# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
## 6 Simulation Results  
In this section, simulation is introduced to evaluate the proposed algorithm performance. Consider a single UAV covering a cell of radius \(R_{c}=1\) km; assuming a suburban environment. An aerial UAV is centered and placed at the optimum height of the cell according to the values in Table 1. Consider IoT devices (N = 300) randomly deployed in the cell, IoT devices are supporting different applications, assuming there are 4 groups of IoT devices each has its unique traffic parameters. The maximum transmit power of each IoT device is 27 dBm and the minimum transmit power is 20 dBm. Unless stated, it is assumed that the system total available bandwidth B = 1.4 MHz consisting of 6 RBs. The noise power density \(\sigma^{2}\ =-174\ dBm/Hz\). The main simulation parameters are summarized in Table 2.  
To verify the efficiency of the proposed scheduler; the proposed algorithm is compared with the maximum CQI scheduling algorithm. According to [35], the best CQI algorithm shows its superiority in the achieved sum rate and spectral efficiency. The idea of the best CQI is based on scheduling the \(N\_sel\) nodes which have the maximum CQI regardless of any other parameters. Figure 5 shows the percentage of IoT devices that exceeds the delay limit, and as clearly seen the proposed algorithm outperforms the maximum CQI and the gap increases with the time, until it reaches a certain point at which the percentage of devices exceeding the delay limit saturates in both algorithms. This is due to its receiving large requests and scheduling _N_sel_ nodes only in each TTI while rejecting the rest of nodes. The percentage of devices exceeding the delay tolerance saturates in the proposed algorithm to nearly 30% but around 44% in maximum CQI. The proposed algorithm gives priority to the nodes of the maximum buffer and least delay tolerance, resulting in 31.8% delay improvement. To evaluate the fairness of the proposed algorithm; the formula of [PERSON]'s fairness index in [PERSON] _et al._ (2018) is used, which is given by  
\[\textit{[PERSON]'s fairness index}=\frac{\left(\sum_{k=1}^{K}R_{k}\right)^{2}}{K \sum_{k=1}^{K}R_{k}^{2}} \tag{44}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline a & Environment Constant & 4.88 \\ b & Environment Constant & 0.43 \\ \(\eta_{LOS}\) & Line of sight Environment Constant & 0.1 \\ \(\eta_{NLOS}\) & Non-Line of sight Environment Constant & 21 \\ \(\theta_{optimum}\) & Optimal elevation angle & 20.34\({}^{\circ}\) \\ \(\sigma^{2}\) & Noise power density & \(-\)174 \\ U & Number of UAVs & 1 \\ \(P_{min}\) & IoT device minimum power & 100 mW–20 dBm \\ \(P_{max}\) & IoT device maximum power & 500 mW–27 dBm \\ \(R_{c}\) & Radius of the cell & 1 km \\ TTI & Time slot & 1 ms \\ Simulation time & 1 s \\ \(D_{limit}\) & Maximum delay limit & [10, 20, 30, 40] ms \\ \(\lambda\) & Arrival rate per group & [100, 250, 600, 400] (packets/s) \\ N & Total number of devices & 300 \\ \(D\) & Packet size & 100 bits \\ \(m\) & Channel blocklength & 100 symbols \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 5: Percentage of devices exceeding the application delay limit at each time slot. where \(R_{k}\) represents the rate of user \(k\) and \(K\) is the total number of users. The fairness index values are confined between 0 and 1 such that the maximum value is achieved when the users have equal data rates. As obviously seen in Figure 6, the proposed algorithm is significantly fairer than the best CQI, since scheduling is performed sequentially from all groups, in addition to the fair power allocation algorithm used unlike the best CQI; resulting in 50.8% fairness enhancement at 300 users. In Figure 7, the relationship between the transmitted power of the IoT devices and the sum rate is shown. As expected, the sum rate of the maximum CQI is greater than the proposed algorithm by only 1.6%; therefore, they still have a very close performance.