## 4 Simulation  
### Simulation Scenario  
Figure 8 shows a simulation scenario for evaluating the performance of the proposed cognitive engine platform. In Figure 8, there is one PU radio required to be protected from the interference  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & **CB** & & **RB** & **WCB** & & **WRB** & **Rank-Sum** & & **Prob-Sum** \\ \hline
**Rank** & **Ch.** & **Prob.** & **Ch.** & **Prob.** & **Ch.** & **Prob.** & **Ch.** & **Prob.** & **Ch.** & **Sum** & **Ch.** & **Sum** \\ \hline
1 & Ch. 2 & 0.58\% & Ch. 1 & 5.43\% & Ch. 2 & 5.54\% & Ch. 2 & 3.26\% & Ch. 2 & 6 & Ch. 2 & 28.23\% \\
2 & Ch. 1 & 10.61\% & Ch. 3 & 16.48\% & Ch. 3 & 5.98\% & Ch. 1 & 8.31\% & Ch. 3 & 9 & Ch. 1 & 45.64\% \\
3 & Ch. 3 & 11.84\% & Ch. 2 & 18.85\% & Ch. 4 & 11.27\% & Ch. 3 & 15.08\% & Ch. 1 & 11 & Ch. 3 & 48.15\% \\
4 & Ch. 4 & 12.95\% & Ch. 4 & 20.36\% & Ch. 6 & 15.33\% & Ch. 6 & 20.58\% & Ch. 4 & 16 & Ch. 4 & 66.44\% \\
5 & Ch. 5 & 19.06\% & Ch. 5 & 22.12\% & Ch. 1 & 20.06\% & Ch. 4 & 21.86\% & Ch. 6 & 20 & Ch. 5 & 85.81\% \\
6 & Ch. 6 & 31.95\% & Ch. 6 & 24.72\% & Ch. 5 & 25.14\% & Ch. 5 & 22.19\% & Ch. 5 & 22 & Ch. 6 & 92.58\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated occupancy probability and channel ranking.  
induced by neighboring cognitive radio nodes. In addition, four cognitive radio nodes are deployed in the simulation environment: one plays the role of master, and the others operate in slave mode. The slave-type TCRNs perform spectrum sensing functionality to observe PU activity periodically, which reads the channel occupancy state. Furthermore, every slave-type node transports the acquired sensing information reflecting the channel occupancy state to the master TCRN. Then, the master TCRN makes a decision as to which channels are appropriate so that cognitive radios equipped with the cognitive engine use those channels in a secure manner, without interfering with the PU.  
In the simulation, the PU channel occupancy characteristic acknowledged by the master TCRN with the help of spectrum sensing technology was artificially generated so as to produce a PU traffic model following a specific statistical distribution. Figure 9 shows a diagram of the simulation process.  
As shown in Figure 9, the PU traffic model can be characterized based on inspecting a probability distribution having the form of an exponential distribution. In other words, the temporal duration of the ON and OFF states statistically follows an exponential distribution. The learning engine uses the PU traffic model generated in the previous reasoning period to quantify the PU channel occupancy probabilities that correspond to the overall channels based on CB, RB, WCB, and WRB. Finally, the reasoning engine, assisted by the rank-sum and prob-sum methods, gives rise to available candidate channels for the usage of the cognitive radio node. After performing a series of assignments,  
Figure 8: Simulation scenario environment. TCRN, tactical cognitive radio node.  
Figure 9: Simulation analysis flowchart.  
the probability associated with the a priori PU traffic prescribed for the simulation is calculated in order to verify the efficacy of the proposed cognitive engine.  
To better describe this process, Figure 10 depicts an example of the consecutive operation of the CR engine. As a result, the candidate channels can be exploited at every reasoning period. The learning engine newly updates the occupancy probabilities obtained by each sampling method and replaces those with the previous ones at the end of each reasoning period if needed. The reasoning engine exploits the available candidate channels for a given TCRN. This process is repeated periodically, and the suitable reasoning period and the number of samples can be achieved as byproducts by matching the recently-acquired traffic pattern with the preceding traffic model, resulting in a similar distribution to the PU occupancy probability.