# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 6 Simulations and Result Discussions
### CHANAL Scenarios, Results, and Discussions
#### 6.3.2 CHANAL Disaster Scenario  
In emergency situations, we assume receiving storms that are identical to past storms but gradually falling into the range of \(\lambda_{\text{(m)}}\in\{5,15,30,50,75\}\). We also estimate that we will receive a double constant H2H average arrival rate, \(\lambda_{\text{(h)}}=50\), and we will keep the bandwidth flexible until it absorbs all prior storms.  
The results shown in Table 6 reveal that by adapting the number of resources from \(\text{rb}_{\text{(m)}}=6\), typically used in a regular LTE-M network, to \(\text{rb}_{\text{(m)}}=72\)) promoted in CHANAL, we can eliminate all M2M storms with a completion rate of 96-100%.  
Meanwhile, H2H traffic does not reveal any degradation from E1-E4 with a constant (\(\text{scr}_{\text{(h)}}=100\%\)) due to having enough resources reserved for H2H traffic.  
For H2H traffic, there is just one issue in the \"worst-case scenario\" when the completion rate decreases to 56% (\(\text{scr}_{\text{(h)}}=56\%\)), since the system reserves 72 resource blocks for M2M traffic, which satisfies 96% of M2M requests.  
However, this flaw might be considered acceptable since the worst-case scenario with an accumulative storm happens rarely, and when it occurs, it lasts for a short period of time. Actually, if a disaster case lasts for a long period of time, many operators usually take the decision to ban some H2H traffic (e.g., watching a movie online) in order to allow emergency traffic to be exchanged easily. Therefore, in this case, our solution could be a good replacement for the total elimination of the H2H traffic strategy, as it keeps H2H traffic working with a 56% completion rate while supporting 96% of M2M requests. Additionally, M2M payloads are sent in the form of a small chunk of data in burst forms. Based on the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group \#** & \(\lambda_{\text{(m)}}\) & \(\text{rb}_{\text{(m)}}\) & \(\text{rb}_{\text{(h)}}\) & \(\text{scr}_{\text{(h)}}\) & \(\text{scr}_{\text{(m)}}\) \\ \hline emergency 1 & 5 & 6 & 94 & 100 & 100 \\ emergency 2 & 15 & 18 & 82 & 100 & 100 \\ emergency 3 & 30 & 30 & 70 & 100 & 100 \\ emergency 4 & 50 & 48 & 52 & 100 & 96 \\ worst-case & 75 & 72 & 28 & 56 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The adaptivity of CHANAL in emergency cases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group No.** & \(\lambda_{\text{(m)}}\) & \(\text{rb}_{\text{(m)}}\) & \(\text{rb}_{\text{(h)}}\) & \(\text{scr}_{\text{(h)}}\) & \(\text{scr}_{\text{(m)}}\) \\ \hline
1 & 5 & 6 & 94 & 100 & 100 \\
2 & 10 & 12 & 88 & 100 & 100 \\
3 & 15 & 18 & 82 & 100 & 100 \\
4 & 20 & 24 & 76 & 100 & 100 \\
5 & 25 & 30 & 70 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The adaptivity of CHANAL in normal cases.  
emergency and traffic priority strategy, H2H traffic (e.g., video streaming, VoIP, etc.) is not as urgent as M2M traffic (e.g., health care, fire alarms, flooding sensors, etc.), which can be sent in a split second compared to the massive H2H traffic size.