Keynotes
Towards Building Sustainable and Resilient 6G Systems: Reconfigurable, User-centric Radio Access Network
Fumiyuki Adachi received the B.S. and Dr. Eng. degrees in electrical engineering from Tohoku University, Sendai, Japan, in 1973 and 1984, respectively. In April 1973, he joined the Electrical Communications Laboratories of NTT and started mobile communications research. From July 1992 to December 1999, he was with NTT DOCOMO, leading a research group on wideband/broadband wireless access for 3G and beyond. He contributed to developing the 3G air interface standard, known as W-CDMA. From October 1984 to September 1985, he was a United Kingdom SERC Visiting Research Fellow in the Department of Electrical Engineering and Electronics at Liverpool University. Since January 2000, he has been with Tohoku University, Sendai, Japan. He was a full professor at the Dept. of Communications Engineering of the Graduate School of Engineering until his retirement from Tohoku University in March 2016. He is a Professor Emeritus and currently a Specially Appointed Research Fellow at the International Research Institute of Disaster Science (IRIDeS), Tohoku University, and is researching the resilient wireless communication technology for beyond 5G/6G systems. His research interests lie in the area of wireless signal processing and networking, including multi-access, equalization, antenna diversity, cooperative transmission, channel coding, and radio resource management. He is IEICE Life Fellow and IEEE Life Fellow. He is the recipient of 2000 IEEE VTS Avant Garde Award, 2002 IEICE Achievement Award, 2004 Thomson Scientific Research Front Award, 2010 Prime Minister Invention Award, 2014 C&C Prize, 2017 IEEE VTS Stuart Meyer Memorial Award, and 2017 IEEE ComSoc RCC Technical Recognition Award.
Abstract
To advance mobile communications into the 6G era, research and development activities are currently being intensified around the world. 6G system aims to provide extreme communication coverage from the ground to the sea, air, and space. In my talk, I will focus on terrestrial communications and introduce efforts to rebuild the radio access network (RAN) from the perspectives of spectrum and energy efficiency, as well as resilience. The rapid growth of mobile data traffic has made it essential to utilize the mmWave band in addition to the sub-6GHz band. However, the mmWave bands have serious disadvantages of high propagation path loss and frequent blockage due to their strong rectilinear propagation nature. Introducing a reconfigurable user-centric RAN based on distributed massive MIMO (mMIMO) is a promising solution that turns the disadvantages of the mmWave band into advantages. This improves the spectrum and energy efficiency of the RAN while maintaining resilience and enables the use of renewable energy. I will introduce a framework for a reconfigurable, user-centric RAN based on distributed mMIMO, towards building sustainable and resilient 6G systems.
Fumiyuki Adachi
Professor, Tohoku University
Zhiqiang Wu
Professor, Wright State University
Intelligent Channel Sensing based Secure Cross Layer Cognitive Networking for Resilient Space and Aerial Communication
Professor Zhiqiang Wu received his B.S. from Beijing University of Posts and Telecommunications, M.S. from Peking University, Ph.D. from Colorado State University. He served as assistant professor at West Virginia University Institute of Technology from 2003 to 2005. He joined Wright State University in 2005 and currently serves as the Brage Golding Distinguished Professor. His research has been supported by NSF, AFRL, AFOSR, NASA, ONR, OFRN and DoE. He also holds visiting professorship at many institutions including Peking University Wuhan Institute of Artificial Intelligence, Tibet University, Harbin Engineering University, Huazhong University of Science and Technology, Xiamen University, Wuhan Institute of Technology, etc.
Abstract
In this talk, we briefly introduce a joint project between Wright State University, Air Force Institute of Technology, Ohio University and University of Toledo on a cognitive networking platform for resilient space and aerial communication. This project is supported by NASA, AFRL, and OFRN. Based on an intelligent channel sensing engine, we design and implement a secure cross layer cognitive networking platform to enhance the performance and security of space and aerial communication. Through collaboration with an industrial partner, the platform has been tested over satellite communication and aerial communication channels.
