# Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
## 3 Evaluation and Discussion
### Performance Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & \multicolumn{4}{c}{**Value**} \\ \hline  & **content-A** & **content-B** & **content-C** & **content-D** \\ \hline Simulation time & \multicolumn{4}{c}{600 [s]} \\ Number of eNB & \multicolumn{4}{c}{1} \\ Maximum data reception amount of eNB & \multicolumn{4}{c}{5 [Mbytes/s]} \\ Ideal load & \multicolumn{4}{c}{4 [Mbytes/s]} \\ Maximum send rate of UE & \multicolumn{4}{c}{500 [Kbyte/s]} \\ Number of UE & \multicolumn{4}{c}{6} \\ Movement method & \multicolumn{4}{c}{Stationary} \\ Initial position of UE & \multicolumn{4}{c}{Uniform distribution} \\ Content size & 7.1 [MB] & 2.1 [MB] & 38.5 [MB] & 1.4 [MB] \\ Data generation interval & 30 [s] & 60 [s] & 60 [s] & 120 [s] \\ Delay tolerance of content & 30 [s] & 60 [s] & 60 [s] & 120 [s] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Scenario for comparing performance. Figure 7: Topology of performance comparison. Secondly, we analyzed how the traffic exceeds \(L_{ideal}\). Table 7 shows the details of the temporal locality. From Table 7, we can confirm that the amount of excessive data of the proposed method is smaller than the time-wise offloading of MDOP. Furthermore, the maximum excess time from \(L_{ideal}\) is also shorter than the time-wise offloading of MDOP. In particular, from the viewpoint of the bandwidth utilization efficiency, it is desirable that the delay tolerance excess duration is short, because this value indicates that the temporal locality continues to occur within the excess duration. These results show that the proposed method suppresses the exceeded delay tolerance of content, because the MDOP sends the data at the maximum transmission rate without considering \(L_{ideal}\) when the delay tolerance is exceeded. Thus, the more the proposed method suppresses the exceeded delay tolerance, the more the proposed method can suppress the occurrence of the temporal locality. However, the excess frequency from \(L_{ideal}\) of the proposed method is greater than the time-wise offloading of MDOP. Although reducing the excess traffic and the excess time from \(L_{ideal}\) is the most important factor in resolving the temporal locality, it is desirable that the excess frequency is small. In order to reduce the excess frequency from \(L_{ideal}\), it appears to be necessary to consider the learning time increase and learning scenario diversification. By considering these, the proposed method more strictly controls the bandwidth. In addition to analyzing the excess traffic, we confirmed whether the traffic is distributed in terms of time. Figure 9 shows eNB usage rate based on \(L_{ideal}\). As eNB usage ratio approaches 1.0, it indicates that the eNB's performance is used sufficiently and temporal locality is reduced. From Figure 9, eNB usage ratio approached 1.0 when applying the proposed method for 600 s. This result shows that the proposed method can efficiently accommodate traffic to the eNB and fully utilizes the performance of the eNB. Therefore, we think that the proposed method balances traffic in terms of time. From Table 6, Table 7, and Figure 9, we confirmed that the proposed method can solve the temporal locality as compared with the time-wise offloading of MDOP. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{\
\begin{tabular}{c} **Excess Amount from \(L_{ideal}\) [MB]** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Transmission Amount Sent within \(L_{ideal}\) [MB]** \\ \end{tabular} } \\ \hline No-control & 33.9 & 1769.2 \\ MDOP & 4.2 & 1795.0 \\ Proposed method & 2.7 & 1797.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Excess volume and transmission volume for performance comparison. Figure 8: Learning result in performance comparison: (**a**) total reward; (**b**) average q-value. Based on the above results, we confirmed that the proposed method improves the bandwidth utilization efficiency of cellular infrastructure by allocating bandwidth while taking into consideration the delay tolerance.