## 2 Related Work  
The increasing demand for reliable communication in emergency response services calls for enhanced coordination, which can be achieved through advanced technologies like 5G. This research aims to assess the potential of 5G networks in improving communication efficiency by employing traffic regression methods. Additionally, it explores the performance of bandwidth allocation using machine learning models for different UAV movement patterns. The study also focuses on establishing key QoS parameters for UAV communication across 5G networks, with an emphasis on bandwidth and different data categories. Previous studies have proposed several conventional bandwidth allocation approaches for 5G communication networks. UAV-GSs are anticipated to play a critical role in 5G and beyond networks, serving as emergency backup networks for disrupted communication infrastructure during natural disasters or sudden network failures, enabling data collection from IoT devices and facilitating content caching for vehicular communication systems [14]. [PERSON] et al. [12] investigated resource management and bandwidth allocation for 5G and beyond radio access networks. They proposed two admission control policies to select admissible users from the perspective of optimizing the QoS. [PERSON] et al. [15] used the proportional fairness algorithm technique for dynamic bandwidth allocation in 5G point-to-point networks. They show that this method can give significant performance gains in comparison to current methods using the max-min fairness algorithm and weighted fair queuing algorithm under different conditions. Their proposed method effectively allocates bandwidth to maximize data transmission, achieving a higher throughput rate. The researchers in [16] use the examined zero-forcing model (ZF) to find the maximum throughput to enable two interfering links to transmit simultaneously in specific scenarios. The focus is on identifying the suitable scheduling schemes that effectively leverage ZF precoding opportunities to enhance the network throughput. To address this, they introduced an innovative link model and obtained multiple performances of throughput in different areas. To study the coverage radius, the three-dimensional (3D) deployment of the base station is challenging, as its placement directly influences its coverage radius. This is particularly critical because, in cellular networks, each user's equipment has unique QoS requirements, and the base station has limited capacity. Consequently, researchers have thoroughly investigated the factors affecting the base station coverage radius in their proposed study [17]. In [18], researchers introduce a novel multi-UAV deployment strategy with variable heights to leverage 3D positioning flexibility. UAVs are increasingly viewed as potential base stations for future wireless networks, where key design challenges include network coverage, throughput, and energy efficiency. To address these, fast and efficient UAV placement and resource allocation strategies are essential. In their study, the multi-tier and variable-height approach is compared against standard equal-height deployments, with height optimization aimed at enhancing energy efficiency and throughput. Their results indicate that the method significantly improves energy efficiency in multi-cell UAV networks compared to existing height optimization techniques. In [PERSON] et al. 's [19] research, they discuss modern estimation approaches that involve actively introducing structured simulated communication into the network to measure available end-to-end bandwidth for an application. By applying various machine learning models to evaluate structural changes along the network path, they can estimate the available bandwidth at the receiving end. Prediction tasks are characterized by supervised learning, as the objective is to predict or categorize a certain result of interest. This learning paradigm encompasses two primary categories: classification and regression.