## 4 Results and Discussion  
A performance analysis for the proposed hybrid MAC protocol integration that utilizes CSMA/CA and TDMA in UWSN communication will be carried out in this section. The results illustrate the effects of multiple network properties on the throughput, energy efficiency, and collision prevention. In addition, we will discuss the performance of the system in relation to the adaptability of the system to changes in the network load conditions and articulate how the hybrid protocol performs and maintains good communication capabilities, even with the influence of potentially adverse environmental parameters. Finally, we highlight the performance of the system through a number of benchmarks to investigate it against state-of-the-art protocols in consideration of certain constraints, like energy consumption, transmission success rate, and scalability. The following subsections provide results of envy evaluation for various benchmark scenarios, showcasing that the proposed approach significantly outperforms the methods based on the evolution of envy per round. Benchmark 1  
In general, this benchmark, as outlined in [26], more specifically considers the WSN-based scenario. The proposed framework has solved one of the critical issues for performance enhancement: real scenario implementation of WSNs with limited power. This means energy efficiency improvement is their main focus. In addition to energy efficiency, maximum throughput, i.e., another performance metric for transmission, is also considered for maximized performance. Summarizing this point, ref. [26] broached the factors of employing a hybrid protocol that consists of TDMA and CSMA at the MAC layer for WSNs. High-level data frameworks enable concurrent transmission, mitigating solving both of the problems. This hybrid approach makes use of both TDMA and CSMA for more optimized network performance. They also present a new power control, which guarantees more energy saving and efficient usage of transmission slots. Benchmark 2  
The second benchmark [42] shows how far the developers have achieved developing underwater full-duplex sensors, which is one of the key technologies (currently) feasible due to the narrow bandwidth and long end-to-end transmission delays in UASNs. Based \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Symbol** & **Parameter** & **Description** \\ \hline \(\mathcal{A}\) & **Action Space** & Set of actions (e.g., power control, wake-up) \\ \hline \(\mathcal{S}\) & **State Space** & Environmental factors (temp., depth, salinity) \\ \hline \(\alpha\) & **Learning Rate** & Controls learning speed during updates \\ \hline \(\gamma\) & **Discount Factor** & Discounts future rewards (value 0.9) \\ \hline \(\epsilon\) & **Exploration Rate** & Probability of random action (value 0.1) \\ \hline \(R\) & **Reward Function** & Based on energy, throughput, latency \\ \hline \(P_{tx}\) & **Power Control Factor** & Adjusts power based on path loss and distance \\ \hline \(T_{slot}\) & **Time Slot Duration (TDMA)** & Duration of time slots for collision-free comm. \\ \hline \(T_{P}\) & **Transmission Power** & Adaptive power based on distance and conditions \\ \hline \(T\) & **Temperature Sensitivity** & Affected by depth, temperature (5–30 °C) \\ \hline \(\alpha_{PL}\) & **Path Loss Exponent** & Determines signal attenuation (typically 2.2) \\ \hline \(N_{actions}\) & **Max Actions** & Max 4 actions (power control, scheduling) \\ \hline \(N\) & **Node Density** & Node count per area (50–100 nodes) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Input parameters for AC-RL-based optimization in the proposed MAC framework. on the proposed framework, they have proposed a machine learning-based duplex MAC protocol for single-hop underwater acoustic sensor networks. But, much more importantly, it can also help to optimize throughput, delay, and access fairness.