# Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
## 5 Simulation Results
Figure 6 indicates the system downlink sum rate versus a different number of users with \(\rm{P_{s}}=2\) W, \(\rm{P_{UAV}}=0.05\) W; the height of the drone was 100 m. We compared the optimization power allocation NOMA scheme for drone deployment at the user's geometric center and the OMA scheme for optimal location optimization power allocation. In the scheme where the drone was deployed at the user's geometric center with optimized power under ideal hardware conditions, the performance obtained by the system was only slightly higher than that obtained by the proposed scheme when \(\rm{HIs}=0.02\), demonstrating the effectiveness of the proposed algorithm. In the case of NOMA, the sum rate increased when the power allocation meet the QoS requirements of all the admitted users. The sum rate decreased with the number of users increased. This is because the transmitted power was not large enough to satisfy the demand of each user. In the case of OMA, the system's sum rate deteriorated as the number of users increased. The performance of the power-optimized OMA scheme at the optimal location degraded by about 45% compared to the system's sum rate that was obtained with the proposed NOMA scheme with ideal hardware. Figure 4: System downlink sum rate versus the maximum power of UAV. Figure 5: System downlink sum rate versus the maximum power of BS with different HIs. Figure 7 shows the total system downlink rate versus the deployed UAV at different altitudes for different schemes with \(\text{P}_{\text{s}}=2\) W, \(\text{P}_{\text{UAV}}=0.03\) W, three downlink users, and HIs of 0.02. We compared the NOMA scenario, which included the UAV deployed at the geometric center of the user with optimized power and average power, and the OMA scenario, which included the UAV deployed at the optimal location of the UAV using optimized power and average power. It can be seen from the results that the performance of all schemes tended to decrease as the UAV altitude increased, which was because the corresponding channel gain became worse as the distance between the UAV and the ground user increased. Still, the proposed scheme always achieved optimal performance. Figure 6: System downlink sum rate versus the number of users. Figure 7: System downlink sum rate versus the altitude of UAV. Figure 8 shows the system downlink sum rate versus \(\rm P_{S}\) under different schemes, where the UAV height was 100 m, \(\rm HIs=0.02\), UAV with maximum power \(\rm P_{UAV}=0.03\) W, and three randomly deployed users. With the same level of hardware impairments, the system's sum rate obtained for all schemes increased as the base station transmit power increased. Because base stations transmit more power, the system can allocate more power to each user. In addition, the optimal performance was always obtained for our proposed scheme because it optimized both the UAV deployment location and the user power allocation while considering the hardware impairments. Figure 9 shows the system downlink sum rate versus \(\rm P_{UAV}\) with a UAV height of 100 m, BS with maximum power \(\rm P_{S}=2\) W, \(\rm HIs=0.02\), and three randomly deployed users. We compared the proposed scheme with the NOMA scheme and optimized average power at the UAV deployment location and the NOMA scheme with an optimized power allocation to deploy the UAV at the user center, and the OMA scheme with optimized power allocation at the optimal deployment location and the OMA scheme with optimized power allocation to deploy the UAV at the user center, respectively. This scheme jointly considered UAV deployment with system power allocation and always obtained the highest sum rate for the same hardware impairments. Unlike Figure 8, increasing the transmitting power of the UAV achieved a higher and more significant system performance with constant base station power. Figure 8: System downlink sum rate versus the maximum power of BS with same HIs.