## 5 Performance Evaluation
### Simulation Results
However, DRLSA-IA allows multiple nodes to transmit simultaneously without collisions, which greatly improves the transmission efficiency. The other two algorithms only allow one message transmission at most. Therefore, the \(PDR\) of DRLSA-IA is higher than the other two algorithms. Figure 11: Packet delivery ratio of algorithms with different network traffic. Figure 10: Throughput of protocols with different network traffic. We also compare the fairness index of the three algorithms with different network traffic. The number of nodes equals 10. As shown in Figure 12, the \(FI\) of DRLSA-IA is marginally less than the \(FI\) of Slotted-Aloha, remaining approximately 0.9. The reason is that Slotted-Aloha starts transmission immediately when there is a message. However, the Slotted-Aloha throughput is considerably low. Compared with Slotted-ALOHA, the throughput of DRLSA-IA increases by 55.23%. Moreover, the \(FI\) of DRLSA-IA is greater than the \(FI\) of GA.  
We then study the network throughput of algorithms with a different number of nodes; the offered network traffic is 80 packets/slots. As shown in Figure 13, the throughput of DRLSA-IA outperforms the other two algorithms. Compared with GA and Slotted-Aloha, the throughput of DRLSA-IA increases by 22.77% and 3.36 times, respectively. The main reason is that DRLSA-IA uses deep reinforcement learning algorithms to intelligently learn the idea of time-domain interference alignment. Moreover, as there is no need to enumerate the state space, DRLSA-IA can be applied to a network with a large number of nodes. We also study the packet delivery ratio of algorithms with a different number of nodes; the offered network traffic is 80 packets/slots. As shown in Figure 14, the \(PDR\) of DRLSA-IA outperforms that of the other two algorithms. Compared with GA and Slotted-Aloha  
Figure 12: Fairness index of algorithms with different network traffic. Figure 13: Throughput of algorithms with different number of nodes. algorithm, the \(PDR\) of DRLSA-IA increases by 20.46% and 3.38 times, respectively. The start transmission time and propagation delay between sender and receiver determine whether there will be a collision at the destination node. Thus, reception time can be shown as a duality between the transmission time and the location of the nodes. In DRLSA-IA, we use reinforcement learning to learn the relationship between them. In addition, we design the TDIA-based reward mechanism to improve the \(PDR\) and reduce message collisions. DRLSA-IA can better adapt to different network scales and maintain a highly \(PDR\) in large-scale networks. We evaluate the fairness index of algorithms with a different number of nodes. The network traffic is 80 packets/slots. As shown in Figure 15, the fairness index of DRLSA-IA outperforms that of the other two algorithms. Compared with Slotted-Aloha and GA algorithm. the fairness index of DRLSA-IA increases by 14.23% and 75.75%, respectively. As the number of nodes increases, the fairness index of DRLSA-IA and GA algorithms decreases, while the Slotted-Aloha remains basically unchanged. The main reason is that as long as there is a message, Slotted-Aloha will immediately send it in the next slot. Changes in the number of nodes do not affect the fairness of Slotted-Aloha. The fairness of DRLSA-IA decreases slightly as the number of nodes increases, but that of DRLSA-IA is still fairer than the other two algorithms. Figure 14: Packet delivery ratio of algorithms with different number of nodes. Figure 15: Fairness index of protocols with different number of nodes.