Towards 6G Wireless Communication Networks: Vision, Enabling Technologies, and New Paradigm Shifts
Fifth generation (5G) wireless communication networks are being deployed worldwide and more capabilities are in the process of being standardized, such as massive connectivity, ultra-reliability, and low latency. However, 5G will not meet all requirements of the future, and sixth generation (6G) wireless networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, greater intelligence and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architectures, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. One vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication networks. Multiple spectra will be exploited to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the very large datasets generated by heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of AI-related technologies. And, fourth, network security will have to be strengthened when developing 6G networks. This talk will review recent advances and future trends in these four aspects.
H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University, where his interests include information theory, machine learning and network science, and their applications in wireless networks, energy systems and related fields. He also has held visiting appointments at a number of other universities, including most recently at Berkeley and Cambridge. Among his publications is the forthcoming book Machine Learning and Wireless Communications, to be published by Cambridge University Press later this year. Dr. Poor is a Member of the U.S. National Academy of Engineering and the U.S. National Academy of Sciences, and a Foreign Member of the Royal Society and other national and international academies. Recent recognition of his work includes the 2017 IEEE Alexander Graham Medal, and honorary doctorates from several universities in Asia, Europe and North America.
Integrated Ground-Air-Space Networking: Just Utopia or a Next-Generation Challenge?
Thanks to the spectacular advances in signal processing and nanotechnology, five wireless generations have been conceived over the past five decades. Indeed, near-capacity operation at an infinitesimally low error-rate has become feasible and flawless multimedia communications is supported in areas of high traffic-density, but how do we fill the huge coverage holes existing across the globe?
As a promising system-architecture, an integrated terrestrial, UAV-aided, airplane-assisted as well as satellite-based global coverage-solution will be highlighted to pave the way for seamless next-generation service provision. However, these links exhibit strongly heterogeneous properties, hence requiring different enabling techniques.
The joint optimization of the associated conflicting performance metrics of throughput, transmit power, latency, error probability, hand-over probability and link-lifetime poses an extremely challenging problem. Explicitly, sophisticated multi-component system optimization is required for finding the Pareto-front of all optimal solutions, where none of the above-mentioned metric can be improved without degrading at least one of the others …
Lajos Hanzo is a Fellow of the Royal Academy of Engineering (FREng), FIEEE, FIET and a EURASIP Fellow, Foreign Member of the Hungarian Academy of Science. He holds honorary Doctorates from the University of Edinburgh and the Technical University of Budapest. He co-authored 19 IEEE Press – John Wiley books and 1900+ research contributions at IEEE Xplore. For further information on his research in progress and associated publications please refer to IEEE Xplore.
Team Playing under Uncertainties
Cooperation is an essential function in a wide array of network scenarios, including wireless, robotics, transports and beyond. In decentralized networks, cooperation (or team play) must be achieved by agents despite information uncertainties in the global state of the network. Cooperation in the presence of information uncertainties is a highly challenging problem for which no systematic optimization solution exist. In this talk, we describe different mechanisms for solving it, from information theoretic to machine learning based. In the machine learning approach this problem, we introduce so called Team Deep Learning Networks (Team-DNN) where agents learn to coordinate with each other under uncertainties. In the communication domain, we show how devices can learn how to message each other relevant information and take appropriate transmission decisions, possibly under the control of a meta-expert, so as to optimize network performance.
David Gesbert is a Professor and head of the Communications Systems Department of EURECOM. He is also heading the Foundations and Algorithms group. He welcomes collaboration with brilliant students and passionate researchers in the fields of communication theory, signal processing for (wireless) networks, information theory, optimization and connected robotics. He teaches “Advanced Topics in Wireless” (Fall) and “Information theory” (Fall) at EURECOM. He has been named in the Thomson-Reuters List of Highly Cited Researchers in Computer Science. He is a Fellow of IEEE. He is a Board member for the OpenAirInterface (OAI) Software Alliance. Since 2015, he is the holder of an Advanced ERC grant on the topic of Smart Device Communications. Since early 2019, he heads the Huawei-funded Chair on Advanced Wireless Systems Towards 6G Networks. Since 2020, he holds a 3IA Chair funded on the topic of AI for future IoT Networks.
How to Achieve Energy Efficiency in Multi-Antenna Systems? A Physical Layer Perspective
Energy efficiency has become as important as spectral efficiency for the future communications infrastructure. Since the wireless access is a main contributor to the overall energy dissipation, the focus here is on the physical layer of mobile communication systems. The key functional units from an energy dissipation point of view are the high power amplifiers (HPAs) in the transmitter (Tx) and the analog to digital converters (ADCs) in the receiver (Rx). An obvious approach is using constant envelope signals for the HPAs and in the Tx and low resolution ADCs in the Rx. The challenge is how to overcome the performance loss due to these restrictions. Since these systems will employ multiple antennas anyway, it is important to exploit the potential of antenna arrays in terms of antenna gain, diversity and multi- streaming. The basis for that is bridging the gap between electromagnetic theory, circuit design, signal processing and information theory. The Multiport Communication Theory is providing a circuit theoretic framework enabling an information theoretic analysis and optimization which is consistent with the underlying physics.
