# Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
## 5 Simulation Results  
In this part, we measure the system performance of our proposed placement and power allocation iterative algorithm by numerical results. We consider a NOMA-based downlink UAV relaying networks scene where the coordinate of BS is set as \(I_{BS}=[0,0]^{T}\), and the users are randomly distributed over a 300 m \(\times\) 300 m geometric region with the geometric center [150 m, 150 m]. We set the power of AWGN \(\sigma_{R}^{2}=\sigma_{U_{m}}^{2}=10^{-11}\) W, \(R_{n}^{\min}=0.5\)\(\mathrm{bps/Hz}\), \(\beta_{0}=10^{-3}\), and assumed that UAV and users both suffered the same grade HIs. Figure 2 illustrates the different UAV deployment locations when the system \(\mathrm{P_{S}=2}\) W, \(\mathrm{P_{UAV}=0.03}\) W, \(\mathrm{H=100}\) m and 8 downlink users exist in the system. The key parameter settings are summarized in Table 2.  
Figure 3 shows the convergence performance of iterations with a fixed UAV height 100 m and \(\mathrm{P_{UAV}=0.03}\) W, BS with maximum power \(\mathrm{P_{S}=2}\) W and 3 randomly deployed users. It can be concluded from the simulation figure that the proposed algorithm could converge to the optimal value within three times in the worst case, which verifies the effectiveness and rapid convergence of the proposed iterative algorithm. When the HIs increase at equal intervals, the system performance worsens. Figure 2: System user’s distribution versus the different scheme of UAV deployment. Figure 4 shows the system downlink sum rate versus P\({}_{\rm UAV}\) with a UAV height of 100 m, BS with maximum power P\({}_{\rm s}\) = 2 W and three randomly deployed users. We compared the proposed scheme with a scheme that deploys the UAV to optimize the power allocation at the user's geometric center location and an average power allocation scheme that optimizes the UAV deployment location. As we expected, the sum rate of the different schemes suffering from different degrees of HIs increased with the increment transmitted power of UAV, and The proposed scheme always achieves the maximum sum rate performance with the same degree of hardware impairments. It is noteworthy that the impact of the UAV's deployment location on the system's sum rate was more significant than that of the fixed deployment location optimized power scheme with fewer hardware impairments. This impact was diminished as the hardware impairments level increased. Figure 5 shows the system downlink sum rate versus P\({}_{\rm S}\) with a UAV height of 100 m, UAV with maximum power P\({}_{\rm UAV}\) = 0.03 W, and three randomly deployed users. The simulation results indicate that the proposed scheme exhibited a trend of initial rapid increase followed by gradual growth with respect to the base station power. It can also be observed that the extent of the hardware impairments was exacerbated, and the sum rate performance of the system decreased. A lower maximum base station transmission power led to a greater impact on the system's sum rate when considering the constant maximum transmission power of the UAV. On the other hand, increasing the transmission power of the UAV with a constant maximum base station transmission power resulted in an enhanced system's sum rate. Nevertheless, it should be noted that an intensification of hardware impairments led to a reduction in differentials. \begin{table}
\begin{tabular}{c c} \hline Base station transmit maximum power P\({}_{\rm S}\) & 2 W \\ \hline UAV transmit maximum power P\({}_{\rm UAV}\) & 0.1 W \\ \hline AWGN \(\sigma_{R}^{2},\sigma_{\rm Its}^{2}\) & \(10^{-11}\) W \\ \hline The flight height H & 100–120 m \\ \hline QoS \(R_{n}^{\rm min}\) & 0.5 bps/Hz \\ \hline Channel power gain \(\beta_{0}\) at a distance of 1 m & –30 dB \\ \hline Coverage area of UAV & 300 m \(\,\times\,\) 300 m \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 3: System downlink sum rate versus the number of iterations.