# Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
## 1 Introduction  
With the rapid development of maritime activities such as marine tourism, oceanic mineral exploration, and maritime rescue, reliable and high-speed maritime wireless communication is receiving more and more attention [1]. In recent years, the use of intelligent applications has been rapidly popularized with the evolution of Internet of Things (IoT) [2, 3, 4]. The emergence of these latency-sensitive and compute-intensive applications, as well as the growth of maritime activities, has greatly improved the demand for maritime wireless communication. However, the satellite communication systems and high frequency/very high frequency (HF/VHF) communications used in traditional maritime communication networks have certain limitations [5, 6]. Although satellite communications have a wide coverage area, they also have disadvantages such as high cost and large transmission delay. HF/VHF communication systems have lower costs, but they are not suitable for transmitting large amounts of data services due to insufficient channel bandwidth. To tackle the above issues, mobile edge computing (MEC) can be an effective solution to provide high-quality computing services for compute-intensive maritime terminals. MEC places computing resources at the edge of the network, which can effectively reduce latency and significantly improve quality of service [7, 8, 9, 10]. Many research works on MEC technology have received increasing attention. In [11], the task delay among users was minimized to optimize the tasks division ratios and offloading the transmit power. The authors in [12] proposed a MEC task offloading problem in ultra-dense networks, aiming to minimize latency while saving battery life of user devices. The offloading decision, user transmit power, and computation resource allocation were jointly optimized in [13] to minimize the weighted sum of energy consumption. In [14], the authors minimizedthe overall cost of energy, computation, and delay for all users by jointly optimizing the offloading decisions of all users' tasks as well as the allocation of computation and communication resources. The authors in [15] minimized the sum of weighted task latency for all users by optimizing offloading decision, transmission power, and resource allocation. However, the above works in the field of MEC networks ignore a fact that the deployment of traditional MEC servers is fixed. In addition, due to the limited coverage of the ground MEC server, it is difficult for ground MEC to meet the computing requirement when the user devices are mobile. In view of the above deficiencies, unmanned aerial vehicles (UAVs) can be regarded as a prospective solution. As a highly flexible aircraft, UAVs have been widely used in maritime wireless communication due to their advantages of high maneuverability, high scalability, and convenient deployment [16; 17; 18]. Hence, UAV has been proposed as an aerial computing platform to provide computing services for user devices, which is called UAV-assisted MEC. The use of UAV-assisted MEC can yield significant advantages over traditional ground MEC in terms of coverage, operational cost, and flexibility. Furthermore, line-of-sight (LoS) links from UAV to ground mobile users can also mitigate the impact of obstacles and shadows [19; 20]. As a result, the combination of UAV and MEC can effectively improve the performance of maritime communication networks. For UAV-assisted MEC system, the authors in [21] formulated a joint optimization problem to maximize the sum of the maximum delay among all users at each time slot. In [22], the authors minimized the weighted-sum energy consumption of UAV and users by jointly optimizing the UAV trajectory and computation resource allocation. In [23], the authors jointly optimized the time slot size, users scheduling, computation resource allocation, and UAVs' trajectories to investigate a minimum time of task completion in a UAV-aided MEC system.