A New Sensing Channel Modeling Approach Based on Ray Tracing and Stochastic Methods for Vehicle-to-Everything Applications
P. Takis Mathiopoulos received the Ph.D. degree in digital communications from the University of Ottawa, Ottawa, Canada, in 1989. From 1982 to 1986, he was with Raytheon Canada Ltd., working in the areas of air navigational and satellite communications. His research activities and contributions have dealt with wireless terrestrial and satellite communication systems and network as well as in remote sensing, LiDAR systems, and information technology, including blockchain systems. In these areas, he has coauthored more than 150 journal papers published mainly in various IEEE journals, 1 book (edited), 5 book chapters, and more than 160 international conference papers. Dr. Mathiopoulos has been or currently serves on the editorial board of several archival journals, including the IET Communications as an Area Editor, the IEEE Transactions on Communications, the Remote Sensing Journal, and as Specialty Chief Editor for the Arial and Space Network Journal of Frontiers.
Abstract
A new sensing channel modeling approach jointly considering ray-tracing (RT) and stochastic methods, to accurate and efficient model sensing channels for vehicle-to-everything (V2X) applications is presented. For the former, moving targets are modeled through accurate RT simulations while for the latter a statistical approach is used for generating complex environmental clutter by emphasizing for the first time individual object modeling. This approach is used to form a feature library of objects which ensures space-time consistency while significantly improving the modeling speed. The channel transfer functions generated by RT and stochastic methods are jointly considered through coherent superposition to form a more complete sensing channel which includes both clutters and targets. To verify its effectiveness and accuracy, a comprehensive experimental study has been conducted taking systematic measurements using a 77-GHz mmWave radar as it is the prevalent equipment for sensing used for intelligent driving applications. We have considered a typical V2X scenario, with the radar deployed on vehicles traveling along roads at an urban intersection. The experimental results obtained have demonstrated that the accuracy in target distance detection and velocity estimation has improved leading to errors of less than 0.5 m and less than 0.2 m/s, respectively, while for clutter modeling, the error of power is 3 to 6 dB. Moreover, compared to traditional RT methods, the proposed approach is 20 times faster. Through the proposed approach, realistic sensing channel data can be obtained in a systematic, effective, and accurate manner, facilitating research of sensing-assisted communication applications.
P. Takis Mathiopoulos
Professor, National and Kapodistrian University of Athens
Dusit Niyato
Professor, Nanyang Technological University
Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks
Dusit Niyato is currently a President’s Chair Professor in the College of Computing & Data Science (CCDS), Nanyang Technological University, Singapore. Dusit’s research interests are in the areas of mobile generative AI, edge intelligence, quantum computing and networking, and incentive mechanism design. Currently, Dusit is serving as Editor-in-Chief of IEEE Transactions on Network Science and Engineering (TNSE). He is also an area editor of IEEE Communications Surveys and Tutorials, IEEE Transactions on Vehicular Technology (TVT), topical editor of IEEE Internet of Things Journal (IoTJ), lead series editor of IEEE Communications Magazine, and associate editor of IEEE Transactions on Wireless Communications (TWC), IEEE Transactions on Communications, IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Cognitive Communications and Networking (TCCN), IEEE Data Descriptions, IEEE Transactions on Services Computing (TSC), and ACM Computing Surveys. He was also a guest editor of IEEE Journal on Selected Areas on Communications. Dusit is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024-2026. He was named the 2017-2024 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.
Abstract
The evolution of generative artificial intelligence (GenAI) has driven revolutionary applications like ChatGPT. The proliferation of these applications is underpinned by the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE’s efficiencies, GenAI still faces challenges in resource utilization when deployed on local user devices. Therefore, we first propose mobile edge networks supported MoE-based GenAI. Rigorously, we review the MoE from traditional AI and GenAI perspectives, scrutinizing its structure, principles, and applications. Next, we present a new framework for using MoE for GenAI services in Metaverse. Moreover, we propose a framework that transfers subtasks to devices in mobile edge networks, aiding GenAI model operation on user devices. Moreover, we introduce a novel approach utilizing MoE, augmented with Large Language Models (LLMs), to analyze user objectives and constraints of optimization problems based on deep reinforcement learning (DRL) effectively. This approach selects specialized DRL experts, and weights each decision from the participating experts. In this process, the LLM acts as the gate network to oversee the expert models, facilitating a collective of experts to tackle a wide range of new tasks. Furthermore, it can also leverage LLM’s advanced reasoning capabilities to manage the output of experts for joint decisions. Lastly, we insightfully identify research opportunities of MoE and mobile edge networks.
