# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 4 Result and Discussion
### Performance Comparison between LSTM-RRM and DRRM  
Here, the performance of the LSTM-RRM method is analyzed in terms of throughput, outage, Jain's index, and dual connectivity. These performances are compared with DRRM [20] to show the effectiveness of the LSTM-RRM method. The DRRM [20] was also implemented and simulated in NS-3 to evaluate the LSTM-RRM method. Figure 5 and Table 3 show the comparison of UE throughput between DRRM [20] and LSTM-RRM. Here, the comparison is made by varying the number of UEs from 5 to 30. From the analysis, it is concluded that the LSTM-RRM achieves higher UE throughput than the DRRM [20]. For example, the UE throughput of the LSTM-RRM varies from 15 Mbps to 63 Mbps, whilst DRRM's [20] UE throughput varies from 10 Mbps to 61 Mbps. Specifically, the UE throughput of LSTM-RRM for 30 UE is improved up to 50% when compared to the DRRM [20]. The LSTM-RRM achieves higher UE throughput because it mitigates the burst errors by using frequency interleaving, and ISI is minimized by using guard level insertion. The comparison of 50 percentile UE throughput between DRRM [20] and LSTM-RRM is shown in Figure 6 and Table 4. Here, the 50 th percentile UE throughput is analyzed by varying the UE from 5 to 30. From the analysis, it is can be seen that the 50 percentile UE throughput of LSTM-RRM is higher than the DRRM [20]. For the instance, the 50 percentile UE throughput of LSTM-RRM is in the range from 65 Mbps to 125 Mbps, whereas the DRRM [20] is in the range from 40 Mbps to 95 Mbps. an effective resource allocation over the 5G network is used to increase the throughput of the LSTM-RRM. The 50 percentile \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Height of BS & 10 m \\ Transmission power of BS & 35 dBm \\ Type of transmission antenna & Narrow beam \\ System bandwidth & 100 MHz \\ Carrier frequency & 28 GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specification parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of UEs**} & \multicolumn{3}{c}{**UE Throughput (Mbps)**} \\ \cline{2-3}  & **DRRM**[20] & **LSTM-RRM** \\ \hline
5 & 61 & 63 \\
10 & 39 & 44 \\
15 & 30 & 35 \\
20 & 22 & 28 \\
25 & 18 & 24 \\
30 & 10 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of UE throughput for LSTM-RRM and DRRM. UE throughput of LSTM-RRM for 30 UE is improved up to 62.5% when compared to the DRRM [20]. Figure 5: Comparison of minimum UE throughput. Figure 6: Comparison of 50% UE throughput. The throughput comparison of 90% UE is shown in Figure 7 and Table 5. Here, the variation of the throughput is analyzed for different numbers of user equipment: 5, 10, 15, 20, 25, and 30. From Figure 7 and Table 5, it can be seen that the LSTM-RRM method obtains higher throughput than the DRRM [20]. For example, the 90% UE throughput of the LSTM-RRM varies from 140 Mbps to 235 Mbps, whilst DRRM [20]'s UE throughput varies from 80 Mbps to 215 Mbps. Specifically, the 90% UE throughput of LSTM-RRM for 30 UE is improved up to 75% when compared to the DRRM [20]. The LSTM-RRM method obtains higher throughput due to the optimal power and bandwidth allocation to the desired UE. Moreover, the losses through the 5G environment are minimized by using frequency interleaving and guard interval insertion. Therefore, the amount of packets received by the BS is increased during the communication. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of UEs**} & \multicolumn{2}{c}{**50\% UE Throughput (Mbps)**} \\ \cline{2-3}  & **DRRM**[20] & **LSTM-RRM** \\ \hline
5 & 95 & 125 \\
10 & 80 & 100 \\
15 & 70 & 95 \\
20 & 60 & 88 \\
25 & 50 & 74 \\
30 & 40 & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of 50% UE throughput for LSTM-RRM and DRRM.