# Spectrum Decision-Making in Collaborative Cognitive Radio Networks
## 2 Materials and Methods
### Input Variables  
To carry out the analysis, real power measurements in the GSM frequency band are used as input variables (824-874 MHz). Figure 1 presents the configuration of the equipment used in the measuring process. The detection technique used was energy detection due to its feasibility of implementation. The measurement ranges of the bands were based on the sweep time, the resolution of bandwidth, and the Span.  
Figure 1: Experimental configuration for measuring spectral occupancy.  
The Span value corresponds to the analyzed frequency range. For this case of 50 MHz (824-874 MHz), the points per Span determine the number of frequency channels (uniform division of a spectrum portion) for which the spectrum analyzer measures the power level during each scan. In accordance with the above, in each sweep, the spectrum analyzer delivers the information of the power value measured in dBm, corresponding to 500 frequency channels with a bandwidth of 100 kHz (50 MHz/500) each.  
The sweep time was adjusted to 290 ms for a one-month measurement period. Approximately 3448 sweeps were obtained in one second. Therefore, the total number of measured power data was 4,468,608,000. A power matrix of 8,937,216 \(\times\) 500 was built from the measured information, which contains the power value of each of the 500 channels for 8,937,216 time instants (each time instant is equivalent to 290 ms). The corresponding amount of information is shown in Table 1. The rows represent time in seconds and the columns represent the frequency channels.  
The measurements require pre-processing to characterize the traffic level as high (few spectral opportunities) or low (many spectral opportunities). The characterization is carried out according to the availability probability (AP) criterion (as shown in Figure 2), the measured power matrix is taken, and the availability matrix is obtained through the Threshold level (-95 dBm); this level is selected regarding the balance search between the detection probability and the false alarm probability [26; 27]. The 40% AP is selected for high traffic and the 80% AP is selected for low traffic.  
Additionally, the type of model to be implemented makes it necessary to identify a data group that can train the models and another data group for validation. The matrix chosen for training allows setting the initial parameters of the algorithms, which are then used for collaborative analysis. The assessment matrix is used to obtain the results of the assessment metrics of the implemented algorithms.  
Regarding the previous statements, two databases are used--one for training and the other for assessment. Each database is classified into two traffic levels: high and low. In total, there are 500 frequency channels for 1 h of training and 10 min for assessment. The size and classification are described in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency Band** & \multicolumn{3}{c}{**Quantity of Data Captured**} \\ \hline \multirow{2}{*}{GSM} & Rows & Columns & Total Data \\  & 8,937,216 & 500 & 4,468,608,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Captured data.  
Figure 2: Experimental configuration for measuring spectral occupancy.