# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 4 Dataset Description and Analysis
### Datasets Analysis and Comparison  
The characteristics of the two datasets collected in Oslo and Rome are as follows:  
* The Oslo dataset includes data from 7 driving measurement runs for a total of 118,880 data entries collected in 5266 data points over an area of approximately \(2\times 2\) km\({}^{2}\). Two different network operators were identified. The runs are presented in Figure 1(a);
* The Rome dataset includes data from 6 campaigns for a total of 31,599 data entries collected in 2670 data points over an area of \(8\times 2.5\) km\({}^{2}\). Three different network operators were identified. The runs are presented in Figure 1(b). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{_Latitude_n_} & \multirow{3}{*}{\(Longitude_{n}\)} & NPCI\({}_{1,n}\) & RSSI\({}_{1,n}\) & SINR\({}_{1,n}\) & RSRP\({}_{1,n}\) & RSRQ\({}_{1,n}\) \\ \cline{3-6}  & & NPCI\({}_{2,n}\) & RSSI\({}_{2,n}\) & SINR\({}_{2,n}\) & RSRP\({}_{2,n}\) & RSRQ\({}_{2,n}\) \\ \cline{2-6}  & & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \cline{3-6}  & & NPCI\({}_{L_{w},n}\) & RSSI\({}_{L_{w},n}\) & SINR\({}_{L_{w},n}\) & RSRP\({}_{L_{w},n}\) & RSRQ\({}_{L_{w},n}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Information associated with the generic \(n\)-th data point. The notation NPCI\({}_{k,n}\) indicates the _unique_ NPCI, corresponding to a unique triplet \(<\)NPCI, eNodeB-ID, MNC\(>\) associated with the \(k\)-th data entry out of the \(L_{n}\) collected at the coordinates \(<\)_Latitude\({}_{n}\), Longitude\({}_{n}\)_>. Figure 2: _Cont._  
The measurement runs included in each dataset were selected so to guarantee a reasonable spatial overlapping between them. An early comparison between the number of data entries and data points highlighted that the Oslo dataset is characterized by a larger average number of entries per data point than is the Rome dataset. Most importantly, the data collected in Rome also often showed missing RF parameters for a given NPCI, whereas this phenomenon was seldom observed in the Oslo data. An example of this difference is presented in Figure 3.  
Figure 3: Examples of data points from the Oslo and Rome datasets; missing RF data in the Rome data are highlighted in red. Figure 2: Measurement runs used for the Oslo campaign (**a**) and Rome campaign (**b**); each color represents a different run. As a result, the average number of unique NPCIs with valid RF parameters per data point was slightly larger in the Oslo dataset than in the Rome dataset (11.34 vs. 10.33) despite the presence of a lower number of network operators (2 vs. 3). The number of unique NPCIs with valid RF data further showed a larger variability in the Rome data, as shown in Figure 4, which presents a box plot representation of the data. Following a review of the setups used to collect the two datasets, the difference was attributed to the heavy additional processing load introduced in the Rome setup by the 5G data collection possibly leading to a slower decoding of NB-IoT signals and corresponding data recording. As a result, at times, RF values would be estimated only for some of the NPCIs detected at a given location, leading to a partial loss of data. This hypothesis was confirmed by an in-depth analysis of the data that identified the presence of \"holes\" in the RF data for a given NPCI during a run with an average ratio of RF data loss, defined as the ratio between the number of data points where the NPCI was detected and RF data recorded as well as the total number of data points where the NPCI was detected, in the order of \(P_{L}=0.25\), with a clear impact on the spatial coherence of data. An example of such impact is presented in Figure 5, which compares the RSSI data collected over time for a sample NPCI in the Oslo vs. Rome datasets and shows the impact of the data loss in the latter set.