects of URLLC for 5G and beyond networks. The tutorial will cover a novel system design framework, state of the art signal processing and optimization techniques; resource allocation and scheduling, as well as multi-connectivity approaches for ultra-reliability. Wireless reliability is understood as successfully transmitting a desired amount of data within a given time. Diversity techniques, such as multi-connectivity, are potential solutions to achieve stringent reliability requirements. Spatial, temporal, spectral diversity and multi-connectivity are well understood from a capacity perspective. In this keynote, we will go one step beyond this traditional point of view and consider the impact of the statistical dependencies of the underlying random channel variables on the latency and reliability of the resulting URLLC links. We characterize the best and worst case dependencies for different use cases based on the concept of copulas and show that independence is neither the best nor the worst case.
Eduard A. Jorswieck was born in 1975 in Berlin, Germany. Since February 2008, he has been the head of the Chair of Communications Theory and Full Professor at Dresden University of Technology (TUD), Germany. He is PI in the new excellence cluster CeTI (centre for Tactile Internet with Human-in-the-loop: ceti.one). Eduard's main research interests are in the area of signal processing for communications and networks, applied information theory, and communications theory. He has published more than 100 journal papers, 3 monographs, 11 book chapter, and some 250 conference papers on these topics. Dr. Jorswieck is senior member of IEEE. Since 2017, he is Editor-in-Chief of the Springer EURASIP Journal on Wireless Communications and Networking. He was member of the IEEE SPCOM Technical Committee (2008-2013) and is member of the IEEE SAM TC (since 2015). Since 2011, he acts as Associate Editor for IEEE Transactions on Signal Processing. Since 2008, continuing until 2013, he has served as an Associate Editor and Senior Editor for IEEE Signal Processing Letters. Since 2016, he serves as Associate Editor for IEEE Transactions on Information Forensics and Security. Since 2013, he serves as Associate Editor for IEEE Transactions on Wireless Communications. In 2006, he received the IEEE Signal Processing Society Best Paper Award.
T2 Wireless communications for big data and IoT: a data-oriented approach.
Speaker(s): Hong-Chuan Yang, University of Victoria, Canada; Mohamed-Slim Alouini, KAUST, Saudi Arabia
Time and Venue: 27.8 13:30 – 17:00 IT105
Abstract: Wireless communication systems play an essential role in the generation and transmission of big data. The design and optimization of wireless transmission strategies for big data application are of critical current interest. In this tutorial, we present a unique data oriented approach for the design and analysis of wireless transmission strategies, specifically targeting at big data transmission. Novel data-oriented performance metrics are proposed and applied to the analysis of wireless transmission strategies in the information theoretical and practical transmission settings. We also develop analytical frameworks to accurately characterize the data transmission time in both cognitive and non-cognitive environments. Compared to conventional analytical approach, the data-oriented approach offers important new insights and leads to interesting new research directions. Through this tutorial, the attendees can obtain a brand new perspective to the analysis and optimization of wireless transmission technologies for big data applications.
