# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation
### Results for the Rome Dataset  
The analysis carried out on the Rome dataset focused on the impact of the data loss observed during the data processing phase. Data were analyzed per operator as conducted for the Oslo dataset in order to identify the operator to be considered in the analysis. The results are presented in Table 6 and show that the three operators are characterized by similar features; Operator 88, characterized by the highest number of unique NPCIs, was selected for an exhaustive analysis of all combinations of strategies and RF parameters using both the original dataset and the one obtained by linear interpolation, following the approach introduced in Section 4.4. The results are presented in Table 7 and highlight the marked impact of the data loss on positioning accuracy: all combinations of strategies and RF parameters are in fact characterized by a poor performance using the original dataset as compared to the one observed using the Oslo dataset. The linear interpolation dramatically improves positioning accuracy for all strategies with the exception of the _net_ one; this is reasonable, since the _Net_ strategy does not use RF parameters but only relies on the presence/absence of a given NPCI in a data point. Since the proposed interpolation technique only fills in gaps for RF parameters in existing NPCIs, its application does not affect the information used by the _net_ strategy. The results also show that even when using interpolation, the positioning error in the Rome dataset for Operator 88 is larger than that in the Oslo dataset for Operator 1. This gap might, however, be due to the different characteristics of the data collected for the two operators in terms of the number of unique NPCIs and the average number of NPCIs per valid point, with a marked advantage for Operator 1 in Oslo as evident from the comparison of Tables 4 and 6; further insight on this aspect is provided in Section 5.4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Operator 1** & **Operator 10** & **Operator 88** \\ \hline Unique NPCIs & 69 & 81 & 87 \\ \hline Valid data points & 2670 & 2669 & 2670 \\ \hline Valid data points (\%) & 100 & 99.9 & 100 \\ \hline Average number of NPCIs per valid data point & 4.9 & 5.2 & 4.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the data used for the three networks detected in the Rome dataset; a valid data point is defined as a data point where at least one unique NPCI was detected for the considered operator. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Strategy**} & \multicolumn{3}{c}{**Operator 1**} & \multicolumn{2}{c}{**Operator 2**} \\ \cline{2-5}  & \(k\) & **Error [m]** & \(k\) & **Error [m]** \\ \hline _Net_ & 8 & 54.0 & 7 & 64.2 \\ \hline _Enhanced Net_ & 2 & 30.4 & 2 & 33.7 \\ \hline _Coverage_ & 2 & 20.1 & 2 & 19.8 \\ \hline _Weighted Coverage_ & 3 & 19.5 & 2 & 19.0 \\ \hline _Single Cell_[7] & 30 & 72.9 & 24 & 81.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Minimum average positioning error and corresponding optimal \(k\) for the proposed strategies and the approach in [7] using RSRQ as the RF parameter in Operator 1 vs. Operator 2 networks in the Oslo dataset. The performance of all strategies using data for each of the three operators detected in Rome was subsequently compared, with a focus on the RSRP parameter, which led to the best performance for Operator 88. The results are presented in Table 8 and show that Operator 88 is the one that leads to the best performance in most cases, in particular when the original dataset is considered. The linear interpolation leads to a marked performance improvement for all strategies and for all operators, with the exception of the _net_ strategy, as already observed for Operator 88. It is also interesting to observe that the interpolation reduces the performance gap between operators, and in particular between Operator 10 and Operator 88, characterized by the same best absolute performance.