## 4 Simulation Results and Discussions
### Performance Comparison  
To better simulate real-world conditions, we have crafted two distinct simulation scenarios, visually depicted in Figure 2. In Scenario 1, the UAV follows a circular trajectory above both the RSU and the jammer, with the RSU functioning as the receiver. Conversely, in Scenario 2, the UAV trails the trajectory of a dynamically moving vehicle, wherein the vehicle serves as the designated receiver. These carefully constructed scenarios facilitate a thorough evaluation and subsequent analysis of the UAV and jammer's performance in varying operational contexts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Notation** & **Value** \\ \hline UAV sensing start frequency & \(f_{U}^{s}\) & \(100\,\mathrm{MHz}\) \\ Total bandwidth & \(B\) & \(100\,\mathrm{MHz}\) \\ Sub-bands’ number & \(M\) & \(25\) \\ UAV’s transmission power & \(P_{U}\) & \(15\,\mathrm{dBm}\) \\ UAV’s transmission cost & \(\lambda_{1}\) & \(0.5\) \\ Jammer’s transmission cost & \(\lambda_{2}\) & \(0.25\) \\ Roll-off factor & \(\eta\) & \(0.4\) \\ Channel gain at the one-meter distance & \(g\omega_{0}\cdot g\)\(\mathbf{1}_{0}\) & \(-60\,\mathrm{dBm}\) \\ Path loss exponent & \(\beta\) & \(3\) \\ Gaussian white noise PSD \(N(f)\) & \(\delta^{2}\) & \(-110\,\mathrm{dBm}\) \\ Parameter in the standard linear programming & \(\zeta\) & \(0.25\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 2: The UAV trajectory and the receiver’s position in different scenarios.  
Figure 3 demonstrates that in Scenario 1, neither the UAV nor the jammer can enhance their respective payoffs by independently adjusting their strategies. Correspondingly, Figure 4 elucidates that in Scenario 2, both the UAV and the jammer are incapable of augmenting their communication revenue through unilateral strategic modifications.  
These observations imply that, within this particular strategy set, any player who unilaterally changes its strategy--while keeping the other player's strategy constant--will not achieve an increase in its individual payoff. This provides compelling evidence that the derived strategies constitute a Nash equilibrium.  
Figure 5 substantiates the superior payoff and stability of our proposed method in comparison to Frequency-Hopping Spread Spectrum (FHSS) and Random Selection (RS). In Scenario 1, our approach yields an average UAV communication payoff that exceeds FHSS by 13.5 and RS by 15.2. Similarly, in Scenario 2, our method surpasses FHSS by an average of 11.1 and RS by 9.4 in UAV communication payoff. These results underscore the efficacy of our approach in augmenting UAV communication performance, particularly in outclassing FHSS and RS under dynamic conditions.  
As illustrated in Figure 6, it becomes apparent that increasing the number of sub-bands--thereby reducing the bandwidth of each--yields higher payoffs for the UAV. This enhancement can be attributed to the increased complexity the jammer faces in disrupting the corresponding signaling channels when more sub-bands are available. Consequently, the UAV gains a strategic advantage as the jammer struggles to execute effective attacks, leading to improved communication performance and elevated payoffs for the UAV. Furthermore, across various scenarios with different sub-bandwidths, our proposed algorithm consistently achieves the highest communication benefits. This advantage becomes increasingly pronounced as the sub-bandwidth expands. Specifically, when the sub-bandwidth  
Figure 4: Scenario 2 model validation.  
Figure 3: Scenario 1 model validation.  
is set to 5 MHz, the average payoff of our proposed method surpasses that of the FHSS method by 16.8 and the RS method by 17.1.