Home

CNS Core: Small: Collaborative: Towards Surge-Resilient Hybrid RF/VLC Networks

Award Numbers: CNS-1910153, CNS-1909372

Project Summary:

The successful deployment of smart city-scale wireless services, such as the Internet of Things (IoT), hinges on the presence of a reliable communication infrastructure that can pervasively connect both human and machine-type devices. In order to support the traffic anticipated from an IoT ecosystem, the wireless infrastructure must be resilient to the surge of traffic that can result from a plethora of events that range from hot-spot events and random failures to natural disasters and targeted attacks. The goal of this research is to develop a novel wireless network architecture that leverages both radio frequency (RF) and visible light communication (VLC) to provide resilient and pervasive connectivity. The developed architecture and associated frameworks will bring together novel, cross-disciplinary ideas from communication theory, learning, and game theory to explore the fundamental theoretical and experimental challenges of a hybrid and resilient RF/VLC network. The research is coupled with a comprehensive educational plan that includes new course material, tutorials, and large-scale involvement of graduate and undergraduate students in research related to resilient communications and networking.

This research will introduce a novel framework for designing, analyzing, and optimizing hybrid RF/VLC communications, in the presence of failures, hotspots, and natural disasters. The key technical contributions will include: 1) Novel scheduling algorithms uniquely tailored towards RF/VLC systems that can maximize the number of users with available connectivity, in the presence of traffic surges; 2) Effective transmission mechanisms based on non-orthogonal multiple access that can enhance the spectral efficiency of flash crowds; 3) Resilient self-organizing algorithms, based on game theory and reinforcement learning, that exploit feedback across radio access technologies to enable intelligent and resilient resource management in the presence of incomplete information; and 4) Practical RF/VLC experiments that will be used to validate the proposed research and bridge the gap between theory and practice.