## 5 Performance Evaluation
### Simulation Results  
Figure 8 shows the average throughput as a function of the iteration times for the three different episode times. As expected, the average throughput first increases as the number of iterations increases, and then it stabilizes. The environment is constantly changing as the nodes perform transmission actions. Thus, the average throughput fluctuates within a certain range. The greater the number of episodes, the more sample experiences DRLSA-IA collects, and thus the better the learning results and the higher the efficiency. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Settings** & **Value** \\ \hline Reward discount factor \(\gamma\) & 0.95 \\ \hline Learning rate \(\alpha\) & Decay from 0.01 to 0 \\ \hline Exploration probability \(\epsilon\) & Decay from 0.1 to 0.001 \\ \hline The physical size of the network \(G\) & 100 time slots \\ \hline The range of node communication & Entire network \\ \hline The range of interference communication & Entire network \\ \hline The number of nodes & 10\(\sim\)100 \\ \hline The network traffic & 10\(\sim\)200 packets/slots \\ \hline Number of simulation time slots \(T\) & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameter SettingsFigure 9 shows the average throughput as a function of the iteration times with different discount factors. We chose a configuration with 10 nodes, 10 time slots, and 100 episodes. As expected, the larger the discount factor, the higher the average throughput, but the slower the convergence rate. The discount factor \(\gamma\) is used to adjust the impact of short-term and long-term rewards. The value range of the discount factor is \((0,1]\). The closer the discount factor is to 0, the more the agent cares about short-term returns and the faster the algorithm converges. Otherwise, the agent considers long-term rewards, and the the algorithm converges more slowly. If the agent only focuses on short-term rewards, the algorithm may fall into a local optimum. If the discount factor is set to 1, the algorithm may be stuck in an infinite loop of states and is impossible to converge. In DRLSA-IA, the transmission of a node may not only interfere with ongoing transmissions but also have an impact on future transmissions. Therefore, we need to consider long-term rewards and set the discount factor to 0.95. We compare the throughput of DRLSA-IA with the other two algorithms with various network traffic. The number of nodes is 10. As shown in Figure 10, the throughput of DRLSA-IA outperforms that of the other two algorithms. Compared with Slotted-ALOHA and GA algorithm, the throughput of DRLSA-IA increase by 55.23% and 19.85%, respectively. Slotted-ALOHA sends the message as soon as there is a message. As a result,  
Figure 8: Correlation between the average throughput and iteration times for different episode times. Figure 9: Correlation between the average throughput and iteration times for different discount factors. the likelihood of conflicts increases as network traffic increases. Slotted-ALOHA cannot reasonably schedule message transmissions, and thus the throughput decreases. GA can reduce conflicts to some extent, but cannot achieve parallel transmission. However, due to TDIA, more than one node can concurrently transmit messages in DRLSA-IA, effectively improving the network throughput. Furthermore, interference is aligned in the interfered time slot, reducing conflicts. We next compare the \(PDR\) of DRLSA-IA with the other two algorithms with different network traffic. The number of nodes equals 10. As shown in Figure 11, the packet delivery ratio of DRLSA-IA outperforms that of the other two algorithms. Compared with Slotted-Aloha and GA, the \(PDR\) of DRLSA-IA is increased by 38.9% and 34.37%, respectively. With the increase in network traffic, the \(PDR\) of the three algorithms drops sharply. High traffic means there are more messages to be generated. That is, the possibility of message conflicts increases. Thus, the \(PDR\) of algorithms decreases.