# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
## 5 Analysis of Simulation Results  
In order to evaluate the performance of the proposed algorithm, we use python to simulate the algorithm and compare it with the caching strategy in the literature [10]. The path loss index is \(\alpha=4\), the channel fading gain is \(\chi=10^{-2}\), the channel width is \(B=1.1\,M\!H\!Z\), the power of Gaussian noise is \(P_{n}=-110\,\ dBm\), and other simulation parameters are shown in Table 1.  
Figure 6 gives the variation of utility with different total number of contents, where the total utility of the NO is determined by both the benefits of the NO and the average delay of the vehicle users. From Figure 6a-d, it can be seen that, as the total number of contents increases, the total utility of the CP and the total utility of the NO decrease, the benefit of the NO increases, and the average request delay increases. This is because the cache capacity of the RSUs and the UAV is fixed and the larger the total amount of content, the smaller the percentage of content cached, the smaller the probabil \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline The number of contents & 50 \\ Cache capacity of RSU & 10 \\ Cache capacity of UAV & 5 \\ The parameter of Zipf distribution & 0.7 \\ The transmission power of RSU (mW) & 2000 \\ The transmission power of UAV (mW) & 200 \\ The communication radius of RSU (m) & 300 \\ The communication radius of UAV (m) & 100 \\ The length of lane (m) & 600 \\ The fee \(b\) paid by the vehicle user & 1 \\ Benefit sharing ratio \(\theta_{1}\) & 0.9 \\ Benefit sharing ratio \(\theta_{2}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. content through cache nodes and the larger the content request latency. The total utility of NO is determined by the benefits of NO and the average request latency; thus, as the total amount of content increases, the total utility of NO decreases, which leads to a lower demand for content by NO during the game, so that the rental price of content decreases and the total utility of CP decreases. Compared with the strategy in [10], the benefit of CP for the strategy in this paper is slightly lower than that of [10]; all other performances are better than that of the strategy in [10]. This is because the policy in [10] does not chunk the content, so if the content is not fully available when the vehicle user drives out of the coverage of the cache node, it needs to be re-downloaded at the next cache node, which leads to a lower probability of successful delivery. Moreover, the policy in [10] does not apply UAV, which leads to an increase in delay, a decrease in the benefit of the NO, and a decrease in the utility of the NO. Since the benefits of CP and NO are fixed, the utility of CP is slightly greater than that of this paper. The performance of this paper is better compared to the caching-by-popularity strategy. This is because the dynamic programming algorithm applied in this paper can optimize both the content rental price and the caching decision of RSUs to obtain better performance compared with caching by popularity. Figure 6: Performance versus total content. (**a**) Total utility of CP in relation to the total number of contents; (**b**) total utility of NO in relation to the total number of contents; (**c**) relationship between the benefit of NO and the total number of contents; (**d**) relationship between average delay and total number of contents. Figure 7 gives the relationship between the performance of this paper's strategy and the Zipf distribution parameter for different total number of contents. From Figure 7b-d, it can be seen that the average delay decreases with the increase in Zipf distribution parameters when the total number of contents is constant and the benefit and total utility of NO increases with the increase in Zipf distribution parameters.