Signal Processing on Networks: From Graphs to Brains

Location: MENACOMM 2022

Date: 10 Dec 2022


Many applications are dealing with ever-increasing data. Not only the sheer amount of data has exponentially increased, the data modality and structure have seen become significantly richer. Examples include internet of things, sensor networks, but also biological networks, and the brain network, in particular.

Network science has emerged as the multidisciplinary field that processes network data using methods from graph theory, statistical mechanics, statistical inference, advanced visualization, and domain knowledge from applied fields. More specifically, graph signal processing (GSP) became a new research theme at the intersection between signal processing and graph theory, with a particular focus on processing graph signals that associate values to the nodes of the graph. Conventional operations on signals such as sampling and filtering can then be redesigned accounting for the graph backbone.

In this talk, GSP will be presented as a novel framework to represent network data, with a focus on the application to neuroimaging, in particular, state-of-the-art magnetic resonance imaging (MRI) that provides unprecedented opportunities to study brain structure (anatomy) and function (physiology). We will take the perspective of considering the structural connectome as derived from diffusion-weighted MRI to represent the major neural pathways in white matter. Next, the functional MRI data is considered as a time-dependent graph signal that is expressed on this anatomical background. The graph Laplacian can be used to measure smoothness of the graph signals and its eigendecomposition to define the graph-equivalent of the Fourier transform. The power spectral density of brain activity in the graph Fourier domain exhibits a power-law trend, similar to the spectral signature of natural images. Therefore, we define complementary low- and high-pass graph filters that separate graph signals in their smooth and non-smooth part, respectively. The nodal energy ratio between these signals is then interpreted as a measure of “coupling” between structure and function, termed the structural decoupling index (SDI). To provide statistical inference, we extend the well-known Fourier phase randomization method to generate surrogate data to the graph setting. The SDI reveals a behaviorally-relevant spatial gradient, where sensory regions tend to be more coupled with structure, and high-level cognitive ones less so. In addition, SDI maps are informative both for task decoding and individual fingerprinting pointing again toward the different involvement of unimodal and transmodal regions, respectively. Finally, recent work will highlight how the spatial resolution of GSP brain graphs can be increased to the voxel level, representing a few hundredth thousands of nodes, where explicit eigendecompositions becomes unfeasible.

Speaker’s Bio:

Dimitri Van De Ville (F) received the M.S. degree in Computer Sciences and the Ph.D. degree in Computer Science Engineering from Ghent University, Belgium, in 1998, and 2002, respectively. He was a post-doctoral fellow (2002-2005) at the lab of Prof. Michael Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, before becoming group leader for the Signal Processing Unit at the University Hospital of Geneva, Switzerland, as part of the Centre d’Imagerie Biomédicale (CIBM). In 2009, he received a Swiss National Science Foundation professorship and since 2015 became Professor of Bioengineering at the École polytechnique fédérale de Lausanne (EPFL) (Institute of Bioengineering), jointly affiliated with the University of Geneva (Department of Radiology and Medical Informatics), Switzerland.

Dr. Van De Ville serves as Senior Editor, IEEE Transactions on Signal Processing (2019-present); Editor, SIAM Journal on Imaging Science (2018-present); Associate Editor, IEEE Transactions on Image Processing (2006 to 2009); Associate Editor, IEEE Signal Processing Letters (2004 to 2006); Chair, Bio Imaging and Signal Processing (BISP) Technical Committee (2012-2013); Founding Chair, EURASIP Biomedical Image & Signal Analytics SAT (2016-2018); Co-Chair, Biennial Wavelets & Sparsity series conferences, together with Y. Lu and M. Papadakis. He is the recipient of the Pfiz

