Dr. Mehdi Bennis is a tenured full Professor at the Centre for Wireless Communications, University of Oulu, Finland, Academy of Finland Research Fellow and head of the intelligent connectivity and networks/systems group (ICON). His main research interests are in radio resource management, heterogeneous networks, game theory and distributed machine learning in 5G networks and beyond. He has published more than 200 research papers in international conferences, journals and book chapters. He has been the recipient of several prestigious awards including the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, the 2016 Best Tutorial Prize from the IEEE Communications Society, the 2017 EURASIP Best paper Award for the Journal of Wireless Communications and Networks, the all-University of Oulu award for research, the 2019 IEEE ComSoc Radio Communications Committee Early Achievement Award and the 2020-2021 Clarviate Highly Cited Researcher by the Web of Science. Dr Bennis is an editor of IEEE TCOM and Specialty Chief Editor for Data Science for Communications in the Frontiers in Communications and Networks journal. Dr Bennis is also an IEEE Fellow.

Short abstract: The current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing. Nevertheless, the advent of a new breed of intelligent devices ranging from drones to self-driving vehicles, makes cloud-based ML inadequate. This talk will present the vision of distributed edgeintelligence featuring key enablers, architectures, algorithms and some recent results in an exciting and rapidly advancing area.

Ramesh Rao is a Senior Director of Engineering in Qualcomm Technology Licensing(QTL) division’s Technology and Product Management Team in San Diego, CA. He is responsible for working with OEM’s and startups from emerging markets and startup communities; Consulting, defining and creating custom packages that jumpstarts these companies to quickly transform their innovative ideas and prototypes into successful commercial products. He has worked at Qualcomm CDMA Technologies(QCT) from 1995 to 2015, where he led the Global Hardware Application Engineering and Technical Account Management teams, supporting OEM’s on 2G, 3G and 4G technologies and launching commercial products implementing these technologies. He established and led QCT’s Customer Engineering center’s in Bangalore, India(2008) and Sao Paulo, Brazil(2011). From 2015 to 2016, he was the North American Engineering lead for a startup with product ambitions in China, India and the USA. He has been in his current role with QTL since 2016 where he has been working with licensees and startups in emerging economies across South East Asia, Latam, India and Europe and leads the setup and management of QTL Labs to support the domestic ecosystems in SEA, India, China and North America. His work in QTL leverages two plus decades of wireless industry experience, across Mobile and IoT projects spanning 5G Products, 4G Feature Phones, entry tier Smart Phones, Smart Metering, Asset Tracking, IIoT, Point of Sales(POS), Smart City Deployments, University Training Programs etc. He has a Bachelor’s degree in Electronics and Communications Engineering and a Master’s degree in Computer Science.

Short abstract: 5G is the unifying connectivity fabric of our modern, hyperconnected societies, and thanks to Qualcomm, the addition of artificial intelligence to 5G is fueling the innovations of the future. Traditionally, AI is viewed as “intelligence” (datasets, learning modes, algorithms, etc.) hosted in a centralized cloud. While this has been proven deeply useful for a variety of cutting-edge applications, both existing and new, factors such as privacy, reliability, latency, and power consumption are driving the industry toward on-device AI. When combined with connectivity like 5G to distribute intelligence and computing, this is known as the “Connected Intelligent Edge.” Enabled by 5G and AI, the Connected Intelligent Edge is where wireless communications, on-device computing, and low power consumption turn into new use cases – creating new business opportunities, business models, and revenue streams.
Locating AI on the edge for real-time processing allows millisecond and microsecond decisions in implementations where this low latency is essential. The 5G and AI-powered connected intelligent edge also allows for more power-efficient processing, vision, sensing, security and intelligence capabilities,. AI and 5G will continue to go hand in hand as complementary ingredients that will enable us to realize the full potential of our connected world.
In this presentation, I’ll focus on things happening today and expected to happen in the future in three areas: how AI will make 5G better, how 5G will make AI-powered experiences better, and how the future of distributed learning over wireless networks will look in a 5G world. I will also touch upon the key research vectors that will enable the path to 6G in the future.

Dr. Tan, Chee Wei received the M.A and Ph.D. degrees in Electrical and Computer Engineering from Princeton University. His research interests are wireless networks, artificial intelligence, optimization and distributed machine learning. He was a Senior Fellow of the Institute for Pure and Applied Mathematics (IPAM) and was a Postdoctoral Scholar in the NetLab Group at Caltech. His industrial experience includes consulting for Tencent AI Lab, Qualcomm R&D (QRC). Dr. Tan has served as Editor for the IEEE/ACM Transactions on Networking, IEEE Transactions on Communications and IEEE Communications Surveys and Tutorials. He received the Princeton University Wu Prize for Excellence, Google Faculty Award, NRF Fellowship, university teaching awards, IEEE Information Theory Society 2015 Chapter of the Year award, and was selected twice for the U.S. National Academy of Engineering China-America Frontiers of Engineering Symposium.

