Prof. Dr. Halim Yanıkömeroğlu (Carleton University, Canada)
Prof. Dr. Hüseyin Arslan (University of South Florida)
Doç. Dr. Pınar Boyraz Baykaş (Chalmers University of Technology, Sweden)
Dr. Salih Ergüt (TURKCELL)
Prof. Visvanattan Ramesh


Prof. Dr. Halim Yanıkömeroğlu

Integrated Space/Aerial/Terrestrial 6G Networksfor Ubiquitous 3D Super-Connectivity in 2030s


5G promises to provide connectivity for a broad range of use-cases in a variety of vertical industries; after all, this rich set of scenarios is indeed what distinguishes 5G from the previous four generations. Many of the envisioned 5G use-cases require challenging target values for one or more of the key QoS elements, such as high rate, high reliability, low latency, and high energy efficiency; we refer to the presence of such demanding links as the super-connectivity.

However, the very fundamental principles of digital and wireless communications reveal that the provision of ubiquitous super-connectivity in the global scale – i.e., beyond indoors, dense downtown or campus-type areas – is infeasible with the legacy terrestrial network architecture as this would require prohibitively expensive gross over-provisioning. The problem will only exacerbate with even more demanding 6G use-cases such as autonomous aerial vehicles requiring connectivity, thus the 3D super-connectivity.

In this talk, we will explore a 5-layer vertical architecture (VHetNet) composed of fully integrated terrestrial and non-terrestrial layers for 6G networks of 2030s:

  • Terrestrial HetNets with macro-, micro-, and pico-BSs
  • Flying-BSs (aerial-/UAV-/drone-BSs);  altitude: up to several 100 m
  • High Altitude Platforms (HAPs) (floating-BSs);  altitude: ~20 km
  • Very Low Earth Orbit (VLEO) satellites;  altitude: 200-1,500 km
  • Geostationary Orbit (GEO) satellites;   altitude: 35,786 km

In the absence of a clear technology roadmap for the 2030s, the talk has, to a certain extent, an exploratory view point to stimulate further thinking and creativity on opportunities and challenges.   


Halim Yanikomeroglu is a Professor in the Department of Systems and Computer Engineering at Carleton University, Ottawa, Canada. His research covers many aspects of wireless communications and networking technologies; his group made contributions to 4G and 5G wireless networks. He supervised 21 PhD students (all completed with theses). He coauthored 375+ peer-reviewed research papers including 125+ in the IEEE journals; these publications have received 11,500+ citations. He is a Fellow of IEEE, a Fellow of Engineering Institute of Canada (EIC), and a Distinguished Speaker for both IEEE Communications Society and IEEE Vehicular Technology Society. He has been one of the most frequent tutorial presenters in the leading international IEEE conferences (30 times). He has had extensive collaboration with industry which resulted in 26 granted patents. During 2012-2016, he led one of the largest academic-industrial collaborative research projects on pre-standards 5G wireless, sponsored by the Ontario Government and the industry. He served as the General Chair (VTC2017-Fall Toronto and VTC2010-Fall Ottawa) and Technical Program Chair (WCNC 2014 Istanbul, WCNC 2008 Las Vegas, and WCNC 2004 Atlanta) of several major international IEEE conferences. He was the Chair of the IEEE Technical Committee on Personal Communications. He is currently serving as the Steering Committee Chair of IEEE’s flagship Wireless Communications and Networking Conference (WCNC).

Prof. Dr. Hüseyin Arslan

Flexible and Cognitive Radio Access Technologies for 5G and Beyond

Prof.  Huseyin Arslan (Univ. of South Florida & Istanbul Medipol Univ.)

5G aims to support new and diverse sets of application classes like eMBB communications, uRLLC and mMTC. Supporting these services using a single framework has introduced a new vision and sets of challenges for wireless researchers in many layers of the protocol stacks, especially in the Physical, Medium Access and Network Layers. The trend on the variety and the number of mobile devices along with the mobile applications will certainly continue beyond 5G. In order to create a system that can support this trend, a wide range of technical challenges and requirements must be simultanously satisfied. A robust system that ensures extreme reliability and low latency under diverse channel conditions, while minimizing production and operational costs and maximizing power and spectrum efficiency is required. Cooperative networking capability and coexistence, dynamic and flexible utilization of wireless spectrum, highly flexible, cognitive and adaptive radio access technologies are key enablers in addressing these technical challenges. In this talk, the potential directions and research opportunities to address the challenges and requirements of the 5G vision will be discussed.

