# Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
## 6 Experimental Results and Discussions
The percentage of resource requests for cluster 1 (urban) and cluster 2 (sub-urban) are illustrated in Figure 7(a), b. Figure 7(a) accounts for 52.9% of throughput estimation, 37.3% of power consumption estimation, and 9.8% of SINR estimation. Figure 7(b) accounts for 52.9% of throughput estimation, 43.1% of power consumption estimation, and 3.9% of SINR estimation. This implies that more resource values are shared between users with the minimum values of power consumption and SINR. The resource management capability of our proposed system with a UAV altitude of 30 m and 60 m, respectively, is illustrated in Figure 7(c),d. When the A2G access link transmission involves 30 m and 60 m altitudes, the management of resources leads to a 31.4% and 90.2% throughput estimation user satisfaction ration, 58.8% and 5.9% power consumption estimation ratio, and 9.8% and 3.9% SINR estimation ratio. Therefore, DRL-based resource management is best when the altitude of the UAV is larger (i.e., 60 m) because its value is automatically changed and it has better performance in terms of user throughput values and lower values of power consumption and SINR. However, Figure 7(e) shows the management of resource estimations without applying the scheduling algorithm (i.e. round-robin). Even if the result is not bad, it is lower compared to others. The resource management of user throughput ratio, power consumption estimation ratio, and SINR estimation ratio is \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Metrics**} & \multicolumn{2}{c}{**Q-Learning**} & \multicolumn{2}{c}{**DRL**} \\ \cline{2-5}  & **Cluster 1** & **Cluster 2** & **Cluster 1** & **Cluster 2** \\ \hline Accuracy (\%) & 87.03 & 85.00 & 94.06 & 91.2 \\ Precision (\%) & 75.00 & 80.01 & 88.05 & 92.00 \\ Recall (\%) & 80.00 & 85.02 & 93.04 & 90.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation. Figure 7: **(a)** Training vs. testing evaluation based on deep reinforcement learning (DRL); **(b)** Illustration of clustering classes. 7.8%, 82.4%, and 9.8%, respectively. Therefore, the integration of the DRL system with the round robin scheduling algorithm performs better for the UAV-based IoT user resource management scheme. Figure 8: The average resource management of the target clusters for different schemes in different learning processes: (**a**) service request at cluster 1, (**b**) service request at cluster 2, (**c**) resource management at 30 m UAV altitude, (**d**) resource management at 60 m UAV altitude, and (**e**) average resource management without applying round robin. Under different scenarios, Table 6 shows the user throughput performance, average power consumption, and average SINR distribution with distinct user density. The user throughput value accuracy assessment is estimated to be 0.96, 0.967,0.967,0.97,0.97, and 0.97, respectively, when the user density is 1, 3, 6, 11, 21, and 26. This illustrates that, when user density is increased, it also improves the value of user throughput performance. This shows that the algorithm can manage a large number of IoT users in a dynamic environment. The energy of average consumption is estimated as 0.96, 0.965, 0.967, 0.967, and 0.96, when the user density becomes 1, 3, 6, 11, 21, and 26, respectively. From the Tx to the Rx is diminished when the number of IoT users is increased, which means a balance of power usage is maintained. The SINR and user density are also calculated to be 0.96, 0.97, 0.75, 0.96, 0.966, and 0.966 where the user density is 1, 3, 6, 11, 21, and 26, respectively. This demonstrates that, when the number of IoT users is increased, the SINR from a transmitter to a receiver is significantly decreased. The proposed algorithm can handle the interference between a user transmitter or an A2G access channel, even though the result is better as the number of IoT users increase. The performance measurement is then carried out using average user throughput data, average power usage, and average SINR results from the target values of clusters 1 and 2.