2.2.2. Availability of the radio environment

The spectral availability information is required to implement the decision-making process through collaborative information exchange and multi-user access. A decision threshold is implemented to obtain channel availability information; there is no single criterion for the decision threshold selection. One of the biggest challenges to implement an energy detector is to choose the threshold (Lipski et al., 2021). A constant threshold is used in most conventional methods to detect the presence or absence of a PU signal. This PU signal can be determined from different strategies, such as the trade-off between detection probability and false alarm probability, binary assumptions using Gaussian distributions for Noise Floor signal, desired detection probability, mean and standard deviation of the whole received signal (Verma, 2020). A threshold level of -95 dBm is used as a criterion selected regarding the balance search between the detection probability and the false alarm probability (Digham et al., 2007; Lehtomaki et al., 2005; Pedraza, 2016). Channels with lower powers than the decision threshold value are classified as available represented in the availability matrix as a logical one (1). In the opposite case, channels with higher powers than the decision threshold value are classified as occupied represented in the availability matrix as a logical zero (0) (Pedraza et al., 2016). From the spectral availability matrix and to extract relevant information for the proposed applied strategy, two processes are performed; the first one determines the traffic level. The second one generates the matrices for the training and validation of the decision-making techniques.

To characterize the traffic level is used the availability probability (AP), a parameter obtained by calculating the average of each of the columns of the availability matrix. A high traffic level indicates a low number of spectral opportunities, and a low traffic level indicates a high number of spectral conveniences. As part of the design criteria, an 80 % AP was selected for low traffic and a 20 % AP for long traffic.

Due to the type of decision-making technique to be implemented and based on the cross-validation methodology, it is required to identify a data set with a matrix structure for training and validation. The training matrix, which allows configuring the initial parameters of the algorithms, is the one used for the collaborative analysis, contains the spectral occupancy information of an hour. The evaluation matrix, used to obtain the results of the evaluation metrics of the implemented algorithms, contains the spectral occupancy information of nine minutes. As a design criterion, it was selected a cross-validation ratio of 70-30. 70 % of the data is used for training, and 30 % of the info is used for validation.

Figure 3 represents the second stage of the spectral information module as previously described. In this stage, the availability matrix is obtained through the threshold level. The size of the availability matrix is equivalent to that of the power matrix, 500 columns and 8,937,216 rows, where the columns represent the frequencies or channels and the rows represent the time in seconds. Additionally, the availability matrix is characterized according to the traffic level. Also, it generates the data for training and validation.

Activities for the availability of the radio environment.