Dr. Hong-Chuan Yang received the Ph.D. degree in electrical engineering from the University of Minnesota in 2003. He is a professor of the Department of Electrical and Computer Engineering at the University of Victoria, Canada. From 1995 to 1998, He was a Research Associate at the Science and Technology Information Center (STIC) of the Ministry of Posts & Telecomm. (MPT), Beijing, China. His current work mainly focuses on different aspects of wireless communications, with special emphasis on channel modeling, diversity techniques, system performance evaluation, cross-layer design, and energy efficient communications. He has published over 200 journal and conference papers. He is the author of the book Introduction to Digital Wireless Communications by IET press and the coauthor of the book Order Statistics in Wireless Communications by Cambridge University
Mohamed-Slim Alouini received the Ph.D. degree in electrical engineering from the California Institute of Technology (Caltech) in 1998. He also received the Habilitation degree from the Universite Pierre et Marie Curie in 2003. Dr. Alouini started his academic career at the University of Minnesota in 1998. In 2005, he joined Texas A&M University at Qatar, Doha, and in 2009, he was appointed as Professor of Electrical Engineering at KAUST, Thuwal, Mekkah Province, Saudi Arabia, where he is responsible for research and teaching in the areas of Communication Theory and Applied Probability. More specifically, his research interests include design and performance analysis of diversity combining techniques, MIMO techniques, multi-hop/cooperative communications systems, cognitive radio systems, and multi-resolution, hierarchical and adaptive modulation schemes. Dr. Alouini has published many papers on the above subjects, and he is co-author of the textbook Digital Communication over Fading Channels published by Wiley Interscience. Dr. Alouini is a (i) Fellow of the Institute of Electrical and Electronics Engineers (IEEE), (ii) IEEE Distinguished Lecturer for the IEEE Communication Society and IEEE Vehicular Technology Society, (iii) member for several times in the annual Thomson ISI Web of Knowledge list of Highly Cited Researchers as well as the Shanghai Ranking/Elsevier list of Most Cited Researchers, and (iv) co-recipient of best paper awards in eleven IEEE conferences (including ICC, GLOBECOM, VTC, PIMRC, ISWCS, and DySPAN)
T3 Future Evolution of 5G NR (rel 16, 17).
Speaker(s): Stefan Parkvall, Ericsson, Sweden
Time and Venue: 27.8 9:00 – 12:30 IT112
Abstract: With the completion of the first release (Rel-15) of the 3GPP fifth-generation (5G) NR specifications, the focus of the research community is now being directed towards the next step in the evolution of wireless mobile communication. Similar to earlier generations, it can be expected that the next ten years will see a gradual evolution of NR, introducing new innovative technology components and further enhancing the capabilities and expanding the scope of 5G wireless access. The first step of the NR evoluition is already ongoing in 3GPP in release 16 with features such as Integrated Access Backhaul (IAB), operation in unlicensed spectrum (NR-U), and vechicle-to-vechile communication. The scope of the second step in the evolution, release 17, is currently under discussion and may include for example include operation in higher frequencies and enhancements to better support machine learning and artificial intelligence. In this presentation, the evolution of 5G NR will be discussed with particular focuis on teh rel-16 and rel-17.
Stefan Parkvall [F] is a Senior Expert at Ericsson Research, working with 5G and future radio access. He is one of the key persons in the development of HSPA, LTE and NR radio access and has been deeply involved in 3GPP standardization for many years. Dr Parkvall is a fellow of the IEEE and served as an IEEE Distinguished lecturer 2011-2012. He is co-author of the popular books “3G Evolution – HSPA and LTE for Mobile Broadband”, “HSPA evolution – the Fundamentals for Mobile Broadband”, “4G – LTE/LTE-Advanced for Mobile Broadband”, “4G, LTE Advanced Pro and the Road to 5G”, and “5G NR – The Next Generation Wireless Access”. Dr. Parkvall has more than 2000 patents in the area of mobile communication. In 2005, he received the Ericsson "Inventor of the Year" award, in 2009 the Swedish government’s Major Technical Award for contributions to the success of HSPA, and in 2014 he and Ericsson colleagues were among the finalists for the European Inventor Award for their contributions to LTE. Dr Parkvall holds a Ph.D. in electrical engineering from the Royal Institute of Technology (KTH) in Stockholm, Sweden. Previous positions include assistant professor in communication theory at KTH, and visiting researcher at University of California, San Diego, USA.
