# Security Performance Analysis of Full-Duplex UAV Assisted Relay System Based on SWIPT Technology
## 4 Experimentation and Simulation  
In this section, the impact of the established model and the proposed new transmission protocol on the security and reliability of the system is verified through simulation experiments, and the applicability of the communication system in the airborne UAV eavesdropping scenario in real scenarios is explored by analyzing the simulation results of the proposed scheme in this paper. If not specified later, the experimental parameters were set as shown in Table 2. In order not to lose the generality of the experiment, the Cartesian 3D coordinate system parameters are all normalized distances, where \(D_{x}=1(\text{CP}^{\star}\) denotes the value taken in the calculation of CP, and SOP* denotes the value taken in the calculation of SOP). Figure 2 shows the variation of CP versus total transmit power \(P\) for UAV relay node U under different self-interference channel gain conditions. It can be seen that when the total transmit power increases, the connection probability CP shows a trend of increasing and then stabilizing. This is because when the power allocation factor \(\lambda\) and the power splitting ratio \(\beta\) are certain, the total transmit power increases, so the power \(P_{A}\) used for transmitting information in the first stage increases, and the channel capacity for effective information transmission increases; therefore, the connection probability shows an upward trend. When the total transmit power continues to increase, the channel capacity for effective information transmission tends to be close to the maximum transmission capacity, so the connection probability value of CP tends to stabilize. When the total transmit power \(P\) is certain, the CP increases as the self-interference channel gain of the UAV relay node U decreases. Because \(S_{u}\) smaller means smaller path loss, the SINR at the destination node B increases, and the channel capacity for effective information transmission increases, so the connection probability increases and the system connectivity performance is enhanced. Figure 3 gives the variation of CP with respect to the power allocation factor \(\lambda\) for different power splitting ratios \(\beta\). As can be seen from the figure, CP tends to be close to 0 when the power allocation factor \(\lambda\) is small, and CP increases as \(\lambda\) increases. An increase in \(\lambda\) means that the more power \(P_{A}\) is used to transmit information in the first stage, therefore, the channel capacity for effective information transmission increases, which confirms the correctness of the conclusions of Figure 2. When the power splitting ratio \(\beta\) is certain, there exists a system connection initiation value \(\lambda^{*}\). When \(\lambda<\lambda^{*}\), the power allocated to the signal transmission is not enough to transmit a valid signal, at this time the system is in the  
unconnected state; when \(\lambda\geq\lambda^{*}\), the system can only be in the connected state. From the figure, it can be seen that when the other condition parameters are certain, the larger the value of power splitting ratio \(\beta\) is taken, the larger the value of system connection initiation \(\lambda^{*}\) is. When the power splitting ratio \(\beta\) decreases from 0.9 to 0.6 at a certain value of \(\lambda\), the power used for energy harvesting decreases while the power used for the first stage of signal processing increases, and therefore the connection probability is enhanced. Figure 4 investigates the variation relationship between connection probability CP and UAV self-interference gain \(S_{u}\) under different flight altitudes of UAV relay node U. From the figure, it can be seen that when \(S_{u}\) increasing, CP decreases, and the higher the height \(H_{1}\) of UAV relay node U, the greater the decreasing trend of CP; when \(S_{u}\) is certain, the higher the normalized height of UAV relay node U, the greater the CP.