# Spectrum Awareness for Cognitive Radios Supported by Radio Environment Maps: Zonal Approach +
## 5 Analysis of the Results
In consequence, the estimated signal level for P17 is much higher than the measured level. The results presented in Table 3 reveal that Zone 3 was dominated by OEPs and CEPs, while OEPs prevailed in Zone 4. It is worth mentioning that there were no such control points for which all the interpolation methods would give the correct signal level. The results for the scenario with 19 control points (20 sensors used for the interpolation process) are given in Table 4. The biggest number of CEPs occurred for NN (10). For Kriging and IDW p6, there were 8 CEPs. It is worth noting that in this case there was only one control point (P3) for which all the methods underestimated the signal level, and two control points (P5, P23) with the correct estimation for all the methods. The number of green fields in the table is much higher than in Table 3. The explanation of this effect is very simple: Fewer control points, means more points used for the interpolation process and as a result, an increase in accuracy, see results in Table 3.  
The comparison of the results presented in Table 4 showed that there were three control points (P15, P30, P31) for which all the methods overestimated the signal level. There were two control points (P6 and P21) for which only NN gave the correct signal value, while for the remaining interpolation techniques the signal level was overestimated. Additionally, there were two control points for which one method only gave the correct signal value, namely IDW p1 for P18 and IDW p4 for P34, while for the remaining interpolation techniques, the signal level was underestimated. The analysis of the results showed that Zone 1 was very diverse and there were 29 CEPs, 24 UEPs, and 11 OEPs. Consequently, it is hard to determine which trend dominates. In contrast, Zone 2 was dominated by OEPs, however the results for P18 were significantly different (depending on the interpolation technique there are 7 UEPs and 1 CEP). The results shown in Table 4 revealed that in Zone 3 and Zone 4 CEPs and OEPs prevailed. The results for the scenario with 13 control points (26 points used for the interpolation) are given in Table 5. It can be easily noticed that the number of CEPs was the highest for Kriging (8) and IDW p1 (6) while for the remaining interpolation methods the number of CEPs did not exceed 4. The comparison showed that there was only one control point (P37) for which all the methods underestimated the signal level. There were two control points for which one method only gave the correct signal value, namely Kriging for P13 and IDW p6 for P15, while for the remaining interpolation techniques the signal level was overestimated. Additionally, there were two control points for which one method only gave the correct signal value, namely IDW p1 for P18 and NN for P33, while for the remaining interpolation techniques the signal level was underestimated. It is worth mentioning that there were no such control points for which all the interpolation methods would give the correct or overestimated signal level. The analysis of the results for the scenario with 13 control sensors confirmed that Zone 1 is very diverse. There were 17 OEPs, 16 UEPs, and 15 CEPs. Zone 2 is also varied as there were 6 OEPs, 9 UEPs, and 9 CEPs. In Zone 3 CEPs (11) prevailed, while Zone 4 was dominated by OEPs (7). A general conclusion from the analysis above was that for areas distant from the transmitter, the effect of overestimation occurred, while for areas very close to the transmitter, the correctness of estimation was hard to define as there were no dominating trends. The analysis of the differences between interpolated and measured values for Zone 4 showed that there was the effect of constant overestimation for IDW method, except for IDW p6 for the scenario with 20 sensors, when the signal level was correctly estimated. The scale of the overestimation depended on the number of measurement sensors.