# Spectrum Awareness for Cognitive Radios Supported by Radio Environment Maps: Zonal Approach +
## 4 Test Scenario and Exemplary Maps
The system was composed of its own source of radio emission working with the output power lower than that used by DVB-T transmitters, which are quite often used as a source of radio signal in these types of research works, and the mobile receiving node [44]. The receiving part of the testbed consisted of an antenna, a vector analyzer, and a computer controlling the receiving operation and recording the results. The receiving antenna was installed on a vehicle at the height of 3 m. The vehicle was moving within a preliminarily selected area presented in Figure 2 and took measurements in specific points (P1-P39). The transmitting part of the testbed was composed of a signal generator, an amplifier, an antenna, and a control computer. The antenna was mounted on the roof of a two-story building at the height of approximately 8 m. The detailed configuration of the measurement system is given in Appendix B.  
Figure 2: Deployment of the sensors and position of the TX Antenna. The following configuration of the testbed was used: (a) Frequency: 1997 MHz; (b) modulation type: CW; (c) output power: 10 W; (d) measured parameter: average RSS; (e) number of averages: 10; (f) antenna type: Omnidirectional. The measurements were taken in Central Europe (Poland) in the area of Zegrze Lake presented in Figure 2 (approx. 4 km\({}^{2}\)). The test area was diverse in terms of coverage, i.e., partly an open meadow neighboring a forest and partly an urbanized area with buildings of medium height. The measurement results obtained for all 39 sensors are presented in Appendix A. Some exemplary maps constructed with the selected interpolation techniques are presented in Figure 3.  
The characteristic feature of the NN method (Figure 3a) are polygons created around each sensor. Within each polygon, the signal level takes the value measured by the sensor. For this reason, the signal level may change suddenly at the edges of the polygons, e.g., between the orange polygon in the center and the dark blue polygon at the top of the map (Figure 3a). It is worth noting that the size and the shape of the polygons depend on the density and deployment of neighboring sensors. The position of the TX antenna can be determined with little accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Scenario 13**} & \multicolumn{3}{c}{**Scenario 20**} & \multicolumn{3}{c}{**Scenario 26**} \\ \hline _Test\_13a_ & _Test\_13b_ & _Test\_13c_ & _Test\_20a_ & _Test\_20b_ & _Test\_20c_ & _Test\_26a_ & _Test\_26b_ & _Test\_26c_ \\ \hline P2 & P1 & P1 & P2 & P1 & P3 & P5 & P2 & P1  
The map generated with the application of the IDW method is shown in Figure 3b. This method generates smoother maps when compared to NN. However, the drawback of the method is the bull's-eye effect visible around each sensor. The size of eyes depends on the power \(p\) used in the interpolation process. The estimation of the signal level is quite accurate if the power \(p\) is set at 3 or higher and the sensors are deployed densely [25]. The location of the transmitter can be determined with moderate precision. When the Kriging method is applied (Figure 3c), the signal level changes smoothly within the whole area. Neither bull's-eye effects nor rapid changes in the signal value are observed even if the sensors are deployed sparsely or irregularly. For this reason, Kriging seems to be the method which is least sensitive to the deployment of the sensors [25]. The source of emission can be located with quite good accuracy. The red-orange zone in the center of the map has quite a regular shape of a circle. The interpolation methods described above can be used to determine the likely location of the bigger number of transmitters working at the same time. Exemplary maps and analysis for the scenario with two transmitters are shown in [44].