## 6 Results and Discussion  
In this section, the performance measures of the proposed system are studied and explained. We compare the proposed system with other systems, with and without queuing. Following that, we simulate examples of the three methodologies previously mentioned with Matlab and Simulink using Markov chain methods. For both eMBB and mMTC requests, performance is analyzed in terms of their service completion rate (SCR). Other parameters of importance, such as the average delay of both eMBB and mMTC requests and the forced probability, are discussed and analyzed. We assume a single C-RAN with 25 available BUU resources (i.e., C = 25). The arrival rate of all the requests to the C-RAN is classified into three priority-based 5G use cases, including uRLLC, eMBB, and mMTC, respectively. We assume that, unless otherwise specified, uRLLC, eMBB, and mMTC have the following arrival rates: \(\lambda_{1}=1\), \(\lambda_{2}=1\), \(\lambda_{3}=2\), respectively. Their service rates are assumed to be \(\mu_{1}=0.5\), \(\mu_{2}=1.0\), and \(\mu_{3}=1.0\) for uRLLC and eMBB, respectively. Additionally, we assume that the sizes of 1 QeMBB and 2 QmMTC are 4 (M = 4) and 2 (N = 2), respectively. First, the analytical and simulation results are compared and validated using the above parameters. In Figure 4, we validated the results of the system using Methodology III. As it is depicted in the figure, the behaviors of both analytical and simulation results for the eMBB and mMTC's SCR as a function of the uRLLC arrival rate are plotted, and the results are a good match. Furthermore, we can see from Figure 4 that the analytical and simulated SCR results are closer for eMBB than for mMTC when the arrival rate of uRLLC is low. The reason for SCR variation is that, in the 2 QmMTC, there are two queuing processes during the arrival and the interruption rate, and the interruption rate is uncertain. In the case of the 1 QeMBB queue, the arrival rate is Poisson. Thus, the proposed scheme shows acceptable matching between both simulation and analytical results. Using all three methodologies, the combined resource utilization of both eMBB and mMTC services has been explored. The service rate of mMTC is variable, while it is fixed for uRLLC and eMBB. As depicted in Figure 5, the resource utilization of both eMBB and mMTC services decreases as the service rate of mMTC increases. This is because when the service rate of mMTC increases rapidly in a short time, more in-service mMTC requests complete their service more rapidly. In this case, the probability of keeping the resources busy with the mMTC requests decreases. Moreover, since the arrival rates of uRLLC and eMBB requests are fixed in this case while the service rate of mMTC increases, the probability of a new arrival requesting an idle resource in the other two cases decreases. Therefore, we can see that the resources in the C-RAN will stay idle for a relatively longer time. This makes sense when the resources are not greatly utilized in the system. Consequently, increasing the service time of one type while fixing it for others results in a reduction in resource utilization. Therefore, using the queuing concept allows the arrived eMBB and mMTC requests to be queued and the interrupted mMTC request to be queued again (as employed in Methodologies I and II). This process increases resource utilization as more chances are offered to the queued requests (including the interrupted mMTC requests) to reattempt the service again in the C-RAN. Figure 6 shows the SCR of mMTC service versus its arrival rate using all three methodologies. The arrival rates of both uRLLC and eMBB are set to a fixed value. As can be  
Figure 4: Analytical and simulated results for eMBB and mMTC SCR as a function of uRLLC arrival rate—Methodology III. Figure 5: eMBB and mMTC resource utilization as a function of mMTC service rate for different methodologies. seen from the figure, the SCR of mMTC increases rapidly, then tends to move smoothly to a peak value in the sequence.