Short Bio: Huseyin Arslan

Dr. Arslan (IEEE Fellow) has received his BS degree from Middle East Technical University (METU), Ankara, Turkey in 1992; MS and Ph.D. degrees in 1994 and 1998 from Southern Methodist University (SMU), Dallas, TX. USA. From January 1998 to August 2002, he was with the research group of Ericsson Inc., NC, USA, where he was involved with several projects related to 2G and 3G wireless communication systems.  Since August 2002, he has been with the Electrical Engineering Dept. of University of South Florida, Tampa, FL, USA, where he is a Professor. In December 2013, he joined Istanbul Medipol University to found the Engineering College, where he has worked as the Dean of the School of Engineering and Natural Sciences. He has also served as the director of the Graduate School of Engineering and Natural Sciences at the same university. In addition, he has worked as a part-time consultant for various companies and institutions including Anritsu Company, Savronik Inc., and The Scientific and Technological Research Council of Turkey.

Dr. Arslan’s research interests are related to advanced signal processing techniques at the physical and medium access layers, with cross-layer design for networking adaptivity and Quality of Service (QoS) control. He is interested in many forms of wireless technologies including cellular radio, wireless PAN/LAN/MANs, fixed wireless access, aeronautical networks, underwater networks, in vivo networks, and wireless sensors networks. His current research interests are on 5G and beyond, physical layer security, interference management (avoidance, awareness, and cancellation), cognitive radio, small cells, powerline communications, smart grid, UWB, multi-carrier wireless technologies, dynamic spectrum access, co-existence issues on heterogeneous networks, aeronautical (High Altitude Platform) communications, in vivo channel modeling and system design, and underwater acoustic communications. He has served as technical program committee chair, technical program committee member, session and symposium organizer, and workshop chair in several IEEE conferences. He is currently a member of the editorial board for the IEEE Surveys and Tutorials and the Sensors Journal. He has also served as a member of the editorial board for the IEEE Transactions on Communications, the IEEE Transactions on Cognitive Communications and Networking (TCCN), the Elsevier Physical Communication Journal, the Hindawi Journal of Electrical and Computer Engineering, and Wiley Wireless Communication and Mobile Computing Journal.

Doçent Dr. Pınar Boyraz Baykaş

Behavioral Signal Processing for Intelligent Vehicles

Pinar Boyraz Baykas, Associate Professor (Docent)

Mechanics and Maritime Sciences (M2), Division of Vehicle Safety

Chalmers University of Technology, Gothenburg, Sweden

In past two decades, we have witnessed disruptive and innovative transformations concerning how humans, machines and environment interact. These are mainly fueled by the increasing availability of real-world data (i.e. Big-Data, Naturalistic Driving Data, Medical Records), developments in Artificial Intelligence (i.e. Deep Learning) and Autonomous Cyber-Physical-Systems (i.e. Self-driving vehicles). In this seminar, we will focus on the signal processing for analyzing and modeling of human behavior in collaborative human-machine interaction scenarios.

As a special case of human-machine interaction research field, we will talk about how human drivers and intelligent vehicle technologies (i.e. partial/conditionally automated vehicles, driver assistance systems, shared control systems) interact in a collaborative manner increasing safety and efficiency. We will give detailed examples of data-driven methodologies utilizing signal processing on in-vehicle (driver-vehicle) and out-of-vehicle (vehicle-environment) data streams. This rich data stream can comprise CAN-Bus data covering vehicle dynamics, kinematics and some driver inputs (engine rpm, speed, steering wheel angle), GPS (for location), IMU (acceleration in 3-axes), in-vehicle cameras (driver’s posture, pose, head movements, facial expressions and eye-gaze), environment cameras (lane marks, traffic signs, neighboring/adjacent vehicles, lead vehicle, weather condition), LIDAR (3D obstacle map of environment).

We will explore several signal processing algorithms and their performance on this data to help the intelligent vehicle (1) understand the driver behavior (via intent recognition, fault detection, distraction detection, attention monitoring modules) for better collaboration, (2) to have context-awareness of the traffic and road conditions. Independent of the autonomy levels, all intelligent vehicle technologies should be able to recognize the driver behavior and the traffic context to be able to respond in a reliable, robust and efficient manner.

