## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Approach** & **Pros** & **Cons** \\ \hline RIPT [14] & Receiver-Initiated MAC & Reduces collisions, improves throughput & Synchronization precision issues, increased latency \\ \hline Handshaking-based [15] & Synchronization-based MAC & Better channel utilization & High latency, synchronization challenges \\ \hline Aloha Variants [16] & Random Access MAC & Improved throughput in low-density networks & High collision risk in dense networks \\ \hline Q-learning MAC [17] & Reinforcement Learning MAC & Adaptive and efficient computational overhead \\ \hline DR-D3 QN-MA [18] & Time-slot Optimization & Better slot allocation & Lacks real-world validation, energy inefficiency \\ \hline MR-SFAMA-Q [19] & Multi-Receiver Handshake & Increased adaptability & High complexity, energy consumption \\ \hline MACA-Q [20] & Q-learning MACA & Energy-efficient, improved throughput & Multi-hop inefficiency, collision risks \\ \hline Framed Aloha-Q [21] & Q-learning Framed Aloha & Reduces delay & Poor multi-hop efficiency, residual collisions \\ \hline TDMA Scheduling [22] & Time-Division MAC & Low collision rate & Inefficient under dynamic traffic conditions \\ \hline Traffic-Aware MAC [23] & Adaptive Scheduling & Improves fairness & Lacks routing integration \\ \hline RL-Multimedia MAC [24] & RL for Multimedia & Optimized throughput & High computational complexity \\ \hline Dynamic Slot Scheduling [25] & Adaptive Time Slot Allocation & Enhances adaptability & High processing overhead \\ \hline Contention-Free MAC [26] & Priority-based MAC & Ensures critical data transmission & Ignores lower-priority traffic \\ \hline Spatially Fair MAC [27] & Fairness-Based MAC & Improves network stability & Lacks dynamic adaptation \\ \hline EE-UWSNs [28] & Energy-Efficient MAC & Energy-aware routing integration & Limited real-world validation \\ \hline RL-Based MAC [29] & Reinforcement Learning MAC & Enhances scalability & High computational overhead \\ \hline Proposed Work & DQN-based MAC-Routing & Dynamic optimization, energy-efficient, scalable & Requires real-world testing and fine-tuning \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Literature Review on MAC Protocols for UWSNs. provide optimistic solutions, a significant drawback is the lack of real-world application and scalability evaluation of these solutions in dynamic marine environments. The authors in [34, 35, 36] emphasize significant advances in UWSNs and maritime communication networks and focus on the role that RL and 6G technologies play in addressing significant challenges. Specifically, refs. [34, 35] explain RL-based routing protocols for UWSNs with emphasis on addressing challenges such as high energy consumption, low bandwidth, and long propagation delays. These works compare various RL-based routing protocols using optimization criteria, performance evaluation, and application applicability, and also identify the directions of future research. However, redundancy of [34, 35] suggests that the two documents report similar material without contributing substantial new information. Meanwhile, ref. [36] directs the focus towards sea communication networks and describes the ways in which 6G technology can advance intelligent transportation systems, smart harbors, and ocean monitoring. This study provides a broader context for the marine communication infrastructure by integrating satellite, aerial, ground, and sea networks. Although these papers are dense in contribution, the major limitation is that there is not much large-scale experimental testing for RL-based UWSN protocols and real-world application cases for 6G MCNs. In the future, real-world implementation and hybrid approaches that combine RL and emerging network technologies to increase efficiency and reliability need to be addressed.