## 5 Evaluation and Results  
In this section, numerical results are presented from the simulations by evaluating and comparing the performance of the presented scheduling techniques. The parameter for the cell with a radius of transmission \(r\) is taken as 50 m, with \(Y_{n}\) taken as 30, which is the UEs randomly distributed inside the cell and sub-channels \(Y_{s}\) of 12, with a \(BW\) of 3 MHz. The cell also contains \(Y_{b}=8\) one-dimensional antenna arrays in the sense that each of the arrays comprises \(Y_{a}=32\) antennas. For the DL transmission with beamforming, the number of static beams \(Y_{b}\) is set at 8 for each of the antenna arrays, with 0.15 m as the distance between \(Y_{a}\) and a departure angle of \(\frac{r}{12}\). In simulating the channel coefficient for each user, the Winner II+ channel model is employed. The power spectral density of the additive white Gaussian noise is taken as \(-170\) dBm/Hz. Algorithm 4 is employed for the RB allocation process, in which four sub-channels are allocated to each transmission of packet. In total, 2500 packets were run with their times of arrival distributed exponentially and their packet destinations distributed uniformly over a finite number of UEs. Since in [25], the EDF algorithm has been proven not to be an ideal technique in scheduling; we then compare the performances of the improved EDF (I-EDF) algorithm, which was upgraded earlier, and proposed the DSC algorithm [32] as well as our proposed OUD algorithm. In Figure 5, the number of packets that were received over the rate of arrival is presented, while the total reward collection for the various techniques is presented in Figure 6. In both figures, it is observed that the techniques behave differently in terms of employing their respective scheduling decisions. There are more packets received by the OUD technique when compared to the DSC and the improved EDF algorithms, and this is attributed to the fact that the task parameters are already known upon arrival and jobs are selected based on the highest output ratio. It is also seen that the total reward collected is considerably higher for the OUD technique than the other two techniques. Figure 7 displays the average throughput for different amounts of users in the system. By throughput, it simply means the average rate of successful packet delivery over a dedicated communication channel. It is noticed from the figure that the average throughput is almost the same for all the techniques when the number of UEs is lower than 30. However, the OUD technique indicates an improved performance compared to the other two techniques when the number of UEs exceeds a certain range of 30. This is because there are more data packets transmitted utilizing this technique since it has already picked priority users based on the algorithm. The OUD technique also proved superior since tasks are being selected using the shortest time to expiry approach. Figure 5: Total number of received packets. Figure 6: Total number of rewards collected. To assess the performance of the schedulability of the techniques, we verify if some of the jobs miss their deadlines during execution. Hence, the schedulability of the technique is measured by the success ratio, which is taken as  
\[\text{Success ratio}=\frac{\text{number of tasks with no missed deadlines}}{\text{number of generated task}}\]  
In Figure 8, the results for the success ratio for the various techniques are shown. From the figure, all three scheduling approaches can successfully manage up to 30 tasks sets before their performance begins to plummet as deadline misses start to appear for increasing task sets. With the drop in the success ratio, the OUD technique still manages to outperform the other two techniques due to the fact that handling the scheduling overheads becomes a bottleneck for the I-EDF and DSC techniques. In other words, an additional set of tasks that are not readily deemed schedulable by I-EDF and DSC are gradually schedulable by OUD.