# Deep Learning-Based Optimization for Maritime Relay Networks
## 1 Introduction
Furthermore, in reference [12], the relay selection problem was formulated as a multi-class classification problem, and a decision tree-based approach was used to address the challenges of estimating and collecting accurate eavesdropper CSI. Additionally, [PERSON]. [13] proposed a deep learning-based channel estimation algorithm that models the rapid variations in communication channels as two-dimensional images, using known pilot positions to predict unknown channel responses. Deep learning can extract implicit features from large amounts of history data and therefore is regarded as a promising solution for connectivity analysis without full channel information. Maritime communications are significantly influenced by unique environmental factors, such as seawater evaporation and tidal movements, which render terrestrial communication channel models inadequate for oceanic applications. Current maritime channel models often rely on well-established terrestrial frameworks but fail to capture the complex and dynamic propagation characteristics of marine environments [14]. This highlights the critical need for wireless channel models specifically tailored to maritime conditions. For instance, [PERSON]. [15] analyzed wireless channel models in oceanic environments and identified key challenges, including highly dynamic network topologies and sparse user distributions, which lead to communication instability and high operational costs [16]. In addition, traditional modeling methods demonstrate clear limitations in rapidly changing oceanic conditions. Their lack of real-time adaptability and comprehensiveness prevents them from effectively characterizing the dynamic behavior of wireless channels in maritime environments. In recent years, deep learning has shown significant potential in various fields. For example, Ref. [17] attempted to combine machine learning techniques with wireless communications. A relay selection scheme in millimeter-wave D2D communications within fifth-generation cellular networks is presented in [18]. The authors in [19] introduced a deep learning-assisted cooperative framework called Predictive Relay Selection (PRS), which enhances the quality of CSI by accurately predicting fading channels. Building upon this progress, the researchers in [20] explored and attempted to apply machine learning in maritime communications. In [21], the authors proposed a deep learning-based approach that analyzes channel and navigation information to predict link availability, providing a foundation for intelligent maritime communication networks. However, existing strategies often rely on accurate CSI. Measuring and integrating such critical data pose extreme challenges in the dynamic and unpredictable oceanic environment. To overcome these challenges, this paper proposes a deep learning-based optimization strategy for maritime relay networks that eliminates the need for real-time CSI estimation. By enabling multi-hop transmission and prioritizing link connectivity, the proposed approach ensures efficient data transmission and enhances communication reliability. Moreover, link connectivity serves as a critical metric, offering intelligent node selection to optimize overall network performance in maritime scenarios. In this paper, a channel modeling method is proposed to approximate the oceanic environment, and a deep learning-based scheme is developed to analyze relay networks. The main contributions of this work are as follows:  
To capture the highly dynamic characteristics of wireless environments over the ocean, a channel modeling method is established, allowing channel parameters to vary randomly. A deep learning-based optimization scheme is proposed, transforming the relay selection problem into a spatiotemporal classification and prediction task, effectively addressing the challenge of obtaining accurate channel state information (CSI) in dynamic oceanic conditions.