Title: The Art of Marriage: How to Integrate Data Communications and Radar Sensing?

Time and Date: Monday, Apr. 29, 2024, 9:00am US Eastern Time (New York Time)

Presenter: Dr. Husheng Li, Purdue University

Venue: https://zoom.us/j/9172542706 (Password: 4Zn7xZ)

Abstract: Integrated Sensing and Communications (ISAC), are expected to be a featuring technology in 6G wireless communication networks. It improves the efficiencies of spectrum and energy by sharing the same waveform for both tasks of communications and sensing. The waveform in ISAC could be traditional communication waveforms (such as OFDM) or existing radar waveforms (such as FMCW). In this talk, we will discuss waveform design dedicated for ISAC, which may go beyond existing waveform designs for individual systems. We focus on two questions, for the marriage of communications and sensing: (a) How to design a natural and enlightening framework for integrating both communications and sensing? To this end, we propose to consider ISAC as a broadcast and thus unify several major designs within the same framework. (b) How to characterize the conflict and trade-off between communications and sensing when they are integrated within the same waveform? We will examine several types of conflict, such as randomness-determinism, environmental complexity, and coding-spreading trade-offs. Finally, I will provide an overview on my future research in the area of ISAC.

Bio: Husheng Li received his BS and PhD degrees, both in electrical engineering, from Tsinghua University (1998) and Princeton University (2005), respectively. He joined Qualcomm Inc. as a senior engineer after his graduation from Princeton. In 2007, he joined the EECS department of the University of Tennessee, Knoxville, where he was promoted to associated professor and full professor in 2013 and 2019, respectively. In 2022, he joined Purdue University, affiliated in both the School of Aeronautics and Astronautics and the Elmore School of Electrical and Computer Engineering. His research interest includes wireless communications, statistical signal processing, cyber physical systems, networked control, and information theory. He has received numerous best paper awards in journals such as the EURASIP Journal on Wireless Communications and Networking (2015) and conferences such as IEEE ICC (2012) and Globecom (2017).

Title: Learning Scheduling and Optimization in Federated Edge Learning

Time and Date: Tuesday, Nov. 14, 2023, 9:00AM US Eastern Time (New York Time)

Presenter: Dr. Yu Wang, Professor in the Department of Computer and Information Sciences, Temple University, Philadelphia, Pennsylvania 19122, USA

Venue: https://zoom.us/j/9172542706 (Password: 4Zn7xZ)

Abstract: Edge computing and federated learning (FL) have gained popularity since they provide a promising edge learning framework that mitigates the limitations of long latency, high cost, and privacy concerns in cloud-based centralized learning. While most existing works on federated edge learning focus on optimizing the training of the global model in edge systems, the concurrent training of multiple FL models from different applications in a shared edge cloud can lead to edge resource competition and affect the training performance of each model. Hence, in this talk, I will present our recent works in addressing this challenge by proposing optimization algorithms to jointly select FL participants and learning rates or topologies for each model, with an aim of minimizing the total training cost. Particularly, I will first introduce a multi-stage optimization framework that allows FL models to select their participants and learning rates or learning topologies. Then, I will describe a quantum assisted algorithm to tackle the joint participant selection and learning scheduling problem using both quantum and classical computing. Finally, I will summarize the talk with discussions about future directions in federated edge learning.

Bio: Dr. Yu Wang is currently a Professor in the Department of Computer and Information Sciences at Temple University. He holds a Ph.D. from Illinois Institute of Technology, an MEng and a BEng from Tsinghua University, all in Computer Science. His research interest includes wireless networks, smart sensing, and mobile computing. He has published over 200 papers in peer reviewed journals and conferences. He has served as general chair, program chair, program committee member, etc. for many international conferences (such as IEEE MASS, IEEE IPCCC, ACM MobiHoc, IEEE INFOCOM, IEEE GLOBECOM, IEEE ICC), and has served as Editorial Board Member for several international journals (including IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Cloud Computing). He is a recipient of Ralph E. Powe Junior Faculty Enhancement Awards from Oak Ridge Associated Universities (2006), Outstanding Faculty Research Award from College of Computing and Informatics at the University of North Carolina at Charlotte (2008), Fellow of IEEE (2018), and ACM Distinguished Member (2020).

Title: Unleashing the Power of Mobile Edge-Cloud Generative AI Services and AIGC Networks

Time and Date: Monday, Aug. 21, 2023, 9:00am US Eastern Time (New York Time)

Presenter: Dr. Dusit Niyato, President’s Chair Professor in Computer Science and Engineering in the School of Computer Science and Engineering, Nanyang Technological University, Singapore

Venue: https://zoom.us/j/9172542706 (Password: 4Zn7xZ)

Abstract: Artificial Intelligence Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This talk aims to on the deployment of AIGC applications, e.g., ChatGPT, at mobile edge networks, i.e., mobile AIGC networks that provide personalized and customized AIGC services in real-time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. We highlight some future research directions and open issues for the full realization of mobile AIGC networks.