T4: Robust Online Machine Learning in 5G Radio Access Networks: Requirements, Challenges, Theory and Promising Approaches
Speaker(s): Slawomir Stanczak, Fraunhofer and Renato Cavalcante, Fraunhofer
Time and Venue: 27.8 13:30 – 17:00 IT105
Abstract: Wireless communications poses fundamental challenges to machine learning (ML). Wireless links are subject to fading and may be exposed to strong interference. Since wireless resources are scarce, this may severely limit the capacity of wireless links, thus requiring distributed ML solutions that efficiently use the wireless resources. Moreover, ML methods need to provide robust results based on small uncertain data sets and under strict latency constraints. Current signal processing algorithms for wireless transceivers are typically based on models that assume, for example, ideal linear amplifiers, perfect channel state information, and knowledge of interference patterns. In practice, however, these assumptions are unrealistic because many parameters have to be estimated, so it is often unclear how well the idealized models can capture the true behavior of real communication systems. As a result, in recent years a great deal of effort has been devoted to replacing many of the building blocks of the radio access network by few machine learning algorithms, with the the intent to reduce drastically the number of assumptions and the number of complex estimation techniques. However, this reduction in model knowledge brings many technical challenges. In particular, in the physical layer, the wireless environment can be considered roughly constant only for few milliseconds, which can be all the time available for acquisition of training sets and for the training procedure. As a result, computationally simple learning techniques that can cope with small training sets, or that are able to extract largely time-invariant features of the wireless signals (so that traditional learning tools can be employed), have been in great demand. In this tutorial, we will review online machine learning algorithms for these tasks. The first part of the tutorial includes a mathematical introduction to machine learning and is based on two courses given to graduate students at the TU Berlin. The content includes topics like learning model, stochastic inequalities and concentration of measure, Markov chains, the concept of VC dimension, fundamentals of reproducing kernel Hilbert spaces and kernel-based learning, convex learning as well as regularization, dimensionality reduction and compressive sensing. We will complete this part with mathematical introduction to deep learning and (deep) reinforcement learning. In the second part, we introduce online machine learning methods based on projections in Hilbert spaces that can be used to realize selected RAN functions. A special attention will be attached to applications such as localization, beamforming in MIMO systems and load/QoS prediction. Meeting the latency requirements of 5G networks requires massive parallelization. Therefore we will also discuss how to parallelize and map these algorithms to GPU architectures to achieve orders-of-magnitude acceleration. We will complete the tutorial by reviewing recent results on the design of neural networks. Our tutorial will also use findings of the ITU-T focus group on machine learning for 5G to discuss the impact of machine learning on future network architectures.
Slawomir Stanczak studied electrical engineering with specialization in control theory at the Wroclaw University of Technology and at the Technical University of Berlin (TU Berlin). He received the Dipl.-Ing. degree in 1998 and the Dr.-Ing. degree (summa cum laude) in electrical engineering in 2003, both from TU Berlin; the Habilitation degree (venialegendi) followed in 2006. Since 2015, he has been a Full Professor for network information theory with TU Berlin and the head of the Wireless Communications and Networks department. Prof. Stanczak is a co-author of two books and more than 200 peer-reviewed journal articles and conference papers in the area of information theory, wireless communications, signal processing and machine learning.He served as an Associate Editor of the IEEE Transactions on Signal Processing between 2012 and 2015. Since February 2018 Prof. Stanczak has been the chairman of the ITU-T focus group on machine learning for future networks including 5G.
Renato Luis Garrido Cavalcante received the electronics engineering degree from the Instituto Tecnologico de Aeronautica (ITA), Brazil, in 2002, and the M.E. and Ph.D. degrees in Communications and Integrated Systems from the Tokyo Institute of Technology, Japan, in 2006 and 2008, respectively. From April 2003 to April 2008, he was a recipient of the Japanese Government (MEXT) Scholarship. He is currently a Research Fellow with the Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany, and a lecturer at the Technical University of Berlin. Previously, he held appointments as a Research Fellow with the University of Southampton, Southampton, U.K., and as a Research Associate with the University of Edinburgh, Edinburgh, U.K. Dr. Cavalcante received the Excellent Paper Award from the IEICE in 2006 and the IEEE Signal Processing Society (Japan Chapter) Student Paper Award in 2008. He also co-authored a study that received a best student paper award at the 13th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) in 2012. His current interests are in signal processing for distributed systems, multiagent systems, convex analysis, machine learning, and wireless communications.