# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
## 6 Simulation Results
By increasing the IoT device's maximum power, the sum rate is nearly the same and does not change; this is due to the slight increase in the device's maximum power that it does not affect the sum rate. Figure 6: Fairness index versus number of users. Figure 7: Sum rate versus the power of the IoT devices. Figure 8 shows a comparison between the proposed algorithm and the best CQI in the achieved sum rate versus the number of users at different bandwidths; in case of 6, 10 and 25 resource blocks. They achieved nearly the same performance, which proves the superiority of the proposed algorithm. As the number of users increases, the sum rate increases to a certain saturation point after which the sum rate is almost constant due to scheduling the maximum number of users \(N_{\text{max}}\) in every TTI achieving almost the same sum rate. It can be noticed that the sum rate in case of 25 RBs outperforms that in case of 6 RBs and 10 RBs, while it is the least in the case of 6 RBs. It is observed that the saturation point is shifted to the right as the number of RBs increases, meaning that as the number of RBs increases, the sum rate saturates at larger number of users. Figure 9 shows the achieved sum rate versus the bandwidth (number of RBs) with maximum 2 users allocated to the resource block. The spectral efficiency of the proposed algorithm is almost closer to that of the maximum CQI, which verifies the efficiency of the proposed algorithm in terms of spectral efficiency. The results obtained in Figures 7-9, show the excellence of the proposed algorithm in terms of spectral efficiency and sum rate. This is because the proposed algorithm cannot exceed the best CQI which represents the upper bound in throughput and spectral efficiency. However, the proposed algorithm records a very close performance. Figure 10 shows the spectral efficiency versus the maximum power of the IoT devices comparing the performance of both the proposed resource allocation algorithm and the best CQI once using the proposed power allocation and once using distance-based power allocation. As can be clearly seen, the proposed power allocation outperforms the distance=based power allocation. Moreover, as expected, the maximum CQI is superior in terms of spectral efficiency which is on average 41.2 bps/Hz followed by the proposed algorithm which achieves very close performance, nearly 40.5 bps/Hz and about 1.69% decrease only. To evaluate the reliability of the algorithms, the relationship between the packet size and probability of decoding error is shown in Figure 11. As expected, as the packet size increases, the probability of error increases in both algorithms. However, the probability of error \(\varepsilon\) in the proposed algorithm is significantly lower than that of the max CQI, where at  
Figure 8: Sum rate versus number of users. packet size = 1000 bits, \(\varepsilon\) is about \(8.5\times 10^{-11}\) and \(4.2\times 10^{-8}\) in the proposed algorithm and the max CQI respectively, resulting in nearly 99.7% performance improvement. Figure 10: Spectral efficiency versus the power of the IoT devices. Figure 9: Sum rate versus the number of RBs. The computational complexity of both the proposed algorithm and the best CQI is \(O(KN)\) which means that they have linear complexity in the number of users and number of RBs. However, in [36], resource allocation for uplink multi carrier NOMA is developed using graph theory with complexity \(O(KN^{3})\). The shortest processing time (SPT) strategy is presented in [37] for uplink NOMA with complexity \(O\big{(}N^{2}\big{)}\). Thus, the proposed algorithm has a worthy complexity improvement.