Combating Spectrum Crunch with Efficient Machine-Learning Based Spectrum Access and Harnessing High-frequency Bands for Next-G Wireless Networks

There is an increasing trend in the number of wireless devices that is now already over 14 billion and is expected to grow to 40 billion devices by 2030. In addition, we are witnessing an unprecedented proliferation of applications and technologies with wireless connectivity requirements such as unmanned aerial vehicles, connected health, and radars for autonomous vehicles. The advent of new wireless technologies and devices will only worsen the current spectrum crunch that service providers and wireless operators are already experiencing. In this PhD study, we address these challenges through the following research thrusts, in which we consider two emerging applications aimed at advancing spectrum efficiency and high-frequency connectivity solutions.

First, we focus on effectively utilizing the existing spectrum resources for emerging applications such as networked UAVs operating within the Unmanned Traffic Management (UTM) system. In this thrust, we develop a coexistence framework for UAVs to share spectrum with traditional cellular networks by using machine learning (ML) techniques so that networked UAVs act as secondary users without interfering with primary users. We propose federated learning (FL) and reinforcement learning (RL) solutions to establish a collaborative spectrum sensing and dynamic spectrum allocation framework for networked UAVs. In the second part, we explore the potential of millimeter-wave (mmWave) and terahertz (THz) frequency bands for high-speed data transmission in urban settings. Specifically, we investigate THz-based midhaul links for 5G networks, where a network's central units (CUs) connect to distributed units (DUs). Through numerical analysis, we assess the feasibility of using 140 GHz links and demonstrate the merits of high-frequency bands to support high data rates in midhaul networks for future urban communications infrastructure. Overall, this research is aimed at establishing frameworks and methodologies that contribute toward the sustainable growth and evolution of wireless connectivity.