Professor Alouini general research interests include design and performance analysis of diversity combining techniques, MIMO techniques, multi-hop/cooperative communications systems, optical wireless communication systems, cognitive radio systems, green communication systems and networks, wireless communication systems and networks in extreme environments, and integrated ground-airborne-space networks. He is currently actively working on addressing the uneven global distribution, access to, and use of information and communication technologies by studying and developing new generations of aerial and space networks as a solution to provide connectivity to far-flung, less-populated, and/or hard-to-reach areas. Professor Alouini has published several papers on the above subjects and he is co-author of the textbook Digital Communication over Fading Channels published by Wiley Interscience. He has also won several awards in his career. For instance, he recently received the 2021 Institute of Electrical and Electronics Engineering (IEEE) Communication Society Education Award, the 2021 TWAS Award in Engineering Sciences, the 2021 NSP Obada Prize (Sponsored by the African Academy of Sciences), the 2020 Kuwait Prize in Applied Sciences, the 2020 IEEE Vehicular Technology Society James Evans Avant Garde Award, and the 2019 Technical Achievement Award of the IEEE Communication Society Communication Theory Technical Committee. Prior to this, he was honored in 2017 with the Organization of Islamic Cooperation (OIC) Science and Technology (S&T) Achievement Award in Engineering Science at the First OIC Summit on Science and Technology, Astana, Kazakhstan. He also received the 2016 Recognition Award of the IEEE Communication Society Wireless Technical Committee as well as the 2016 Abdul Hameed Shoman Award for Arab Researchers in Engineering Sciences. Other recognitions include his selection as (i) Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Optica (formerly the Optical Society of America (OSA)), the African Academy of Science (AAS), the European Academy of Science and Arts (EASA), the Academia Europaea (AE), and The World Academy of Science (TWAS), (ii) IEEE Distinguished Lecturer for the IEEE Communication Society and for the IEEE Vehicular Technology Society, (iii) member for several times in the annual Thomson ISI/Clarivate Web of Knowledge list of Highly Cited Researchers as well as the AMiner 2020 list of the AI 2000 Most Influential Scholars in the area of Internet of Things and the Shanghai Ranking/Elsevier list of Most Cited Researchers, and (iv) a co-recipient of best paper/poster/video awards in 19 journals/conferences/challenges.

Dr. Anwar graduated (cum laude) from the department of Electrical Engineering (Telecommunications), Institut Teknologi Bandung (ITB), Bandung, Indonesia in 2000 for his Bachelor degree (S.T.). He received Master and Doctor Degrees from Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan, in 2005 and 2008, respectively. Dr. Anwar was in University of Melbourne, Australia, in 2007, University of Oulu, Finland, in 2010, and Cranfield University, United Kingdom, in 2018, as a visiting professor. In September 2008, he was with the School of Information Science, Japan Advanced Institute of Science and Technology (JAIST) as an assistant professor. Since September 2016 Dr. Anwar is with the school of electrical engineering, Telkom University, Bandung, Indonesia as an associate professor, the director of the Center for Advanced Intelligent Communications (AICOMS) (formerly the Center for Advanced Wireless Technologies (AdWiTech) 2016–2020), and) as one of the Indonesia University Center of Excellences supported by the Ministry of Higher Education since July 2020. AICOMS received The 2021 Rector Awards on best research center in the category of research grant achievement in 2021. His research interests are error correction coding, iterative decoding, coding for super-dense networks, network information theory, signal processing for wireless communications, molecular communications, quantum communications and error corrections. He serves as a reviewer for a number of main journals and conferences in the area of wireless communications, coding theory and signal processing. Dr. Anwar is a senior member of IEEE (Information Theory society, Communications society).

Short abstract: This tutorial will cover the introductions to all aspects of artificial intelligence (AI) development for channel coding scheme from classical, molecular, and quantum error correction coding (ECC), where the differences between them will be highlighted. The talk will first start from the historical background of channel coding, AI, and quantum information theory. The duality particle and wave will be root of the discussion to connect those three types of coding scheme of classical, molecular, quantum ECC. The classical coding schemes will be started from the parity check matrix H and generator matrix G of low-density parity check codes (LDPC) since they will also be used in molecular communications and quantum ECC. The next talk will move to the tutorial on the basic of machine learning (ML) for channel coding started from the demonstration of neural network for solving the XOR problem, which is the workhorse of almost all classical and molecular channel coding scheme. The tutorial then moves to the idea of future ML-based channel coding, i.e., rateless channel coding scheme based on the reinforcement learning. In this session, deep philosophical understanding on matrix H is important since the selection on the rate will be combined with the learning algorithm. The last session will be on the introduction to the quantum ECC, where the tutorial will be started by the definition of qubit state and some important quantum gates to be involved in the quantum ECC. The tutorial then will be closed with the demonstration of quantum machine learning for future complex demapper, where 5G NR complex binary phase shift keying (CBPSK) modulation is used as an example, before talking in brief about the future challenges on machine learning for channel coding.

She is currently a Senior Lecturer at Universiti Teknologi Malaysia (UTM). She graduated from Universiti Sains Malaysia with a B.Eng in Electrical and Electronics in 2000. She received M.Sc. in Information Technology and Science Qualitative from Universiti Teknologi MARA in 2004 and Ph.D. from the University of Bristol, the UK in 2012. She has expertise in scheduling and resource allocation, Quality-of-Service (QoS), Quality of Experience (QoE), and cross-layer design between MAC and PHY layers. She introduced Greedy Latency and Dynamic Burst Mapping techniques in a single cell of a WiMAX system. Then she expanded research on Intercell Interference Coordination (ICIC) by introducing an adaptive Soft Frequency Reuse. She has a research interest in now towards wireless communication applications, however, fundamental research of wireless communication based on simulations work.

Short abstract: The main objective of this tutorial is to provide a fundamental background of the 5G system and then study recent advances in 5G to address practical challenges in deploying 5G networks. Some 5G use cases are presented to aid the audiences in having a better understanding of how 5G may uplift the human lifestyle and businesses. The 5G advanced, an evolution towards the 6G besides various candidates for the 6G technology is also highlighted. The emerging issues include multiple access, wireless caching, data offloading, network slicing, and open radio access networks, which are significant to next-generation networks such as 5G and 6G are also reviewed. Finally, the important challenges and future research directions on the path to 6G are also discussed.