# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 2 Related Work  
Near-Earth orbit (LEO) satellites play an important role in maritime communications, and many references in the literature have conducted specific studies on the satellite-wide-area coverage problem. For example, ref. [13] analyzes the average backhaul capacity of terrestrial satellite terminals using stochastic geometry and queuing theory and proposes a multi-layer LEO satellite constellation deployment scheme that takes satellite mobility into account and supports seamless global coverage. To address the saturation problem in available LEO space and meet the ultra-low latency requirements of future 6G, ref. [14] adopts a decomposition-aggregation approach combined with an elite strategy genetic algorithm to minimize satellite size and maximize average coverage, ensuring high robustness and reliability. To address issues such as the limitations of a single satellite deployment model, insufficient channel model accuracy, and limited communication coverage, ref. [15] proposes a multi-layer satellite deployment strategy. By deploying satellites on multi-layer concentric spheres, this strategy significantly enhances both the model's pervasiveness and the coverage capability of the satellite communication system. Meanwhile, the Shadow Rice Fading (SR) model is introduced to characterize the channel between satellites and terrestrial gateway stations, which improves the accuracy of the channel model. Although some progress has been made with existing satellite-deployment strategies, these methods usually do not take into account the deployment of drones to supplement coverage. When satellites are deployed on a large scale to extend coverage, it is not only costly but also results in higher losses during signal transmission and lower transmission rates, making it difficult to meet the demand for low latency and high reliability in maritime communications. The mobility and flexibility of UAVs enable them to provide essential communication services to users in the edge areas of the cell as well as to users with higher requirements for communication quality. Several research papers have focused on applications and studies that rely on UAVs alone for coverage. For example, ref. [16] investigates the problem of 3D path planning for UAVs with cellular network connectivity and proposes a multi-step dueling DDQN-based algorithm for coverage maximization. Ref. [17] mainly explores the system performance of UAV-assisted networks in urban environments, focusing on the radiation gain of directional antennas. By deriving the network coverage probability based on stochastic geometric theory, the signal transmission efficiency and coverage range were significantly improved. Ref. [18] proposed a distributed algorithm based on the virtual Coulomb force and Voronoi diagram with two mobility schemes and redundant UAV dormancy strategy to minimize the number of UAVs, improve communication coverage, and save energy. Ref. [19] investigates the coverage utility and energy multi-objective optimization problem in multi-UAV communication scenarios and proposes an improved multi-objective Gray Wolf optimization algorithm. The algorithm optimizes the number, position, and speed of UAVs to maximize the coverage utility and minimize energy consumption through role determination and hybrid solution initialization strategies. Ref. [20] investigates the multi-UAV base station deployment problem considering constraints such as movement speed, energy consumption, and communication coverage radius and proposes an algorithm called Dense Multi-Intelligent Body Reinforcement Learning, which aims to maximize the communication coverage of the vehicle-mounted network. For the problem of UAV swarms performing full coverage of an area, ref. [21] proposes a path planning algorithm realized by information exchange between UAV swarms and adopts the parallel line full coverage path to provide an effective solution for UAV swarms' full coverage path planning in a simple area.