AI Based Ultra-Reliable Wireless Networked Control Systems in 6G

Location: MENACOMM 2022

Date: 7 December 2022


Unlike previous generation networks that were mainly designed to meet the requirements of human-type communications, 5G networks enable the collection of data from the machines with the total number of devices expected to be about 26 billion in 2026 according to Ericsson Mobility Report. The next step in 6G systems is to enable a new spectrum of control applications based on these data, such as extended reality, remote surgery, autonomous vehicle platoons. The design of communication systems for control applications requires meeting the strict delay and reliability requirements of communication systems and addressing the semantics of the control systems. In the first part of this talk, ultra-reliable communication techniques, technologies and architectures are introduced by demonstrating the usage of extreme value theory, federated learning and reinforcement learning. In the second part of the talk, the fundamental paradigm shift from the Shannon paradigm is introduced. While Shannon paradigm aims to guarantee the correct reception of each single transmitted bit, irrespective of the meaning conveyed by transmitted bits, communication for control applications focuses on guaranteeing the success of the task execution, such as plant stability for automated production lines, detection accuracy in cooperative vehicle systems. Novel AI based resource allocation techniques for the joint design of control and communication systems are presented. 



Sinem Coleri is Professor in the department of Electrical and Electronics Engineering at Koc University. She is also the founding director of Wireless Networks Laboratory (WNL). Her research interests are in wireless communications and networking with applications in machine-to-machine communication, sensor networks and intelligent transportation systems. Dr. Coleri received the BS degree in electrical and electronics engineering from Bilkent University in 2000, the M.S. and Ph.D. degrees in electrical engineering and computer sciences from University of California Berkeley in 2002 and 2005. She worked as a research scientist in Wireless Sensor Networks Berkeley Lab under sponsorship of Pirelli and Telecom Italia from 2006 to 2009. Since September 2009, she has been a faculty member in the department of Electrical and Electronics Engineering at Koc University.

Dr. Coleri has more than 180 peer-reviewed publications with citations over 8900 (Google scholar profile). She has received numerous awards and recognitions, including TUBITAK (The Scientific and Technological Research Council of Turkey) Incentive Award and IEEE Vehicular Technology Society 2020 Neal Shepherd Memorial Best Propagation Paper Award in 2020, College of Engineering Outstanding Faculty Award at Koc University and IEEE Communications Letters Exemplary Editor Award as Area Editor in 2019, Turkish Academy of Sciences Distinguished Young Scientist (TUBA-GEBIP) in 2015.

Dr. Coleri has been Area Editor of IEEE Communications Letters and IEEE Open Journal of the Communications Society since 2019, Editor of IEEE Transactions on Communications since 2017 and Editor of IEEE Transactions on Vehicular Technology since 2016. She is an IEEE Fellow.



Is NOMA Efficient for Multi-Antenna Systems

Location: Zoom

Date: 9 September 2021


In the past few years, a large body of literature has been created on downlink Non-Orthogonal Multiple Access (NOMA), employing superposition coding and Successive Interference Cancellation (SIC), in multi-antenna wireless networks. Furthermore, the benefits of NOMA over Orthogonal Multiple Access (OMA) have been highlighted. In this talk, we take a critical and fresh look at the downlink Next Generation Multiple Access (NGMA) literature. Instead of contrasting NOMA with OMA, we contrast NOMA with two other multiple access baselines. The first is conventional Multi-User Linear Precoding (MU–LP), as used in Space-Division Multiple Access (SDMA) and multi-user Multiple-Input Multiple-Output (MIMO) in 4G and 5G. The second, called Rate-Splitting Multiple Access (RSMA), is based on multi-antenna Rate Splitting (RS). It is also a non-orthogonal transmission strategy relying on SIC developed in the past few years in parallel and independently from NOMA. We show that there is some confusion about the benefits of NOMA, and we dispel the associated misconceptions.


