Abstract

For decades, the wireless link between transmitter and receiver has been seen as a mere bit pipe whose goal is to faithfully reconstruct the exact transmitted signal at the receiver, without paying attention to the meaning or effect of the source message. This classical design may excel in delivering high communication rates and low bit-level errors, but its limitations become apparent when faced with the challenge of transmitting massive data streams for connected intelligence, Internet of Senses, or holographic applications, where the message intent and effectiveness must be considered, and extremely stringent requirements for reliability and latency must be met, often simultaneously. In this regard, the concept of semantic communication, in which the meaning of the source messages are incorporated in the design of a communication link, has recently emerged as a promising solution. However, despite a recent surge of efforts in this area, remarkably, the research landscape is still limited to basic constructs in which even the very definition of "semantics" remains ambiguous. In this talk, we seek to remove this ambiguity and present a bold, forward-looking vision on how to build and design semantic communication networks from the ground-up. In particular, we opine that major breakthroughs in semantic communications can only be made by equipping the communication nodes with the capability to exploit information semantics at a fundamental level (from the data structure and relationships) which enables them to build a knowledge base, reason on their data, and engage in a form of communication using a machine language, similar to human conversation, with the capability to deduce meaning from the data in a manner akin to human reasoning. Towards this goal, we introduce our holistic vision for semantic communications that is firmly grounded in rigorous artificial intelligence (AI) and causal reasoning foundations, with the potential to revolutionize the way information is modeled, transmitted, and processed in communication systems. We show how, by embracing semantic communication through our proposed vision, we can usher in a new era of knowledge-driven, reasoning wireless networks that are more sustainable and resilient than today's data-driven, knowledge-agnostic networks. We also shed light on how this framework can create AI-native networks - a key requirement of future wireless systems. As a key step towards enabling this paradigm shift, we present our recent key results in this area, with foundations in AI, theory of mind, and game theory, that showcase how the proposed approach for semantic communications can reduce the volume of data circulating in a network while improving reliability, two critical requirements for emerging wireless services, such as connected intelligence and digital twins. We conclude with a discussion on future opportunities in this exciting area.

Biography

Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo, Norway in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network sciEnce, Wireless, and Security (NEWS) laboratory. He is also the Next-G Wireless Faculty Lead at Virginia Tech's Innovation Campus. His research interests include wireless networks (5G/6G/beyond), machine learning, game theory, security, UAVs, semantic communications, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE. He is also the recipient of the NSF CAREER award in 2013, the AFOSR summer faculty fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the (co-)author of eleven conference best paper awards at IEEE WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM (2018 and 2020), IFIP NTMS in 2019, IEEE ICC (2020 and 2022). He is the recipient of the 2015 and 2022 Fred W. Ellersick Prize from the IEEE Communications Society, and of the IEEE Communications Society Marconi Prize Award in 2023. He was also a co-author of the papers that received the IEEE Communications Society Young Author Best Paper award in 2019, 2021, and 2023. Other recognitions include the 2017 IEEE ComSoc Best Young Professional in Academia award, the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and the 2019 IEEE ComSoc Communication Theory Technical Committee Early Achievement Award. From 2015-2017, Dr. Saad was named the Stephen O. Lane Junior Faculty Fellow at Virginia Tech and, in 2017, he was named College of Engineering Faculty Fellow. He received the Dean's award for Research Excellence from Virginia Tech in 2019. He was also an IEEE Distinguished Lecturer in 2019-2020. He has been annually listed in the Clarivate Web of Science Highly Cited Researcher List since 2019. He currently serves as an Area Editor for the IEEE Transactions on Network Science and Engineering and the IEEE Transactions on Communications. He is the Editor-in-Chief for the IEEE Transactions on Machine Learning in Communications and Networking.

