# Deep Learning-Based Optimization for Maritime Relay Networks
## 1 Introduction  
The last two decades have witnessed great progress in terrestrial wireless communications to support various high-rate low-latency applications. However, the development of maritime communications is still far behind. In recent years, providing broadband communications for marine users has attracted attention due to rapidly increasing oceanic activities, including oil exploitation, sea farming, scientific expedition, tourism, etc. Currently, obtaining access to onshore base stations (BSs) and terrestrial cellular networks is the most appropriate method to provide high-rate and low-cost network service. Hence, techniques to extend the coverage of onshore BSs have been investigated, among which relaying is a mature and effective one. Based on this technology, researchers have continuously explored new applications to enhance long-distance communication capabilities. For example, a multi-hop solution utilizing balloons to achieve broadband communication in remote maritime areas was proposed in [1], providing a novel perspective on addressing maritime communication challenges. Additionally, the work in [2] explored the design of relay networks for multi-domain maritime operations (land, sea, air and space), highlighting the critical need for connectivity and efficient network performance across these diverse environments. Moreover, the flexibility and mobility ofunmanned aerial vehicles (UAVs) have garnered significant attention as a means to improve the coverage of maritime communication networks [3]. To address the challenges posed by the lack of stable connections between maritime communication nodes, researchers have proposed integrating UAVs into the topology of sixth-generation maritime communication networks [4]. In scenarios where direct connections to terrestrial base stations are limited, UAV swarms can significantly improve wireless communication performance. However, due to the mobility of UAVs, the state of communication links can easily be affected during operations. To address this issue, [PERSON]. [5] proposed a channel relay selection method based on UAV position prediction to improve the stability of communication links. Additionally, Ref. [6] introduced a Q-learning-based relay selection scheme using the signal-to-noise ratio (SNR) to identify the optimal relay for multi-hop transmissions. Ref. [7] proposed an optimal relay selection scheme based on deep neural networks (DNNs), where the DNN is used to predict the outage probability and perform optimal relay selection, thereby improving network performance. Ref. [8] introduced the optimization of relay selection to improve the performance of Device-to-Device (D2D) communication systems in complex wireless propagation environments. Broadband reliable wireless communications depend on accurate and instantaneous knowledge of channel information. However, in highly dynamic oceanic environments, channel information changes with hydrological and meteorological parameters, including wind, wave, moisture and temperature, to name a few. The fluctuations of these parameters present significant challenges for estimating channel information in maritime communications, which makes link analysis a challenging task. Ref. [9] proposed a novel opportunistic routing scheme aimed at improving the efficiency of nautical wireless ad hoc networks, particularly in situations where nodes frequently switch between network types. The scheme enhances packet delivery performance by optimizing relay node selection and utilizing prediction mechanisms. [PERSON]. [10] made an initial attempt to leverage the advantages of machine learning to solve the relay selection problem in two-hop networks. In [11], a solution based on Q-learning was proposed to optimize relay selection in scenarios where channel state information (CSI) is unavailable.