CNS Core: Small: Collaborative: Towards Surge-Resilient Hybrid RF/VLC Networks
Project runs from 11/01/2019 to 10/31/2022
The proposed research will study a hybrid RF/VLC network where the number of IoT users that require wireless communications is significantly
larger than the number of RF base stations (BSs). Our research is organized into three synergistic research thrusts. First, in Thrust-1, a novel, hybrid NOMA VLC/RF wireless access will be introduced, for which surge-resilient RAT/LED assignment and NOMA transmission techniques will be developed. The proposed approach can schedule UEs/MTDs based on their QoS needs under traffic surges and/or network failures. Subsequently, Thrust-2 will introduce novel, resilient cross-system learning for self-organizing resource management, and explore the network resilience against censored information, i.e., information that is not available to the network due to failures or environmental changes. The proposed cross-system learning framework allows for feedback between multiple learning algorithms operating across RATs to devise optimal resource allocation strategies that guarantee the required QoS levels across RATs, even when information is censored. Finally, Thrust-3 will introduce testbeds and SDR experimental platforms to evaluate the findings from the first two research thrusts on multihop RF/VLC communications and LED selection with NOMA.