Abstract

In recent years, mobile devices are equipped with increasingly advanced computing capabilities, which opens up countless possibilities for meaningful applications. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, multi-access edge computing (MEC) has been proposed to bring intelligence closer to the edge, where data is originally generated. However, conventional edge ML technologies still require personal data to be shared with edge servers. Recently, in light of increasing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train a local ML model required by the server. The end devices then send the local model updates instead of raw data to the server for aggregation. FL can serve as enabling technology in MEC since it enables the collaborative training of an ML model and also enables ML for mobile edge network optimization. However, in a large-scale and complex mobile edge network, FL still faces implementation challenges with regard to communication costs and resource allocation. In this talk, we begin with an introduction to the background and fundamentals of FL. Then, we discuss several potential challenges for FL implementation such as unsupervised FL and matching game based multi-task FL. In addition, we study the extension to Federated Analysis (FA) with potential applications such as federated skewness analytics and federated anomaly detection.

Biography

Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor in Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in Electrical and Computer Engineering Department as well as Computer Science Department at University of Houston, Texas. His research interests include security, wireless resource allocation and management, wireless communication and networking, game theory, and wireless multimedia. Dr. Han is an NSF CAREER award recipient of 2010. Dr. Han has several IEEE conference best paper awards, and winner of 2011 IEEE Fred W. Ellersick Prize, 2015 EURASIP Best Paper Award for the Journal on Advances in Signal Processing and 2016 IEEE Leonard G. Abraham Prize in the field of Communication Systems (Best Paper Award for IEEE Journal on Selected Areas on Communications). Dr. Han is the winner 2021 IEEE Kiyo Tomiyasu Award. He has been an IEEE fellow since 2014, AAAS fellow since 2020, IEEE Distinguished Lecturer from 2015 to 2018 and ACM Distinguished Speaker from 2022-2025. Dr. Han is a 1% highly cited researcher according to Web of Science since 2017.

Abstract

It is expected that there will be 100 Billion ‘Internet-of-Things’ devices by the year 2025. Thus, the need for improved wireless reliability and latency is greater than ever. However, implementing algorithms to ensure low-latency communication for massive numbers of power-constrained mobile devices conflicts directly with the need for ultra-reliability. Recent advances in communication technologies and powerful computation platforms open opportunities to implement a wide range of breakthrough applications, especially for time sensitive services in industrial automation. In terms of communication perspective, 6G with ultra-reliable and low latency communications (URLLC) will play a vital role in the development and deployment of mission-critical applications, which require high demands on reliability and low latency communications. This opens opportunities to enable a wide range of new applications such as virtual reality (VR) with a 360-degree view, factory automation, autonomous vehicles, remote healthcare, etc. In addition, the development of digital twin opens new opportunities for transforming the cyber-physical systems in terms of intelligence, efficiency and flexibility. However, there are still many technical issues to be resolved to achieve high reliability and low latency with digital twin and apply this technology in practical scenarios due to the complexity of resource allocation in short packet transmissions. This talk will discuss digital twin technologies in industrial automation that require high data rates with ultra-reliability at very low latency for which URLLC is a natural choice. This talk discusses a joint communications and computation design of URLLC multi-tier computing in 6G that supports digital twin networks, not only fundamental requirements, but also enabling technologies, visions, and future challenges

Biography

Dr. Trung Q. Duong (IEEE Fellow and AAIA Fellow) is a Chair Professor in Telecommunications at Queen’s University Belfast, U.K. and a Research Chair of the Royal Academy of Engineering, U.K. His current research interests include optimisation, signal processing, and machine learning in wireless communications. He has published 450+ books/bookchapters/papers with 17,000+ citations and h-index 70. He has served as an Editor for many reputable IEEE journals (IEEE Trans on Wireless Communications, IEEE Trans on Communications, IEEE Trans on Vehicular Technology, EEE Communications Surveys & Tutorials, IEEE Communications Letters, and IEEE Wireless Communications Letters) and has been awarded best paper awards in many flagship conferences including IEEE ICC 2014, IEEE GLOBECOM 2016, 2019, and 2022. He is the recipient of the Research Fellowship (2015-2020) and Research Chair (2020-2025) of the Royal Academy of Engineering. In 2017, he was awarded the first Newton Prize from the UK government for the best research and innovation that promotes the economic development and social welfare. He is a Fellow of IEEE and a Fellow of Asia-Pacific Artificial Intelligence Association (AAIA).