Eduard Jorswieck, Technische Universitat Dresden, Communications Laboratory, Dresden, Germany
Aydin Sezgin, Ruhr-Universitat Bochum, Institute of Digital Comm. Systems, Bochum, Germany
- Introduction and Motivation 5G and Beyond (15 Min - EJ)
- Interference Alignment and Applications (45 Min - AS)
- Rate Splitting and Applications (30 Min - AS)
- Interference Neutralization and Applications (45 Min - EJ)
- Physical Layer Networking Coding + Compute and Forward (30 Min - EJ)
- Conclusions and Outlook (15 Min - AS)
About the Speakers
Aydin Sezgin was born in 1975 in Kemah, Anatolia. He received the Dipl.-Ing. (M.S.) degree in communications engineering and the Dr.-Ing. (Ph.D.) degree in electrical engineering from the TFH Berlin in 2000 and the TU Berlin, in 2005, respectively. From 2001 to 2006, he was with the Heinrich-Hertz-Institut (HHI), Berlin. From 2006 to 2008, he was a Postdoc and Lecturer at the Information Systems Laboratory, Department of Electrical Engineering, Stanford University. From 2008 to 2009, he was a Post-doc at the Department of Electrical Engineering and Computer Science at the University of California Irvine. From 2009 to 2011, he was the Head of the Emmy-Noether-Research Group on Wireless Networks at the Ulm University. In 2011, he was professor at TU Darmstadt, Germany. He is currently a professor of Information Systems and Sciences at the Department of Electrical Engineering and Information Technology at Ruhr-University Bochum, Germany. Aydin is interested in signal processing, communication and information theory with focus on wireless networks. He has published several book chapters, more than 30 journal and 120 conference papers on these topics. He has co-authored a book on multi-way communications. He served as Associate Editor for IEEE Transactions on Wireless Communications 2009-2014. Aydin is the winner of the ITG-sponsorship award in 2006. He is the rst recipient of the prestigious Emmy-Noether grant by the German Research Foundation (DFG) in communication engineering in 2009. He has co-authored a paper that received the best poster award at the IEEE Comm. Theory Workshop in 2011. He has also co-authored a paper that received the best paper award at ICCSPA in 2015.
Rogério Dionisio, Instituto Politécnico de Castelo Branco, Portugal
SEAMCAT® is a software tool, based on the Monte-Carlo simulation method, which is developed within the frame of European Conference of Postal and Telecommunication administrations (CEPT). This tool permits statistical modelling of different radio interference scenarios for performing sharing and compatibility studies between radiocommunications systems in the same or adjacent frequency bands. The software is maintained by the European Comunication Office (ECO) and distributed free-of-charge ( www.seamcat.org ). It is worth mentioning that the SEAMCAT tool is used by many regulatory agencies across the world for various spectrum sharing and compatibility studies such as between mobile systems and terrestrial broadcasting networks.
The workshop will start by presenting the modelling principles used by SEAMCAT and how to configure a coexistence scenario between heterogeneous radio systems. After that, the attenders will be invited to install the latest version of SEAMCAT in theirs laptops and two hands-on examples will be performed and discussed:
- Modelling co-channel interference between fixed links;
- Modelling interference between mobile systems.
About the Speaker
Rogério Dionisio received his B.Sc., M.Sc. and Ph.D. degrees in Electronics and Telecommunications Engineering from the University of Aveiro (Portugal) in 1997, 2004 and 2014, respectively. Since 1999 he is an Assistant Professor at the Polytechnic Institute of Castelo Branco. He participates in several National and European research projects on wireless communications (AGILE, COGEU, CREW, White Spaces Access, Fed4FIRE, WISHFUL) and European networks of excellence (BONE, EURO-FOS). He received the “New Frontiers of Engineering” award and was also granted Telecommunication Specialist in 2015, by the Portuguese Engineer Council. He is co-founder of Allbesmart, a start-up focused on IoT for smart-territories. He is author of several journal and conference publications, and his main research interests are advanced signal processing techniques for wideband communication systems, radio spectral coexistence analysis (PHY level) and wireless sensor networks.
Ingrid Moerman, imec - Universiteit Gent - IDLab
Some trends observed in wireless networking are the ever-increasing number and diversity of wireless devices running applications that become more critical and bandwidth-hungry. Unfortunately, as the wireless radio spectrum is a scarce resource, the available spectral bandwidth does not scale with the increasing bandwidth demands from wireless devices and applications.This calls for new sophisticated techniques for accessing and sharing the wireless medium in a more efficient manner. This tutorial addresses techniques to improve the coordination within a given spectral band, shared by multiple heterogeneous wireless technologies, residing in the same wireless environment or even in the same physical device. Spectral efficiency is not only impacted by PHY layer settings, but is also influenced by MAC and network operation mode. This tutorial will present an innovative architecture, as developed in the H2020 WiSHFUL project, that decouples the control plane from data (or user) plane hereby adopting the general idea of software defined networking (SDN). The WiSHFUL architecture abstracts radio and network control functions and is capable to control the behaviour of heterogeneous wireless devices and networks at runtime through a well-defined unified programming interface.
