## 4 Performance Evaluation
### Convergence Metrics and Sensitivity Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Justification** \\ \hline Learning Rate & \(\alpha\) & 0.05 & Ensures stable learning while avoiding divergence. \\ \hline Discount Factor & \(\gamma\) & 0.85 & Balances short-term and long-term reward optimization. \\ \hline Exploration & \(\epsilon\) & 0.95\({}^{\dagger}\) & Gradual decay maintains sufficient exploration. \\ \hline Mini-batch Size & - & 16–32 & Optimizes memory efficiency while retaining learning quality. \\ \hline Target Network & - & Every 500 iterations & Reduces computational complexity while maintaining stability. \\ Update Frequency & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Achieved Sensitivity Analysis Results. Figure 9: Collision rate vs. Traffic load. Figure 11 bar chart shows how four slots (Slot 1 to Slot 4) are successfully used over 10 frames in the network. Each bar represents a frame, and the different colors show how much each slot contributed to successful communication. Some frames show a balanced use of all slots, while others have uneven usage, likely because of competition or how the proposed DAWPC-MAC system schedules access. This pattern gives a clear picture of how well the network manages its resources and whether the slots are shared fairly and efficiently among users. Figure 12 \"Throughput vs. Time\", shows how proposed DAWPC-MAC system performance improves over time. At first, the throughput is relatively low, around 0.4, but it steadily increases as the system adjusts and stabilizes. By the halfway point, the performance reaches about 0.7, indicating significant progress. Toward the end, the graph shows that the throughput levels off at around 0.9, demonstrating that the system has reached a stable and efficient state. This visualization highlights how systems can improve their efficiency over time before settling into an optimal performance range. Finally, the utility function weights in the equation are used to combine factors affecting the decision making process to select the node-slot pair in the Q-learning-based MAC protocol. \[U_{\text{output}}=w_{1}\cdot Q+w_{2}\cdot\left(\frac{1}{D_{\text{norm}}}\right) +w_{3}\cdot E_{\text{norm}}+w_{4}\cdot S_{\text{norm}}+w_{5}\cdot\left(\frac{ 1}{T_{\text{norm}}}\right)+w_{6}\cdot A_{\text{norm}}. \tag{29}\]  
Each factor, representing an important aspect of network performance or node behavior, is assigned a weight \(w_{1},w_{2},w_{3},w_{4},w_{5},w_{6}\) that determines its relative importance in the final utility value. Specifically, \(w_{1}\) is associated with the quality of the node's signal, with higher signal quality (represented by \(Q\)) being preferred. \(w_{2}\) and \(w_{5}\) are related to the distance and transmission rate, respectively, with smaller distances (represented by \(D_{\text{norm}}\)) and higher transmission rates (represented by \(T_{\text{norm}}\)) being favored. \(w_{3}\) focuses  
Figure 11: Successful and unsuccessful slot usage across frames. Figure 12: Through vs. Time. on energy efficiency, with nodes having higher remaining energy being prioritized. \(w_{4}\) reflects stability, with more stable nodes being favored for their consistent behavior. Finally, \(w_{6}\) accounts for signal attenuation, with less attenuation (represented by \(A_{\text{norm}}\)) being preferred for effective communication. By assigning different weights to these factors, the utility function allows the prioritization of nodes based on overall network goals, such as maximizing throughput, minimizing energy consumption, and reducing collisions, leading to more efficient and reliable slot allocation in the network. Figure 13 bar chart, \"Utility Scores for Candidate Nodes (First Simulation)\", is used to compare the utility of different nodes in a simulated scenario. The nodes are numbered from 1 to 5, and their utility scores are displayed on the y-axis. Node 2 is identified as the best choice, with the highest score of around 3.5.