Abstract: Despite the revolution in communication technologies that was witnessed in the past decade, there is still a significant portion of the Earth’s population who falls out of today’s wireless broadband coverage. While people who live in under-developed, disaster-affected, or rural areas remain in “wireless darkness”, communities in megacities are also often experiencing below-par wireless connectivity due to the increasing demand for wireless services. To provide high-speed, reliable wireless connectivity to those on the less-served side of the digital divide, as well as to crowded urban environments, airborne and satellite-based communication platforms can be deployed as promising solution to boost the capacity and coverage of existing wireless cellular networks (e.g., 5G and beyond). In this talk, we address some of the fundamental challenges that face the realization of integrated space-air-ground communication systems by developing new frameworks that weave together new concepts from communications, machine learning, and game theory. First, we focus on the problem of wireless-aware control and navigation for drones that are used as access points to provide connectivity to remote areas. In particular, we introduce a novel multi-agent, reinforcement learning solution with meta learning capabilities that can be used to control the navigation of these aerial access points, allowing them to provide effective, on-demand coverage to distributed, dynamic, and unpredictable ground user requests. We show that, using value decomposition techniques and a meta training mechanism, the proposed low training overhead control framework is guaranteed to converge to a local optimal coverage for the users, under wireless dynamics. Second, we investigate the problem of distributed wireless resource management in large-scale, integrated space-air-ground communication systems. In particular, we develop a novel exchange market-based framework that allows the integrated system to efficiently exploit its spectral resources while optimizing its communication performance in terms of data rates. We then show that the optimal, equilibrium allocation of resources can be found by using a lightweight, distributed solution that facilitates cooperative spectrum sharing in the integrated system and yields a faster convergence compared to a baseline sub-gradient algorithm. We conclude the talk with an overview on future, exciting research directions.
Speaker Bio: Ye Hu (S’17) received her PhD. in the Bradley department of Electrical and Computer Engineering at Virginia Tech, Virginia, USA, in 2021, and was also a postdoctoral research scientist at the Electrical Engineering Department at Columbia University, New York, USA. Her research interests include machine learning, game theory, cybersecurity, blockchain, unmanned aerial vehicles, cube satellite, and wireless communication. She is also the recipient of the best paper award at IEEE GLOBECOM 2020 for her work on meta-learning for drone-based communications.