Bio:Dusit Niyato is currently a President’s Chair Professor in Computer Science and Engineering in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He received B.E. from King Mongkuk’s Institute of Technology Ladkrabang (KMITL), Thailand in 1999 and Ph.D. in Electrical and Computer Engineering from the University of Manitoba, Canada in 2008. Dusit’s research interests are in the areas of distributed collaborative machine learning, Internet of Things (IoT), edge intelligent metaverse, mobile and distributed computing, and wireless networks. Dusit won the IEEE Communications Society (ComSoc) Best Survey Paper Award, IEEE Asia-Pacific Board (APB) Outstanding Paper Award, the IEEE Computer Society Middle Career Researcher Award for Excellence in Scalable Computing in and Distinguished Technical Achievement Recognition Award of IEEE ComSoc Technical Committee on Green Communications and Computing. Dusit also won a number of best paper awards including IEEE Wireless Communications and Networking Conference (WCNC), IEEE International Conference on Communications (ICC), IEEE ComSoc Communication Systems Integration and Modelling Technical Committee and IEEE ComSoc Signal Processing and Computing for Communications Technical Committee 2021. Currently, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials, an area editor of IEEE Transactions on Vehicular Technology, editor of IEEE Transactions on Wireless Communications, associate editor of IEEE Internet of Things Journal, IEEE Transactions on Mobile Computing, IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), and ACM Computing Surveys. He was a guest editor of IEEE Journal on Selected Areas on Communications. He was a Distinguished Lecturer of the IEEE Communications Society for 2016-2017. He was named the 2017-2022 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.

Title: On Optimal Partitioning and Scheduling of DNNs in Mobile Edge/Cloud Computing

Time and Date: Friday, July 21, 2023, 9:00am US Eastern Time (New York Time)

Presenter:  Dr. Jie Wu, Director of the Center for Networked Computing and Laura H. Carnell professor, Temple University, USA

Venue: https://zoom.us/j/9172542706 (Password: 4Zn7xZ)

Abstract: As Deep Neural Networks (DNNs) have been widely used in various applications, including computer vision on image segmentation and recognition, it is important to reduce the makespan of DNN inference computation, especially when running on mobile devices. Offloading is a viable solution that offloads computation from a slow mobile device to a fast, but remote edge/cloud. As DNN computation consists of a multiple-stage processing pipeline, it is critical to decide on what stage should offloading occur to minimize the makespan. Our observations show that the local computation time on a mobile device follows a linear increasing function, while the offloading time on a mobile device is monotonic decreasing and follows a convex curve as more DNN layers are computed in the mobile device. Based on this observation, we first study the optimal partition and scheduling for one line-structure DNN. Then, we extend the result to multiple line-structure DNNs. Heuristic results for general-structure DNNs, represented by Directed Acyclic Graphs (DAGs), are also elaborated based on a path-based scheduling policy. Extensions to DNN training are also discussed.

Bio: Jie Wu is the Director of the Center for Networked Computing and Laura H. Carnell professor at Temple University. He served as Chair of Department of Computer and Information Sciences from the summer of 2009 to the summer of 2016 and Associate Vice Provost for International Affairs from the fall of 2015 to the summer of 2017. Prior to joining Temple University, he was a program director at the National Science Foundation and was a distinguished professor at Florida Atlantic University where he received his PhD in 1989. His current research interests include mobile computing and wireless networks, routing protocols, network trust and security, distributed algorithms, applied machine learning, and cloud computing. Dr. Wu regularly publishes in scholarly journals, conference proceedings, and books. He serves on several editorial boards, including IEEE Transactions on Service Computing and Journal of Computer Science and Technology. Dr. Wu is/was general chair/co-chair for IEEE DCOSS’09, IEEE ICDCS’13, ICPP’16, IEEE CNS’16, WiOpt’21, ICDCN’22, IEEE IPDPS’23, and ACM MobiHoc’23 as well as program chair/cochair for IEEE MASS’04, IEEE INFOCOM’11, CCF CNCC’13, and ICCCN’20. He was an IEEE Computer Society Distinguished Visitor, ACM Distinguished Speaker, and Chair for the IEEE Technical Committee on Distributed Processing (TCDP). Dr. Wu is a Fellow of the AAAS and a Fellow of the IEEE. He is a Member of Academia Europaea (MAE).

Title: On Communication and Sensing Measurements and Modeling for Next “G”

Time and Date: Thursday, June 15, 2023, 9:00am US Eastern Time (New York Time)

Presenter:  Dr. Nada T. Golmie, National Institute of Standards and Technology, USA

Venue: https://zoom.us/j/9172542706 (Password: 4Zn7xZ)

Abstract: As the needs for sensing the physical world to support detection, tracking, AR/VR and slew of other applications increase, the use of communications waveforms for sensing is attracting a lot of attention and is likely to emerge as one of the main features in NextG development.
In this talk we discuss key challenges in measurement science and modeling approaches for advanced communications and sensing going forward. We will review the main building blocks for joint communications and sensing in terms of efficient spectrum use, higher frequency bands and the use machine learning. The millimeter-wave and terahertz bands hold the promise of significant bandwidth and speed due to large swaths of untapped spectrum. In addition, as massive data volumes are being collected, analyzed, and delivered, communications systems have become too complex to develop, manage, and operate. The insights that are “mined” from the data using Machine Learning (ML) techniques have become standard practice. In this talk, we discuss state -of -the art and key challenges in measurement and modeling techniques to expedite the development and pave the way for the next “G”.

Bio: Nada Golmie (nada@nist.gov) received her Ph.D. in computer science from the University of Maryland at College Park. Since 1993, she has been a research engineer at the National Institute of Standards and Technology. From 2014 to 2022, she served as the chief for the Wireless Networks Division in the Communications Technology Laboratory. She is an IEEE and a NIST Fellow. Her research in media access control and protocols for wireless networks led to over 200 technical papers presented at professional conferences, journals, and contributed to international standard organizations and industry led consortia. She is the author of “Coexistence in Wireless Networks: Challenges and System-level Solutions in the Unlicensed Bands,” published by Cambridge University Press (2006). She leads several projects related to the modeling and evaluation of future generation wireless systems and protocols and serves as the NextG Channel Model Alliance chair.