Short Bio:

Pinar Boyraz Baykas (IEEE Member since 2009) received double-major BSc. degrees in in Mechanical and Textile Engineering from Istanbul Technical University (ITU), Turkey in 2003-2004 and her PhD in Mechatronics from Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, UK in 2008. She worked as a Post-doctoral RA in the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, in U.S.A. during 2008-2010, focusing on driver behavior modeling and active safety system development. She was an Assist. Prof. during 2010-2014 and an Assoc. Prof.  during 2014-2018 in Mechanical Engineering Dept. of ITU, Turkey, conducting research in applied robotics. She was awarded by Alexander von Humboldt Foundation with Experience Researcher Fellowship during her research in applied robotics at Leibniz Universität Hannover, Germany in 2016-17. Starting from March 2018, she works as an Assoc. Prof.  at Mechanics and Maritime Sciences Dept. of Chalmers University of Technology, Gothenburg, Sweden. Her research interests broadly include applications of mathematical modelling, mechatronics, signal processing and control theory.

Dr. Salih Ergüt (TURKCELL)

Dealing with 5G complexity with AI

5G is becoming a reality as mobile service providers are racing for initial deployments and trying to identify new use cases to create business. As opposed to previous generations, 5G is designed accomodate diverse requirements from verticals in addition to providing even faster data rates.  High bandwidth applications such as 360 degree, 4K video and AR/VR, ultra delay sensitive applications  in the automotive, manufacturing, healthcare, etc. industries, and massive IoT device deployments will need to be served on the same network infrastructure.  5G addresses challenges stemming from those diverse applications with a set of technological and architectural innovations such as network slicing, massive MIMO, network densification, softwarization, control and user plane separation. As the network complexity is increased tremendously, operators and vendors are investing in AI-powered network intelligence solutions for planning, operation and maintanance, resource management, performance monitoring, energy efficiency, and network optimization as opposed to traditional manual handling. To accelerate the deployment of AI-based solutions in operator networks, overlay AI network architectures on top of existing heteregenous communication infrastructure are studied by many standardization development organizations (SDO), including ITU-T focus group on “Machine learning for future networks including 5G”.

Short Bio

Dr. Salih Ergüt has more than 15 years of experience in the telecommunication domain in academia and industry. He has worked for vendor and operator companies in the sector including Ericsson Wireless (San Diego, CA), Ericsson Silicon Valley, Aware (Boston, MA), Nextwave (San Diego, CA), Turk Telekom Group (Istanbul, Turkey) and is currently working at Turkcell 5G R&D team in Istanbul, Turkey and serving as Vice-chairman at ITU-T Focus Group on “Machine Learning for Future Networks including 5G”.

Dr. Salih Ergut received his BS in Electrical Engineering from Bilkent University (Ankara, Turkey), MS in Electrical & Computer Engineering from Northeastern University (Boston, MA), and PhD in Electrical & Computer Engineering from University of California San Diego (La Jolla, CA).

His current research interests include 5G technologies, machine learning for communication networks, big data technologies and network slicing.

Prof. Dr. Visvanattan Ramesh

Systems engineering for visual cognition


Prof. Dr. Visvanathan Ramesh is a Software Engineering Professor at the Department of Computer Science and Mathematics in the Goethe University, Frankfurt, with emphasis on Bio-Inspired Computer Vision systems. He served as the coordinator the Bernstein Focus NeuroTechnology initiative in Frankfurt at Goethe University and Frankfurt Institute for Advanced Studies from 2011 – 2016. He joined Goethe University after 16 years at Siemens Corporate Research Inc. in Princeton, NJ, where he was responsible for directing research & development in industrial vision, wireless and signal processing and multimedia systems with applications in security, safety and automation. His global team developed and deployed high-performance real-world products and solutions for video surveillance; vision based driver assistance systems, and 3D vision systems for automation and control. He has numerous publications spanning over 25 years which have focused on statistical modelling for computer vision with emphasis on systematic engineering and performance characterization of vision systems. His present research focus is on transdisciplinary research in systems engineering of intelligence with emphasis on bridging model-based design practices with modern Machine Learning and cognitive/brain sciences. Dr. Ramesh earned his Ph.D. in Electrical Engineering from the University of Washington where he defended his dissertation on “Performance Characterization of Image Understanding Algorithms”. He also was a co-author of an award winning paper on real-time tracking at the IEEE Computer Vision and Pattern Recognition Conference, 2000. He and his team received the Siemens Inventor of the Year award in 2008 for outstanding contributions in real-time vision and modelling. He was also the recipient of the IEEE Longuet-Higgins Award in 2010 for foundational contributions in computer vision.