Speaker’s Bio: 

Bruno Clerckx is a (Full) Professor, the Head of the Wireless Communications and Signal Processing Lab, and the Deputy Head of the Communications and Signal Processing Group, within the Electrical and Electronic Engineering Department, Imperial College London, London, U.K. He received the M.S. and Ph.D. degrees in applied science from the Université Catholique de Louvain, Louvain-la-Neuve, Belgium, in 2000 and 2005, respectively. From 2006 to 2011, he was with Samsung Electronics, Suwon, South Korea, where he actively contributed to 4G (3GPP LTE/LTE-A and IEEE 802.16m) and acted as the Rapporteur for the 3GPP Coordinated Multi-Point (CoMP) Study Item. Since 2011, he has been with Imperial College London, first as a Lecturer from 2011 to 2015, Senior Lecturer from 2015 to 2017, Reader from 2017 to 2020, and now as a Full Professor. From 2014 to 2016, he also was an Associate Professor with Korea University, Seoul, South Korea. He also held various long or short-term visiting research appointments at Stanford University, EURECOM, National University of Singapore, The University of Hong Kong, Princeton University, The University of Edinburgh, The University of New South Wales, and Tsinghua University.

He has authored two books on “MIMO Wireless Communications” and “MIMO Wireless Networks”, 200 peer-reviewed international research papers, and 150 standards contributions, and is the inventor of 80 issued or pending patents among which 15 have been adopted in the specifications of 4G standards and are used by billions of devices worldwide. His research area is communication theory and signal processing for wireless networks. He has been a TPC member, a symposium chair, or a TPC chair of many symposia on communication theory, signal processing for communication and wireless communication for several leading international IEEE conferences. He was an Elected Member of the IEEE Signal Processing Society SPCOM Technical Committee. He served as an Editor for the IEEE TRANSACTIONS ON COMMUNICATIONS, the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, and the IEEE TRANSACTIONS ON SIGNAL PROCESSING. He has also been a (lead) guest editor for special issues of the EURASIP Journal on Wireless Communications and NetworkingIEEE ACCESS, the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, and the PROCEEDINGS OF THE IEEE. He was an Editor for the 3GPP LTE-Advanced Standard Technical Report on CoMP. He is an IEEE ComSoc Distinguished Lecturer 2021-2022.

Wireless Systems Design in the Beyond 5G Era: Promises of Deep Learning and Deep Reinforcement Learning

Location: Zoom.

Date: 27 Sep 2020


Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problems, often it is computationally demanding to obtain the optimal resource allocation. Machine learning, especially Deep learning (DL), is a powerful tool where a multi-layer neural network can be trained to model a resource management algorithm using network data. Therefore, resource allocation decisions can be obtained without intensive online computations which would be required otherwise for the solution of resource allocation problems.  Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems. Unlike deep learning (DL), DRL does not require any optimal/near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data. In this talk, I shall present a novel centralized DRL-based downlink power allocation scheme for a multi-cell system intending to maximize the total network throughput. Specifically, I shall discuss a deep Q-learning (DQL) approach to achieve near-optimal power allocation policy. I shall present some simulation results to compare the proposed DRL-based power allocation scheme with the conventional schemes in a multi-cell scenario.

Speaker’s Bio:

Ekram Hossain (F’15) is a Professor in the Department of Electrical and Computer Engineering at University of Manitoba, Canada. He is a Member (Class of 2016) of the College of the Royal Society of Canada. Also, he is a Fellow of the Canadian Academy of Engineering. Dr. Hossain’s current research interests include design, analysis, and optimization beyond 5G/6G cellular wireless networks. He was elevated to an IEEE Fellow “for contributions to spectrum management and resource allocation in cognitive and cellular radio networks”. To date, his research works have received 30,500+ citations (in Google Scholar, with h-index = 91). He received the 2017 IEEE ComSoc TCGCC (Technical Committee on Green Communications & Computing) Distinguished Technical Achievement Recognition Award “for outstanding technical leadership and achievement in green wireless communications and networking”. Dr. Hossain has won several research awards including the “2017 IEEE Communications Society Best Survey Paper Award” and the “2011 IEEE Communications Society Fred Ellersick Prize Paper Award”. He was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2017, 2018, and 2019. Currently he serves as the Editor-in-Chief of IEEE Press and an Editor for the IEEE Transactions on Mobile Computing. Previously, he served as the Editor-in-Chief for the IEEE Communications Surveys and Tutorials (2012-2016). He is a Distinguished Lecturer of the IEEE Communications Society and the IEEE Vehicular Technology Society. Also, he is an elected member of the Board of Governors of the IEEE Communications Society for the term 2018-2020.

