Keynote speakers
College of Computing & Data Science (CCDS), Nanyang Technological University, Singapore
Title: Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking
Bio:
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 (impact factor 7.9). He is also the past Editor-in-Chief and current area editor of IEEE Communications Surveys and Tutorials (impact factor 46.7), also the area editor of IEEE Transactions on Vehicular Technology, topical editor of IEEE Internet of Things Journal, lead series editor of IEEE Communications Magazine, topic editor of IEEE Transactions on Services Computing. He was named the 2017-2024 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.
Abstract
Low-Altitude Economic Networking (LAENet) aims to support diverse flying applications below 1,000 meters by deploying various aerial vehicles for flexible and cost-effective aerial networking. However, complex decision-making, resource constraints, and environmental uncertainty pose significant challenges to the development of the LAENet. Reinforcement learning (RL) offers a potential solution in response to these challenges but has limitations in generalization, reward design, and model stability. The emergence of large language models (LLMs) offers new opportunities for RL to mitigate these limitations. We first present a tutorial about integrating LLMs into RL by using the capacities of generation, contextual understanding, and structured reasoning of LLMs. We then propose an LLM-enhanced RL framework for the LAENet in terms of serving the LLM as information processor, reward designer, decision-maker, and generator. Moreover, we conduct a case study by using LLMs to design a reward function to improve the learning performance of RL in the LAENet. Finally, we provide a conclusion and discuss future work.
Title: Generalized Pinching-Antenna Systems: A New Paradigm for Wireless Transceiver Designs
Bio:
Zhiguo Ding is currently a Professor in Communications at Nanyang Technological University and an Academic Visitor at Princeton University. His research interests are 6G networks, communications and signal processing. His h-index is over 100 and his work receives 70,000+ Google citations. He is serving as the EiC of IEEE JSAC, an Area Editor for the IEEE TWC and OJSP, an Editor for IEEE TVT, and OJ-SP, and was an Area Editor for IEEE TCOM and OJCOMS, an Editor for IEEE TCOM, TWC, COMST, WCL, CL and WCMC. He received the best paper award of IET ICWMC-2009 and IEEE WCSP-2014, the EU Marie Curie Fellowship 2012-2014, the Top IEEE TVT Editor 2017, IEEE Heinrich Hertz Award 2018, IEEE Jack Neubauer Memorial Award 2018, IEEE Best Signal Processing Letter Award 2018, Alexander von Humboldt Foundation Friedrich Wilhelm Bessel Research Award 2020, IEEE SPCC Technical Recognition Award 2021, IEEE VTS Best Magazine Paper Award 2023, and the Best Paper Award in IEEE GLOBCOM 2024. He is a Web of Science Highly Cited Researcher in two disciplines (2019-2025), and a Fellow of the IEEE.
Abstract
Abstract: Due to the explosive growth in the number of wireless devices and diverse wireless services, next-generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, flexible-antenna systems have been recognized as key enabling technologies of the sixth-generation (6G) wireless networks, as they can intelligently reconfigure users’ effective channel gains and hence significantly enhance their data transmission capabilities. However, the existing flexible-antenna systems have been developed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they are lack of the capability to reconstruct strong line-of-sight (LoS) links which are typically 100 times stronger than NLoS links. Furthermore, the existing flexible-antenna systems exhibit restricted flexibility, where adding/removing an antenna is not straightforward. This talk focuses on an innovative flexible-antenna system, termed generalised pinching-antenna systems. The principles of generalized pinching-antenna systems are described first together with specific examples of generalized pinching-antenna systems, including Docomo’s dielectric waveguide based pinching antennas,  leaky coaxial cable (LCX), etc. In addition, promising 6G related applications of generalized pinching antennas, including environment division multiple access (EDMA), integrated sensing and communication, next-generation multiple access, etc, are also illustrated. Finally, important directions for future research, such as antenna/waveguide deployment, channel estimation, etc, are highlighted.
Nanyang Technological University, Singapore & Academic Visitor, Princeton University, USA
Title: Optical wireless communication: from underwater to space connectivity
Bio:
Dr Sujan Rajbhandari (Senior Member, IEEE) is an internationally recognized expert in optical wireless communication with 15+ years of experience in academia and industry. Currently, he is a Senior Lecturer at the University of Strathclyde, UK. His expertise spans optical wireless communication, advanced signal processing, and the application of machine learning/artificial intelligence to communication systems. He has authored over 300 scholarly articles with citations of over 9900 and a notable book, “Optical Wireless Communications: System and Channel Modelling with MATLAB®”.
Abstract
As communication networks expand into non-terrestrial domains, traditional radio frequency (RF) technologies are increasingly becoming bottlenecks in meeting the growing demands for data and connectivity. As a result, future generations of wireless networks are expected to seamlessly integrate multiple bands of spectrum to address the unprecedented demand across underwater, terrestrial, and space environments.
Achieving 3D connectivity requires the convergence of conventional wireless systems with new and emerging technologies tailored to these diverse and challenging applications. Optical wireless communication (OWC) has emerged as a highly promising technology that complements existing RF systems to extend network coverage. In this talk, we will explore how recent advancements in OWC are transforming communication capabilities from underwater, terrestrial and space. The talk will also highlight the key challenges faced in different environments, present emerging solutions, and discuss future perspectives.
Title: Advances in Machine Learning for Automated Microsystems Design
Bio:
Dr. Lihong Zhang (Senior Member, IEEE) is currently a Full Professor with the Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada. He received the Ph.D. degree in electrical engineering from the Otto-von-Guericke University of Magdeburg, Magdeburg, Germany, in 2003. He was a Postdoctoral Research Associate with Concordia University, Montreal, QC, Canada; Dalhousie University, Halifax, NS, Canada; and the University of Washington, Seattle, WA, USA. His current research interests include very large-scale integration computer-aided design, mixed-signal integrated system/circuit design, microelectromechanical systems design and design automation, wireless sensor networks, microfluidics and biosensors, and microprocessor-based instrumentation for ocean and biomedical applications.
Abstract
Low-Altitude Economic Networking (LAENet) aims to support diverse flying applications below 1,000 meters by deploying various aerial vehicles for flexible and cost-effective aerial networking. However, complex decision-making, resource constraints, and environmental uncertainty pose significant challenges to the development of the LAENet. Reinforcement learning (RL) offers a potential solution in response to these challenges but has limitations in generalization, reward design, and model stability. The emergence of large language models (LLMs) offers new opportunities for RL to mitigate these limitations. We first present a tutorial about integrating LLMs into RL by using the capacities of generation, contextual understanding, and structured reasoning of LLMs. We then propose an LLM-enhanced RL framework for the LAENet in terms of serving the LLM as information processor, reward designer, decision-maker, and generator. Moreover, we conduct a case study by using LLMs to design a reward function to improve the learning performance of RL in the LAENet. Finally, we provide a conclusion and discuss future work.
