# Spectrum Decision-Making in Collaborative Cognitive Radio Networks
## 2 Materials and Methods
### Collaborative Model
#### 2.2.4 Operation of the Collaborative Model  
The previous section describes the functions of the collaborative model. In this section, we present the specific description of the collaborative algorithm. The input and output diagram of the collaborative model is presented in Figure 4. It has three input parameters and three output variables. The logic of the algorithm is based on taking the training matrix (input data) and segmenting it according to the User Relations and Number of Users inputs. The outputs of the model are the segmented power matrix for training, the total number of users in which the matrix was sectioned (User Full), and the users that will participate in the training (User Simulation). \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline Power Segmentation Training & The power matrix segmented according to the number of users \\  & participating in the training \\ User Full & Total number of users used to divide the power matrix \\ User Simulation & Number of users participating in the training \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Output variables. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Element** & **Description** \\ \hline \multirow{3}{*}{Segmentation} & Random Zone & The percentage of users selected for simulation is chosen at random. \\ \cline{2-3}  & Continuous Zone & The percentage of users selected for simulation is taken in \\  & & an orderly manner, row-wise or column-wise. \\ \hline \multirow{6}{*}{Division} & & If the number of users is greater than 10, the rows of the power \\  & & matrix are divided into 10 equal parts, and the columns are split into \\  & & \(n\) parts until the number of users is completed. \\  & & If the number of users is less than 10, the rows of the power matrix \\  & & are divided into 2 equal parts, and the columns are split into \(n\) parts until the number of users is completed. \\ \hline User Percentage & 10–100 & Percentage of users participating in the training. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: User relation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Element** & **Description** \\ \hline Number of Users & 1–1000 Users & Number of users used to divide the power matrix for the training \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of users. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Element** & **Description** \\ \hline Number of Users & 1–1000 Users & Number of users used to divide the power matrix for the training \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Power matrix. A particular description of the operation of the model is presented in Figure 5. As input matrix (Input Data), information is collected from the frequency bands; the black blocks represent available frequencies, and the white blocks represent unavailable frequencies. It begins by dividing the matrix into submatrices (Number of Users) to characterize users. Each submatrix corresponds to the information of a user. Therefore, the number of submatrices corresponds to the number of users adjusted to the model input. For the model proposed in Figure 5, the number of users adjusted corresponds to four (Number of Users = 4). After the submatrix separation process, the amount of information to be shared in the training phase is determined (Segmentation Training) according to the collaboration level and the number of users or submatrices. According to Figure 5, a collaboration level of 25% (User Relation = 25%) corresponds to a training process based on the information of a single user. A collaboration level of 75% (User Relation = 75%) corresponds to a training process based on the information of three users. If the level reaches 100% (User relation = 100%), then the training process uses all the information available. For better understanding of the strategy, two examples are shown.