## 3 Cognitive Radio Engine Platform Usage Scenario
### Available Channel Extraction Method via Conducting the Reasoning Engine  
#### 3.4.1 Reasoning from the PU Traffic Reference Model  
In this paper, we propose a novel channel selection method in which the CR node selects a suitable unoccupied channel, and the channel with the lowest probability of being occupied by the primary user among vacant wireless channels is compared with the previous occupation activity. The CR engine is designed to select the proper candidate channel that is inducing the low level of the collision probability. We also evaluated the performance of the CR engine by comparing the occupancy patterns in a primary user traffic model generated by the mathematical modeling performed by the cognitive engine. In addition, appropriate reasoning and learning parameters, such as the reasoning cycle, sample number, sampling interval, and so on, for satisfying the collision probability requirements of the system were calculated. Clearly, since the collision probability could not be directly controlled, the proposed method employs the case-based reasoning, which is an indirect way of retrieving appropriate parameters associated with the reference model of PU traffic inducing a low level of collision probability. Here, the reference model plays a major role attributed to giving the CR node a versatile opportunity of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sampling Methods** & \multicolumn{2}{c}{**Main Features**} \\ \hline \multirow{4}{*}{Uniform Sampling} & \(\bullet\) (CB) Divides the time slots to be sampled at a fixed time slot in the sampling interval \(\bullet\) (RB) Divides the time slots to be sampled at a random time slot in the sampling interval slots of random size and position \\ \hline \multirow{4}{*}{Weighted Sampling} & \(\bullet\) (WCB) Assigns exponentially-decaying weights from the present to the past unit time slots sampled by CB. \(\bullet\) (WRB) Assigns exponentially-decaying weights from the present to the past unit time slots sampled by RB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main features of uniform sampling and weighted sampling methods. selecting relevant parameters. The case-based reasoning is triggered by specifying the feature of the current PU traffic pattern via conducting the shape matching process as a way of comparing the feature relevant to the newly-acquired histogram reflecting the current PU traffic with that stored in the reference model in a clustered and sorted manner. For the next step, the simple extraction process for searching parameters in the reference model is executed. For the purpose of achieving a high degree of precision on the selection of parameters, quite a few arbitrary PU traffic generations should be conducted subject to ensuring the collision probability is as low as possible. The proposed reasoning method could not perfectly protect the PU from the interference induced by the SU because the candidate channels are only parameters exploited from referring to the PU traffic model. Here, the reference model is constructed by conducting an enormous amount of PU traffic generations followed by executing the deliberate clusterization steps to execute the PU traffic matching. This whole process can be regarded as the learning engine, which can provide adequate information about the reasoning period, the sampling period, and the number of samples for precise traffic matching together with the available channel after the sorting and ranking process proposed in this paper. It is valuable to emphasize that these relevant parameters are exploited by conducting an exhaustive search in the direction of minimizing the collision probability. Assuming that there is no prior knowledge about the channel state and PU activity in this paper, at the instant of starting a new reasoning period, the SU only can transmit the signal over one of the channels suggested by conducting the reasoning process.