## 6 Simulation Result and Analysis
### Transmission Success Rate and Power Efficiency  
The simulation result of transmission success rate is illustrated in Figure 5(a), where the horizontal axis denotes the terminals' average arrival and thus shows the IoT transmission traffic. When the traffic load exceeds a certain limit, the transmission success rate of all methods begins to decrease with the increase in traffic. The DRL-RA, which takes the success of the current transmission as the immediate benefit, achieves the best effect. The proposed DRL-QoS-RA algorithm can also achieve a higher transmission success rate than the DRL-EERA and GA. For example, when the transmission success rate is 0.8, DRL-RA achieves the traffic arrival rate \(\lambda=10.90\), whereas DRL-QoS-RA, DRL-EERA and GA can carry the traffic with the arrival rate \(\lambda=8.48\), \(\lambda=4.87\), and \(\lambda=5.21\), respectively. In other words, the proposed DRL-QoS-RA method can improve transmission success rate by 74.12% and 62.76% compared with DRL-EERA and GA, respectively. The reason why DRL-RA achieves better performance than the other methods may lie in the fact that its reward concentrates on success rate. Figure 5: Deployment training process based on transfer learning. Figure 6b shows the trend of the terminals' energy efficiency with the gain of beam traffic. With the increase in the average transmission rate of the terminal, the power utilization rates of DRL-QoS-RA, DRL-EERA, DRL-RA, and GA show a similar trend. After a slight increase, they decrease and become stable eventually. The reason for the subsequent decline stage is that with the increase in requests, co-channel interference inevitably increases, and thus the increase in throughput is less than the power consumption. Then, with the continuous increase in traffic flow, the system becomes saturated by rejecting many new transmission requests. As a result, the power utilization rate of the system appears to be stable. Concerning the power efficiency of S-IoT's normal operation condition, when we can find that the proposed DRL-QoS-RA is 0.66 Mbit/(Joule), while that of DRL-EERA, GA and DRL-RA is 0.69 Mbit/(Joule), 0.41 Mbit/(Joule), and 0.27 Mbit/(Joule), respectively. The proposed method can improve the power efficiency by 60.91% and 144.44% compared with GA and DRL_RA, while its power efficiency is only 4.55% lower than that of DRL-EERA. Although DRL-EERA and DRL-RA achieve the best performance in energy efficiency and success rate, respectively, the proposed DRL-QoS-RA better achieves a trade-off between energy saving and transmission QoS satisfaction. Table 3 shows the success rate, energy efficiency and computational time of the above six optimization algorithms under different transmission frequencies. The calculation time of the GA method increases greatly with the increase in terminal request's frequency; thus, it is not suitable for S-IoT. The calculation time of the four DRL-based methods remains stable with the increase in in-service terminal. As DRL-QoS-RA transferred adopts the transfer learning mechanism, its training steps are lower than the other three DRL-based methods and can achieve a favorable transmission success rate and energy efficiency with lower calculation cost. Similar to the performance in Figure 6, the proposed DRL-QoS-RA and DRL-QoS-RA-transferred can effectively achieve the tradeoff between energy utilization and transmission success rate. Such a promotion is presented with either low terminal transmission frequency (when S-IoT is relatively idle) or high terminal transmission frequency (when S-IoT tends to be saturated).