Wireless networks further have become extremely complex systems that cannot be optimized anymore following traditional approaches such as design-time planning and simple one-size-fits-all configuration strategies after deployment. Instead there is an increasing need for more intelligent techniques, being cognitive radio networking solutions, that can monitor the dynamic behaviour of the network, for instance the dynamics of the wireless environment and the varying application demands, and based on that autonomously adapt the network and radio parameters to maximize network performance and QoS. This tutorial will explain how machine learning techniques can be utilised to better understand, diagnose, optimize and remedy wireless networks. Different machine learning techniques will be analysed in terms of benefits and limitations for improving network performance. A detailed example will be presented how to apply machine learning on wireless traces. This tutorial will further show how machine leaning techniques can be implemented on top top of WiSHFUL architecture for enabling intelligent radio and network control, as explored in the H2020 WiSHFUL and eWINE projects.
This tutorial consist of three parts:
- Part 1: Presentation on a software architecture for advanced radio and network control to improve spectral efficiency in coexisting wireless networks and illustration with concrete examples
- Part 2: Presentation on the utilisation of machine learning techniques to add intelligent control in wireless networks and illustration with concrete examples
- Part 3: Real-life demonstration of the radio and network control architecture and intelligent control by adapting PHY and MAC parameters in a wireless coexistence scenario.
About the Speaker
Ingrid Moerman received her degree in Electrical Engineering (1987) and the Ph.D. degree (1992) from the Ghent University, where she became a part-time professor in 2000. She is a staff member at IDLab, a core research group of imec with research activities embedded in Ghent University and University of Antwerp. Ingrid Moerman is coordinating the research activities on mobile and wireless networking, and she is leading a research team of about 30 members at Ghent University. Her main research interests include: Internet of Things, Low Power Wide Area Networks (LPWAN), High-density wireless access networks, collaborative and cooperative networks, intelligent cognitive radio networks, real-time software defined radio, flexible hardware/software architectures for radio/network control and management, and experimentally-supported research. Ingrid Moerman has a longstanding experience in running and coordinating national and EU research funded projects. At the European level, Ingrid Moerman is in particular very active in the Future Networks research area, where she has coordinated and is coordinating several FP7/H2020 projects (CREW, WiSHFUL, eWINE, ORCA) and participating in other projects (FLEX, Flex5Gware, Fed4FIRE+).
Ingrid Moerman has received 14 awards and prizes during her career, of which 9 best paper awards, 2 prizes awarded by FWO (Research Foundation - Flanders), the IMEC Prize of excellence 2001, one MSc Thesis Award (as promoter), and one best demo/exhibit award (at ICT 2013). Ingrid Moerman is author or co-author of more than 700 publications in international journals or conference proceedings.
Suzan Bayhan, Telecommunications Networks Group (TKN), TU Berlin, Germany
Gürkan Gür, TETAM, Bogazici University, Istanbul, Turkey
The dramatic increase in the number of smart mobile devices with novel paradigms such as IoT and fog computing resulted in new challenges for efficient operation of wireless systems. At the spectral front, the emergence of ultra-dense and multi-technology wireless networks has led to a crowded yet underutilized spectrum landscape. In this context, cognitive radios (CR) and dynamic spectrum access are paramount for meeting expected level of service quality and user experience. However, spectrum sharing, which is a key pillar of CR operation, is a complicated challenge. In that regard, machine learning (ML) techniques are crucial enablers.
In this tutorial, we aim to provide the basics of machine learning in CR networks (CRNs) including the applications and the involved challenges. We discuss that machine learning is inherently a CR research topic due to envisaged CR characteristics. Moreover, we overview the related work from the perspective of relevant trade-offs. We elaborate on ML topics such as supervised learning, unsupervised learning, reinforcement learning, and online learning. We also overview how such learning schemes are used to enable efficient spectrum sharing for cognitive radio in general and for specific cases, WiFi for AP-coexistence solutions and LTE+WiFi coexistence, to name a few. The tutorial will:
- Capture the current state-of-the-art in the fields of machine learning and spectrum sharing;
- Identify challenges, potential solutions and research directions.
About the Speakers
Suzan Bayhan received her PhD degree in computer engineering in 2012 from Bogazici University, Istanbul. Between 2012-2016, she was a post-doctoral researcher at the University of Helsinki. Currently, she is a senior researcher at TKN, TU Berlin. She is also a docent in computer science at the University of Helsinki. Her research interests include wireless networking, cognitive radios, mobile opportunistic networks, and content-centric networking. She received the Google Anita Borg EMEA scholarship in 2009 and co-authored the best paper at ACM ICN 2015. Suzan is on N2Women Board as one of the mentoring co-chairs.
Gürkan Gür received his B.S. degree in electrical engineering in 2001 and Ph.D. degree in computer engineering in 2013 from Bogazici University, Istanbul, Turkey. Currently, he is a senior researcher at TETAM, Bogazici University. He is also a member of Satellite Networks Research Laboratory (SATLAB) at Bogazici University. His research interests include cognitive radios, green wireless communications, small-cell networks, network security and information-centric networking. He has two patents and published more than 50 academic works. He is a senior member of IEEE.