# Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond
## 2 Related Works  
In recent years, researchers have been actively investigating the use of UAVs for providing communication services in a variety of scenarios, such as disaster-hit scenarios [4; 5], extending coverage of fixed infrastructure [6], dealing with network overload conditions [7], etc. Moreover, the use of UAVs for enhancing the capabilities of the 5G network has also been investigated [8]. This work considers a UAV-based small cell network and investigates how ABSs can be optimally placed based on user density for improving received power and SIR.  
The placement of ABSs affects various network performance parameters, such as coverage, throughput, connectivity, and revenue. The issue of mobile base station placement to maximize coverage is also studied in Wireless Sensor Networks (WSNs), where the goal is to place the base stations in a way that maximizes the number sensor nodes being covered, which resembles the problem of coverage maximization in mobile networks. There are works that attempt to maximize throughput or spectrum efficiency. In [9], the authors proposed an algorithm for optimizing the position of a single UAV for throughput maximization of users based on the user data rate requirements and their positions. In [4] the authors proposed an algorithm that determines optimal positions of multiple ABSs to maximize throughput considering a software-defined wireless network deployed in a disaster-hit area. In [10], the authors used deep learning to determine optimal UAV positions to maximize throughput. The authors of [11] studied maximizing the sum rate via power control and UAV positioning. [PERSON] et al. [12] studied the problem of UAV-based relays' placement to support high-quality live video transmission. In [13], the authors investigated the optimal altitude of low-altitude aerial platforms for coverage maximization.  
Other works exploit the dynamic nature of UAV positioning to enhance network capacity. For example, considering a multi-tier drone-cell network to complement a terrestrial heterogeneous network, [PERSON] et al. [14] studied the positioning of drone base stations (BSs) to maximize coverage and revenue. [PERSON] et al. [15] conducted a theoretical analysis of how interference affects capacity and worked out a closed-form expression determining optimal ABS locations. [PERSON] et al. [16] investigated the three-dimensional (3D) placement of ABSs to maximize the number of served users and their total rate. A mobility control algorithm has been proposed by [PERSON] et al. [17] for the optimal placement of a chain of UAV-based communication relays for enabling end-to-end communication. In [18], the authors investigated the optimum altitude for both static and mobile UAVs and its effects on the total power loss and bit error rates, etc. The authors of [19] propose an algorithm for the 3D placement of ABSs to maximize the coverage of users with different quality of service (QoS) requirements.  
Since fixed-winged UAVs cannot hover at a particular location, there are works that attempt to enhance various network parameters through trajectory control of the UAVs. For example, [PERSON] et al. [20] proposed an iterative algorithm to determine optimal UAV trajectory for offloading traffic at the edge regions of three adjacent base stations. [PERSON] et al. [21] proposed an algorithm aimed at optimizing the performance of a ground-to-relay link by controlling the heading angle of the UAV. None of the solutions, and, to the best of our knowledge, any other related works, are suitable to efficiently solve the problem of receiving power and SIR maximization in ABS-based UDNs. Hence, this work studies this problem and designs a solution for it.