Josef A. Nossek (S’72–M’74–SM’81–F’93—LF’12) received the Dipl.-Ing. and the Dr. techn. degrees in electrical engineering from the University of Technology in Vienna, Austria in 1974 and 1980, respectively. In 1974 he joined Siemens AG in Munich, Germany as a member of technical staff, in 1978 he became supervisor, and from 1980 on he was Head of Department. In 1987 he was promoted to be Head of all radio systems design. Since 1989 he has been Full Professor for circuit theory and signal processing at the Munich University of Technology where he teaches undergraduate and graduate courses on circuit and systems theory and signal processing and leads research on signal processing algorithms for communications, especially multiantenna systems. He was President Elect, President and Past President of the IEEE Circuits and Systems Society in 2001, 2002 and 2003 respectively. He was Vicepresident of VDE (Verband der Elektrotechnik, Elektronik und Informationstechnik e.V.) 2005 and 2006, President of VDE 2007 and 2008 and was Vicepresident again in 2009 and 2010. His awards include the ITG Best Paper Award 1988, the Mannesmann Mobilfunk (now Vodafone) Innovationsaward 1998, the Award for Excellence in Teaching from the Bavarian Ministry for Science, Research and Art in 1998. From the IEEE Circuits and Systems Society he received the Golden Jubilee Medal for ‘Outstanding Contributions to the Society’ in 1999 and the Education Award in 2008. In 2008 he also received the Order of Merit of the Federal Republic of Germany and in 2009 he has been elected member of the German National Academy of Engineering Sciences (acatech). In 2013 he received an Honorary Doctorate and in 2014 the Ring of Honor from VDE. From 2016 to 2019 he has been Full Professor at the Federal University of Ceará in Brasil.
6G – Ever-present Intelligent Communication 2030 and Beyond
The first release of 5G NR have been successfully standardized by 3GPP and commercial networks are being rolled out around the globe. 5G will continue to evolve for many years to come with the recently completed release 16 and currently ongoing release 17 being the two first steps of the evolution.
In parallel, the research community have begun initial discussions on 6G and wireless communication in 2030 and beyond. At that point, society will have been shaped by 5G for 10 years, and new needs and services will have appeared. Even with the built-in flexibility of 5G, we are beginning to see the horizon where further capabilities are needed.
In this talk we will discuss fundamental drivers, possible use cases, basic capabilities, and potential key technologies for a future 6G system. Such a system will go beyond connectivity alone and will be a trusted platform for communication and compute, encouraging innovation and serving as the information backbone of society.
Stefan Parkvall (F) is currently a Senior Expert at Ericsson Research working with research on 6G 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, served as an IEEE Distinguished lecturer in 2011-2012, and is co-author of several popular books such as “3G Evolution – HSPA and LTE 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”. He has more than 1500 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 his contributions to the success of HSPA, and in 2014 he and colleagues at Ericsson was one of three finalists for the European Inventor Award, the most prestigious inventor award in Europe, for their contributions to LTE. Dr Parkvall received the Ph.D. degree in electrical engineering from the Royal Institute of Technology in 1996. His previous positions include assistant professor in communication theory at the Royal Institute of Technology, Stockholm, Sweden, and visiting researcher at University of California, San Diego, USA.
Data-Selective Online Learning
In the era of big data, profitable opportunities are becoming available for many applications. As the amount of data keeps increasing, online learning becomes an attractive tool to analyze the information acquired. However, harnessing meaningful data remains a challenge. Classical machine learning tools apply all training data without taking into consideration how relevant are some of them.
This current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifies to improve the related statistical inference or if it consists of an outlier or a non-innovative entry. Highlighting online solutions, we discuss some adaptive filtering and machine learning algorithms enabling data selection which also addresses the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some prior assumptions regarding the environment data or some simple estimation based on the available information.
Test results also show the effectiveness of the proposed algorithms for selecting data during the training step of neural networks to obtain the most meaningful data information and improve algorithm performance during training. The results apply to classification and regression problems leading to computational savings and classification error reduction. Based on open datasets, the examples corroborate the effectiveness of the discussed strategy.
Paulo S. R. Diniz received the Electronics Eng. degree (Cum Laude) from the Federal University of Rio de Janeiro (UFRJ) in 1978, the M.Sc. degree from COPPE/UFRJ in 1981, and the Ph.D. from Concordia University, Montreal, P. Q., Canada, in 1984, all in electrical engineering. He wrote the text books ADAPTIVE FILTERING: Algorithms and Practical Implementation, Fifth Edition, Springer, Cham, Switzerland, 2020, and DIGITAL SIGNAL PROCESSING: System Analysis and Design, Second Edition, Cambridge University Press, Cambridge, UK, 2010 (with E. A. B. da Silva and S. L. Netto), and the monograph BLOCK TRANSCEIVERS: OFDM and Beyond, Morgan & Claypool, New York, NY, 2012 (W. A. Martins, and M. V. S. Lima). He has published over 100 refereed papers in journals and over 200 conference papers in some of these areas. He is a member of the National Academy of Engineering (ANE) and the Brazilian Academy of Science (ABC). He is also a Fellow of IEEE and EURASIP.