Driverless Cars: Alice or Bob?

Location: Zoom.

Date: 14 Oct 2020


You are driving with an expert passenger named Alice. She watches how you drive, and assesses your every move, reaction, or lack of reaction, patiently waiting to provide help. Bob, on the other hand, will be your chauffer, and you let him take over. This is the driverless car, and you are entirely reliant on Bob’s expertise.  Whom do you choose, Alice or Bob?

The race to full autonomy is on, but smart infrastructure is needed for mass adoption. This requires resilient coordination, self-healing networks, learning, and rapid collaborative decision making with humans and machines. The problem difficulty is driven by environmental variation and complexity, tempo, and interaction between autonomous and human operation, while design is complicated by heterogeneity, scale, and communications rate.

Speaker’s Bio:

Brian M. Sadler (Fellow IEEE, Fellow ARL) is the Army Senior Scientist for Intelligent Systems at the Army Research Laboratory (ARL) in Adelphi, MD, and lectured at Johns Hopkins University in communications and signal processing for 15 years. He has been an Associate and Guest Editor for a variety of journals in communications, signal processing, and robotics, including the IEEE Journal on Selected Areas in Communications, IEEE Transactions on Signal Processing, and IEEE Transactions on Robotics, as well as IEEE JSTSP, IEEE SP Magazine, International Journal of Robotics Research, and Autonomous Robots.  He received Best Paper Awards from the IEEE Signal Processing Society in 2006 and 2010, was a Distinguished Lecturer for the IEEE SPS Society in 2017-2018, and was General Co-Chair of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP’16). His research focuses on multi-disciplinary approaches to distributed intelligent systems, incorporating communications networking, distributed processing, learning, and control. This includes collaborative autonomy that blends resilient networking and processing, physical layer techniques for robust security and authentication, and new ways for using low-VHF communications in complex environments.



Location: JCSC 2018.

Date: November 2018.


Since the development of the 4G LTE standards around 2010, the research communities both in academia and industry have been brainstorming to predict the use cases and scenarios of 2020s, to determine the corresponding technical requirements, and to develop the enabling technologies, protocols, and network architectures towards the next-generation (5G) wireless standardization. This exploratory phase is winding down as the 5G standards are currently being developed with a scheduled completion date of late-2019; the 5G wireless networks are expected to be deployed globally throughout 2020s. As such, it is time to reinitiate a similar brainstorming endeavour followed by the technical groundwork towards the subsequent generation (6G) wireless networks of 2030s.

One reasonable starting point in this new 6G discussion is to reflect on the possible shortcomings of the 5G networks to-be-deployed. 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 UAVs requiring connectivity (ex: delivery drones), thus the 3D super-connectivity.

In this lecture, we will present a 5-layer vertical architecture 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: 200-1,000 km
• Geostationary Orbit (GEO) satellites; altitude: 35,786 km

In the absence of a clear technology roadmap for the 2030s, the lecture has, to a certain extent, an exploratory view point to stimulate further thinking and creativity. We are certainly at the dawn of a new era in wireless research and innovation; the next twenty years will be very interesting.

Speaker’s Biography:

Halim Yanikomeroglu (FIEEE, FEIC, FCAE) was born in Giresun, Turkey. He received the B.Sc. degree in electrical and electronics engineering from the Middle East Technical University, Ankara, Turkey, in 1990, and the M.A.Sc. degree in electrical engineering (now ECE) and the Ph.D. degree in electrical and computer engineering from the University of Toronto, Canada, in 1992 and 1998, respectively.

