## 1 Introduction
### State-of-the-Art Review Articles and Their Limitations  
Table 1 presents a summary of existing reviews on ML and DL techniques in UWA communication. As shown in the table, previous reviews explore the potential of ML and DL approaches to enhance system robustness, optimize communication protocols, and improve signal processing in complex underwater environments. While these contributions are valuable, they exhibit notable research gaps. First, they lack a holistic evaluation of ML and DL techniques for channel estimation, modulation recognition, and adaptive modulation. Second, there is no detailed comparative analysis of ML and DL algorithms, including their underlying system characteristics and performance metrics. Finally, the broader impact of ML and DL advancements on the efficiency, scalability, and reliability of UWA communication systems remains underexplored. This systematic literature review provides the first integrated synthesis of ML and DL techniques across three key physical-layer challenges in UWA communication: channel estimation, adaptive modulation, and modulation recognition. Unlike prior reviews, it offers structured comparisons of model architectures, performance metrics, and deployment feasibility.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Ref. (Year)** & **Key Contributions** & **Limitations** \\ \hline
[4] (2022) & Targets adaptive modulation at the physical layer, and provides a taxonomy of ML algorithms while discussing their potential to address UWA challenges. & Does not evaluate ML and DL techniques for channel estimation or modulation recognition, and does not provide performance metrics for adaptive modulation. \\ \hline
[4] (2023) & Highlights the potential of ML and DL in addressing dynamic underwater environments, with a focus on adaptive modulation, channel prediction, and demodulation. & Lacks a detailed comparative analysis of ML and DL algorithms, including their system characteristics and performance metrics for key UWA challenges. \\ \hline
[5] (2023) & Focuses on source localization, target recognition, and geoacoustic inversion, while providing an evaluation of key techniques, datasets, and ML/DL models. & Lacks emphasis on ML and DL techniques for addressing UWA challenges such as channel estimation, adaptive modulation, and modulation recognition. \\ \hline
[10] (2024) & Analyzes UWA channel prediction techniques, categorizing them into linear, kernel-based, and deep learning approaches, with evaluations of performance and complexity. & Lacks investigations and the impact of adaptive modulation and modulation recognition on enhancing the efficiency and reliability of UWA communication. \\ \hline
[11] (2025) & Focuses on channel modeling, signal processing techniques, and network protocols, while suggesting future directions like standardized models and data-driven solutions. & Lacks exploration of ML and DL applications for specific UWA challenges, such as channel estimation, adaptive modulation, and modulation recognition. \\ \hline
[1] (2025) & Investigates DL techniques for modulation recognition in UWA communication, proposing a hybrid model with multi-scale feature fusion. & Does not explore the other key components such as channel estimation and adaptive modulation. \\ \hline
[8] (2025) & Analyzes routing protocols and ML algorithms, highlighting benefits, challenges, future prospects, and providing a detailed taxonomy and performance evaluation. & Does not explore channel estimation, adaptive modulation, and modulation recognition in terms of their key system characteristics and performance metrics. \\ \hline \end{tabular}
\end{table}
Table 1: Summary of existing reviews on ML/DL techniques in UWA communication.