Modelling and analysis of UAV-Assisted cellular networks

Abstract

Emerging as a transformative solution in next-generation wireless networks, unmanned-aerial vehicles (UAVs) provide unprecedented flexibility, rapid deployment, and enhanced connectivity. Their integration into conventional cellular networks presents numerous opportunities, such as dynamic coverage expansion, disaster relief and emergency response, and military and surveillance applications. However, it also brings challenges, including energy constraints, fronthaul and backhaul limitations, and mobility and handover management. This thesis explores three critical aspects of UAV-enabled networks: spectrum management in integrated access and backhaul (IAB) networks, mobility management for seamless handovers, and joint UAV activation control and power optimization in UAV enabled cell-free massive multiple input multiple output (mMIMO) networks under fronthaul capacity limitations. In the first part, we investigate spectrum management in UAV-enabled IAB networks, where the UAVs act as access points and relay data to the core network. We address the challenge of optimally allocating limited spectrum resources between access and backhaul links to maximize network efficiency. Our analysis includes disaster recovery scenarios, where optimal UAV positioning and resource partitioning are derived to sustain user connectivity and maximize throughput. Additionally, in urban environments, we introduce cache-enabled UAVs that reduce reliance on backhaul links, improving content delivery performance. Key metrics such as signal to interference noise ratio (SINR) coverage probability and successful content delivery probability are evaluated. The second focus is on mobility management in UAV-enabled networks with mobile users. Frequent handovers (HOs) due to user mobility present significant challenges to network performance. To address this, we propose a caching-based handover management scheme that reduces handover occurrences by utilizing device caching, thereby enhancing quality of service (QoS). Using spatio-temporal analysis, we assess the scheme’s effectiveness in minimizing handover frequency and ensuring seamless connectivity. Additionally, we examine network reliability by analyzing the conditional success probability (CSP) experienced by users in the presence of blockages. Furthermore, we derive the meta distribution (MD) of SINR and mean local delay (MLD), offering deeper insights into network reliability. The third aspect focuses on the joint optimization of UAV activation and power consumption in UAV-based cell-free mMIMO networks. These networks promise uniform service quality over large areas but are constrained by the limited capacity of wireless fronthaul links connecting UAVs to the central processing unit (CPU). We incorporate functional split options, specifically Options 8 and 7.2, to balance fronthaul capacity, computational complexity, and latency. By formulating and solving a joint placement and power optimization problem, we ensure efficient resource utilization while maintaining fair SINR coverage across users. This thesis provides a structured framework for integrating UAVs into cellular networks, addressing key challenges in spectrum management, mobility, and resource optimization, paving the way for more reliable and efficient UAV-based wireless communication systems.