# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
###### Abstract  
The limited coverage of terrestrial base stations and the limited transmission distance and onboard resources of satellite communications make it difficult to ensure the quality of communication services for marine users by relying only on satellites and terrestrial base stations. In contrast, UAVs, as flexible mobile communication nodes, have the capacity for dynamic deployment and real-time adjustment. They can effectively make up for the communication blind spots of traditional satellites and ground base stations in the marine environment, especially in the vast and unpredictable marine environment. Considering the mobility of maritime users, one can effectively reduce the communication delay and optimize the deployment scheme of UAVs by predicting their sailing trajectories in advance, thus enhancing the communication service quality. Therefore, this paper proposes a communication coverage model based on mobile user route prediction and a UAV dynamic deployment algorithm (RUDD). It aims to optimize the coverage efficiency of the maritime communication network, minimize the communication delay, and effectively reduce the energy consumption of UAVs. In this algorithm, the RUDD algorithm employs a modified Long Short-Term Memory (LSTM) network to predict the maritime user's trajectory, utilizing its strengths in processing time-series data to provide accurate predictions. The prediction results are then used to guide the Proximal Policy Optimization (PPO) algorithm for the dynamic deployment of UAVs. The PPO algorithm can optimize the deployment strategy in dynamic environments, improve communication coverage, and reduce energy consumption. Simulation results show that the proposed algorithm can complement the existing satellite and terrestrial networks well in terms of coverage, with a communication coverage rate of more than 95%, which significantly improves the communication quality of marine users in areas far from land and beyond the reach of traditional networks, and enhances network reliability and user experience. dynamic deployment of UAVs; communication coverage; maritime communication; route prediction 2024875939  
stations can provide high-speed networks for some sea areas, their coverage is extremely limited. In particular in open sea areas and areas far from land, the coastline of marine areas is long and variable, and terrestrial base stations cannot provide continuous coverage. Meanwhile, for high-speed moving targets such as ships and offshore platforms, the fixed deployment of base stations cannot meet their real-time changing communication needs [6]. In this case, relying only on terrestrial networks as well as satellite networks to provide maritime services has certain limitations in terms of coverage, communication capacity, and computational resources, and thus it is difficult to meet large-scale, highly reliable, and low-latency maritime communication needs. In this context, UAV-assisted communication has gradually become an effective program. With the characteristics of flexibility and high mobility, UAVs can dynamically adjust their flight altitude and position according to real-time demand in the marine environment, thereby covering the blind areas that it is difficult to cover with traditional communication networks. Compared with satellites and terrestrial base stations, drones can be rapidly deployed to specific areas to provide high-quality localized communication coverage, especially in hot spots where communication needs are more concentrated, such as sea areas far from land and near offshore platforms [7; 8]. UAVs are also able to adjust flight routes and altitudes according to real-time communication needs, reducing signal attenuation and ensuring communication quality. In addition, UAVs can increase the line-of-sight (LOS) transmission range of the airborne base station so that it can communicate more effectively with ships, buoys, and other equipment on the sea surface, further improving the communication quality and network reliability [9; 10].