## 4 Performance Evaluation
### Result Evaluation
#### 4.3.7 Channel Utilization vs. Slot Size  
UWCNs are a domain where effective communication depends critically on the dynamic interaction between channel use and slot size. The severe underwater environment presents tremendous problems for traditional static techniques to channel allocation and setting slot sizes since acoustic channels display substantial attenuation and unpredictable propagation delays. Here, Q-learning dynamically adjusts to changing network circumstances, maximizing channel use and slot size for improved efficiency, as shown in Figure 12. By using historical data to identify the best regulations, the algorithm creates a responsive and adaptable network that lowers latency, decreases collisions, and increases overall efficiency. One potential option is to use the reinforcement learning method known as Q-learning. The applicability of Q-learning in the management and optimization of channel allocation and episodes makes this work very important in improving the effectiveness of communication in UWCNs. Underwater communication is characterized by high attenua  
Figure 11: Average traffic time vs. no of nodes. Figure 12: Comparison of channel utilization and slot size. tion, varying propagation delay, and varying conditions, making it tricky to establish an appreciable, clear channel. Q-learning is effective in tackling these issues mainly due to the fact that the system can learn different approaches in different stages depending on the network conditions. In particular, it maximizes the usage level and distribution of the entire communication links, which plays a critical role in addressing issues of latency and loss of packets. An episode in Q-learning refers to a sequence of decision making and interaction that takes place within the network. Through such episodes, the Q-learning algorithm dynamically modifies the channel selection patterns depending on prior encounters to enhance system performance. This dynamic adjustment process is more useful in underwater networks than in terrestrial networks since activity in the underwater environment changes more frequently. It is seen from Figure 13 that the Q-learning enables the system not only to manage the episodes but also to control the usage of channels that would enhance the entire performance of the UWCN. Further, the interaction of these aspects is examined in detail in the study to demonstrate how Q-learning can cope with diverse challenges of underwater communication management, including the variability in the propagation delay and high error rate, given that it can adapt to new conditions as they are encountered. By comparing Q-learning to more conventional approaches, this comparative study aims to assess Q-learning's effectiveness and provide insight into how well it can handle the complexities of underwater communication, eventually leading to the creation of more resilient and adaptable UWCNs. The graph in Figure 14 explains channel usage, which is an important parameter which signifies the credentials of the network in terms of how well is it using all the available channels for data transfer. In terms of throughput and energy consumption, the proposed protocol has better performance than T-LOHI-MAC, CSMA-MAC, SFAMA-MAC, and CAPC-MAC under various network scenarios, and especially in mobile network scenarios. As illustrated in Table 3, our protocol outperforms these alternatives, showing notable gains in channel optimization: We achieved an average of 24% improvement in throughput compared to T-LOHI-MAC, 20% compared to SFAMA-MAC, 17% compared to CAPC-MAC, and 16% compared to CSMA-MAC. This demonstrates that the proposed system is capable of maintaining a good level in dynamic mobile underwater networks, which again justifies the general enhancement in system performance, as demonstrated in Figure 15.  
Figure 13: Comparison of channel utilization and slot size.