# Spectrum Awareness for Cognitive Radios Supported by Radio Environment Maps: Zonal Approach +
## 6 Conclusions  
The quality of radio environment maps depends on several factors, among others, on the regularity of deployment of sensors, the number of sensors, the propagation environment, and the interpolation technique. In general, an increased number of sensors in the network is beneficial since the RMSE drops significantly. If the number of sensors in the network is limited, more attention should be paid to the optimum deployment of sensors. In the literature on the topic, several methods are presented, although the most promising one seems to be the deployment algorithm based on the stratified approach, which assumes that in some zones the sensor network is more densely covered with sensors than in others. Such deployment of sensors seems to be reasonable in diverse areas, like the one presented in this paper, where RMSE values for the scenario with 20 sensors were much lower than for the scenario with 26 sensors. It should be emphasized that, independently of the scenario, IDW p1 and NN interpolation techniques offered the highest RMSE, which also means the worst REM quality. The highest values of RMSE observed for NN and IDW p1 resulted from high uncertainty in estimating the signal level, even above 20 dB, which is a significant difference when compared to the other interpolation techniques. This effect results from a sudden and deep change in the interpolated signal level between neighboring areas. Consequently, even if the number of miscalculated estimations is low, the range of errors of estimation makes the value of RMSE high. For this reason, these methods should not be recommended for REM construction. It is worth noting that for the lowest RMSE case, irrespectively of the scenario, the RMSE for Kriging oscillated at a low level of around 6 dB. IDW p3 and IDW p4 methods offer RMSE at levels that are comparable with RMSE for Kriging, particularly for tests with 20 measurement sensors and more. In our previous research work, these interpolation techniques were assessed as promising for REM application. On the other hand, the main conclusion from the zone analysis is that Kriging outperforms the other interpolation techniques. Even if in some cases IDW method was better than Kriging, differences between RMSE levels were negligible. We also noticed that for all the interpolation techniques, the quality of estimation was lower in the area close to the transmitter than in other zones. This results from the fact that interpolation methods are not able to extrapolate the signal level, i.e., to estimate well the signal level in the area close to the transmitter if all measurement results are provided by distant sensors only. Another conclusion is that for areas far away from the transmitter, all the analyzed interpolation methods overestimated the signal level. Therefore, such areas may be interpreted as FAZs. One of the possible solutions to this problem is the assessment of the coefficient by which the signal level is overestimated and recalculation of the estimated level for distant areas to get the level closer to the real value. The analysis of the differences between interpolated and measured levels shows that the number of CEPs is higher for Kriging than for the other interpolation methods. Furthermore, it is for Kriging interpolation method only that an increase in the number of measurement sensors causes the percentage of correct estimations to grow, which in turn leads to the minimalization of the risk of the FAZ and the MDZ. It must be emphasized that, contrary to the statement based on the RMSE analysis only, IDW method is less accurate than Kriging. In our previous analyses, IDW p3 and IDW p4 methods were assessed as promising and comparable with Kriging in some cases. The zonal approach and analysis of CEPs revealed that Kriging outperforms the IDW method. Nevertheless, Kriging is much more complex than the IDW method and requires much higher computational power. For this reason, in some applications IDW p3 or IDW p4 may be preferable and sufficiently accurate.