title: UAV-aided hybrid RF-optical wireless networks
Creators: name: Nafees, Muhammad
Publisher: name: The University of Edinburgh
Year: 2024
Subjects: subject: B5G networks
subject: hybrid FSO-RF networks
subject: unmanned aerial vehicles
subject: UAVs
subject: hybrid relays
subject: mmWave link
subject: weather impact

description: The conventional radio frequency (RF) spectrum is nearly saturated due to the exponential growth of devices and mobile traffic in recent years. In beyond fifth-generation (B5G) networks, free-space optical (FSO) communication is expected to play an indispensable role as it offers high achievable data rates, low cost, an outstanding security level, and ease of deployment compared to other wireless techniques. However, infrequent adverse weather conditions can incapacitate its performance. The combination of FSO and RF, termed as hybrid FSO-RF, has emerged as an effective solution for meeting the growing need for high data rates. Unmanned aerial vehicles (UAVs) are also anticipated to play an instrumental role in B5G networks due to their flexible movement and deployment. In this thesis, several UAV deployment issues concerning hybrid FSO-RF networks have been investigated to enhance network performance. Fast, low-complexity, and efficient UAV placement and resource allocation strategies are imperative for the B5G networks. This thesis presents a novel variable-height multi-UAV deployment strategy to exploit the 3D flexibility of UAVs as base stations (BSs). A multi-tier variable-height UAV-aided network deployment is proposed, and its performance is compared to the state-ofthe- art equal-height deployment. Height optimization is performed to deliver energy efficiency and throughput maximization for each cell. It is shown that the proposed method is more energyefficient in a multi-cell UAV network than the most widely used height optimization method. In wireless networks, UAVs are considered to provide quick and on-demand relaying operations. In this thesis, it is proposed that UAVs intercept an FSO link and serve as hybrid relays to increase the FSO link’s availability in adverse weather (e.g., fog). The UAVs establish an intermediate millimeter wave (mmWave) link without affecting the source or destination. The end-to-end system capacity is maximized by optimizing UAVs’ location based on the negatively correlated FSO and mmWave link lengths. The performance of the proposed model is compared to benchmark systems such as single FSO, fixed relay-assisted FSO, and fixed hybrid FSO-RF. One of the key applications of UAVs in wireless networks is to provide offloading services. This thesis investigates a UAV-aided hybrid FSO-RF backhauling system using a matching game theory (GT) and reinforcement learning (RL) framework. When the backhaul capacity of a ground base station (GBS) is limited due to weather attenuation, users are offloaded from its coverage area using a UAV. It is demonstrated that the proposed scheme and its sub-optimal version outperform benchmark schemes in terms of network throughput and availability in adverse weather. Weather conditions can also have an impact on the UAV’s trajectory in wireless networks. In this thesis, the impact of adverse weather on FSO backhaul-aware UAV trajectory is evaluated to perform network capacity enhancement. A novel expression for the average FSO backhaul capacity over the UAV trajectory is derived. The proposed system is demonstrated to optimize the UAV’s trajectory in view of the weather conditions to enhance the capacity of the network.