Keynotes

Biography

Nick Mastronarde is an Associate Professor in the Department of Electrical Engineering at the University at Buffalo. He received his Ph.D. degree in Electrical Engineering at the University of California, Los Angeles (UCLA) in 2011 and his B.S. and M.S. degrees in Electrical Engineering from the University of California, Davis in 2005 (Highest Honors, Department Citation) and 2006, respectively. He has been the recipient of several awards and honors including a first year department fellowship through the Electrical Engineering department at UCLA, the Dissertation Year Fellowship through the Graduate Division at UCLA, the Dimitris N. Chorafas Foundation Award for 2011, the 2020 SEAS Senior Teacher of the Year Award, and UB’s Teaching Innovation Award 2022. He has spent four summers (2013, 2015, 2016, 2018) as a faculty fellow at the US Air Force Research Laboratory (AFRL) Information Directorate in Rome, NY.
Prof. Mastronarde’s research interests are in the areas of resource allocation and scheduling in wireless networks and systems, UAV networks, 5G and beyond networks, and reinforcement learning for wireless communications and networking.

Talk

Title: Simulating swarms of small unmanned aircraft systems with the UB-ANC Emulator

Abstract:

Miniaturization of hardware, sensing, and battery technologies have enabled practical design of low-cost small unmanned aircraft systems (sUAS) for civilian and military applications. In parallel, many such applications have been envisioned that bring together multiple networked sUAS to execute complex missions. However, designing, implementing, and testing these missions on actual hardware poses numerous inter-disciplinary challenges spanning communications, networking, planning, and multi-agent control, as well as regulatory challenges. To mitigate these, we have developed an open software/hardware platform called the University at Buffalo’s Airborne Networking and Communications (UB-ANC) Emulator. The UB-ANC Emulator not only provides a platform to study problems at the intersection of the aforementioned disciplines, but it also facilitates rapid transition from theory to simulation to deployment on actual sUAS.
In this talk, we motivate the need for the UB-ANC Emulator, describe its software architecture, and demonstrate its utility through several illustrative examples. To accurately reflect the performance of a swarm where communication links are subject to interference and packet losses, and protocols at all layers affect network throughput, latency, and reliability, we have connected UB-ANC to different network simulators including ns-3, EMANE, and a custom-built software-in-the-loop channel emulator in GNU Radio.

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Biography

Dr. Zhangyu Guan is an Assistant Professor with the Department of Electrical Engineering at University at Buffalo. He received his PhD in Communication and Information Systems from Shandong University in China in 2010. He worked at University at Buffalo as a Postdoctoral Research Associate from 2012 to 2015. After that, he worked as an Associate Research Scientist with the Department of Electrical and Computer Engineering at Northeastern University in Boston, MA, from 2015 to 2018. Currently Dr. Guan directs the Wireless Intelligent Networking and Security (WINGS) Lab at University at Buffalo, focusing on research and technology transfer in zero-touch theories and algorithms, new spectrum technologies, wireless network security, testbed design for future networks, and printable electronic circuits.

Talk

Title: Towards Zero-Touch Automated UAV-Enabled NextG Networks Through Digital Twin-Assisted Domain Adaptation

Abstract: In existing wireless networks, the control programs have been designed manually and for certain predefined scenarios. This process is complicated and error-prone, and the resulting control programs are not resilient to disruptive changes. Data-driven control based on Artificial Intelligence and Machine Learning (AI/ML) has been envisioned as a key technique to automate the modeling, optimization and control of complex wireless systems. However, existing AI/ML techniques rely on sufficient well-labeled data and may suffer from slow convergence and poor generalizability. In this talk, focusing on digital twin-assisted wireless unmanned aerial vehicle (UAV) systems, I will discuss the emerging techniques that can enable fast-converging data-driven control of wireless systems with enhanced generalization capability to new environments. These include SLAM-based sensing and network softwarization for digital twin construction, robust reinforcement learning and system identification for domain adaptation, and testing facility sharing and federation. The corresponding research opportunities are also discussed.