During 1993–1994, he was with the R&D Group of Marconi Kominikasyon A.S., Ankara, Turkey. Since 1998 he has been with the Department of Systems and Computer Engineering at Carleton University, Ottawa, Canada, where he is now a Full Professor. His research interests cover many aspects of wireless communications systems and networks. Dr. Yanikomeroglu has supervised 28 PhD and 30 MASc students (all completed with theses); several of his PhD students received the Carleton University Senate Medal for Outstanding Doctoral Thesis. He has coauthored ~580+ published peer-reviewed research papers including 266 papers in 30 different IEEE journals; these publications have received 21,700+ citations (h-index: 64). He has given around 110 keynotes, seminars, tutorials, and panel talks in the last five years. Dr. Yanikomeroglu’s collaborative research with industry (including the leading players in the ICT domain) resulted in 39 granted patents.

Dr. Yanikomeroglu is a Fellow of the IEEE with the citation “for contributions to wireless access architectures in cellular networks”, a Fellow of the Engineering Institute of Canada (EIC),a Fellow of the Canadian Academy of Engineering (CAE), and an Expert Panelist of the Council of Canadian Academies (CCA|CAC). He is a Distinguished Speaker for the IEEE Communications Society and the IEEE Vehicular Technology Society on 5G/6G wireless technologies. Dr. Yanikomeroglu is currently serving as a Member of IEEE ComSoc Governence Council (2023-2024), IEEE ComSoc Conference Council (2022-2023), and IEEE ComSoc GIMS (2023-2025). He has been involved in the organization of the IEEE Wireless Communications and Networking Conference (WCNC) from its inception in 1998 in various capacities including serving as a Executive Committee member and the Technical Program Chair or Co-Chair of WCNC 2004 (Atlanta), WCNC 2008 (Las Vegas), and WCNC 2014 (Istanbul); currently, he is serving as the Chair of the WCNC Steering Committee (2019-2024). He is also a Member of the IEEE PIMRC (International Symposium on Personal, Indoor and Mobile Radio Communications) Steering Committee (2019-2024). He was the General Co-Chair of the IEEE 72nd Vehicular Technology Conference (VTC 2010-Fall) held in Ottawa, and the General Chair of the IEEE 86th Vehicular Technology Conference (VTC 2017-Fall) held in Toronto. He has served in the editorial boards of the IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, and IEEE Communications Surveys & Tutorials. He was the Chair of the IEEE’s Technical Committee on Personal Communications (now called Wireless Communications Technical Committee with 1,500+ members).

Dr. Yanikomeroglu is a recipient of the IEEE Communications Society Fred W. Ellersick Prize in 2021, IEEE Vehicular Technology Society Stuart Meyer Memorial Award in 2020, and IEEE Communications Society Wireless Communications Technical Committee Recognition Award in 2018. He is also a recipient of the IEEE Ottawa Section Outstanding Service Award in 2018, the IEEE Ottawa Section Outstanding Educator Award in 2014, Carleton University Faculty Graduate Mentoring Award in 2010, the Carleton University Graduate Students Association Excellence Award in Graduate Teaching in 2010, and the Carleton University Research Achievement Award in 2009 and 2018. Dr. Yanikomeroglu received best paper awards at IEEE Comptetion on Non-Terrestrial Networks for B5G and 6G in 2022, IEEE International Conference in Communications (ICC) 2021 and IEEE Conference on Wireless for Space & Extreme Environments (WISEE) 2021 and 2022. Dr. Yanikomeroglu spent the 2011–2012 academic year at TOBB University of Economics and Technology, Ankara, Turkey, as a Visiting Professor. He is a registered Professional Engineer in the province of Ontario, Canada.

Dr. Yanikomeroglu and his wife Dr. Berna Akcakir has three children: Erdem (BEng in Computer Systems Engineering), Sezgi (first year, Faculty of Medicine, University of Toronto), and Esin (first year, Health Sciences, Western University).