# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation
### Additional Performance Enhancements
#### 5.4.1 Increasing Information: Combining Data from Multiple Operators  
Although from an operator perspective, it is appealing to rely only on data under the operator's control, scenarios can be envisioned where either a third party collects data for multiple operators, as the authors did in this work, and uses all the available data to provide a positioning service, or operators decide to share data under a mutual agreement and provide a common service to their customers. As a result, the set of unique NPCIs for each data point will be given by the combination of the NPCIs detected for each operator, typically leading to more information. Table 9 shows the characteristics of both datasets when information from all operators is combined and can be directly compared with Tables 4 and 6 for the Oslo and Rome datasets, respectively. The comparison highlights that the combined data are characterized by much larger numbers of unique NPCIs and of valid data points, resulting in a 100% validity of data points.  
Table 10 compares the minimum average positioning error obtained for a single operator vs. combined operators data using the _weighted coverage_ strategy in combination with the RF parameter that led to the best performance in each dataset, which was RSRQ for Oslo and RSRP for Rome. The results show that combining data leads to a marked performance improvement for all datasets. Interestingly, the combined results for the original Rome dataset also show that the combination of data partially compensates for the performance degradation caused by data loss, leading to an error slightly closer to the one observed for single operators when using the interpolated dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{3}{c}{**Minimum Average Positioning Error [m]**} \\ \hline \multirow{2}{*}{Rome (original)} & Op. 1 & Op. 10 & Op. 88 & Combined \\ \cline{2-5}  & 69.3 & 58.1 & 49.0 & 46.2 \\ \hline \multirow{2}{*}{Rome (interpolated)} & Op. 1 & Op. 10 & Op. 88 & Combined \\ \cline{2-5}  & 35.0 & 26.8 & 26.8 & 15.3 \\ \hline \multirow{2}{*}{Oslo} & Op. 1 & Op. 2 & - & Combined \\ \cline{2-5}  & 19.5 & 19.0 & - & 16.1 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Minimum average positioning error in meters using each the single-operator and combined-operators data in the Oslo and Rome datasets for the _weighted coverage_ strategy in combination with RSRQ (Oslo) and RSRP (Rome); results using both the original and interpolated datasets are presented for Rome.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Oslo** & **Rome** \\ \hline Unique NPCIs & 299 & 237 \\ \hline Valid data points & 5266 & 2670 \\ \hline Valid data points (\%) & 100 & 100 \\ \hline Average number of NPCIs per valid data point & 11.3 & 15.0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison between the Oslo and Rome datasets using combined data from all operators detected in each dataset; a valid data point is defined as a data point where at least one unique NPCI was detected.